| n_samples | n_permutations | update_interval | n_consecutive_dev… | result |
|---|---|---|---|---|
| 226 | 32 | 50 | 1 | 0.193798 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_samples | range | 100 | 1000 | int | |
| n_permutations | range | 10 | 50 | int | |
| update_interval | range | 50 | 250 | int | |
| n_consecutive_deviations | range | 1 | 5 | int |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 497 | 13 | 510 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,n_permutations,update_interval,n_consecutive_deviations 0,0_0,COMPLETED,Sobol,0.314328582145536383762873811065,249,31,230,1 1,1_0,COMPLETED,Sobol,0.272818204551137810653926862869,276,27,149,4 2,2_0,COMPLETED,Sobol,0.300325081270317539861025579739,915,30,103,1 3,3_0,COMPLETED,Sobol,0.311577894473618388637703446875,827,46,184,3 4,4_0,COMPLETED,Sobol,0.286571642910727675257476221304,135,30,239,2 5,5_0,COMPLETED,Sobol,0.307576894223555941110248568293,519,37,156,5 6,6_0,COMPLETED,Sobol,0.246561640410102533849112660391,460,32,72,3 7,7_0,COMPLETED,Sobol,0.308577144286071525236536672310,662,44,250,1 8,8_0,COMPLETED,Sobol,0.291572893223305817933521666419,438,11,186,2 9,9_0,COMPLETED,Sobol,0.290322580645161254508934689511,499,36,160,4 10,10_0,COMPLETED,Sobol,0.311077769442360541063408163609,871,50,138,1 11,11_0,COMPLETED,Sobol,0.288572143035758954532354891853,699,37,209,5 12,12_0,COMPLETED,Sobol,0.326081520380094969091544498951,830,29,205,3 13,13_0,COMPLETED,Sobol,0.299824956239059803309032758989,573,31,219,1 14,14_0,COMPLETED,Sobol,0.209552388097024255841915874043,245,31,54,2 15,15_0,COMPLETED,Sobol,0.324831207801950516689259984560,958,14,228,3 16,16_0,COMPLETED,Sobol,0.283570892723180811856309446739,261,27,202,2 17,17_0,COMPLETED,Sobol,0.292823205801450381358108643326,396,44,240,3 18,18_0,COMPLETED,Sobol,0.264816204051012804576714643190,380,33,166,2 19,19_0,COMPLETED,Sobol,0.261065266316579114302953712468,338,13,87,5 20,20_0,COMPLETED,BoTorch,0.208052013003250824141332486761,164,26,50,2 21,21_0,COMPLETED,BoTorch,0.229057264316078978971802371234,209,40,50,2 22,22_0,COMPLETED,BoTorch,0.212553138284571119243082648609,304,26,50,2 23,23_0,COMPLETED,BoTorch,0.225056264066016531444347492652,164,31,50,3 24,24_0,COMPLETED,BoTorch,0.218054513628407109493423376989,282,32,50,1 25,25_0,COMPLETED,BoTorch,0.233058264566141537521559712332,186,32,80,2 26,26_0,COMPLETED,BoTorch,0.235808952238059532646730076522,100,32,50,2 27,27_0,COMPLETED,BoTorch,0.225306326581645399720343903027,208,18,51,2 28,28_0,COMPLETED,BoTorch,0.222305576394098536319177128462,307,39,50,2 29,29_0,COMPLETED,BoTorch,0.240310077519379827748480238370,129,26,83,2 30,30_0,COMPLETED,BoTorch,0.218804701175293825343715070630,161,41,50,3 31,31_0,COMPLETED,BoTorch,0.209052263065766408267620590777,126,18,50,3 32,32_0,COMPLETED,BoTorch,0.210552638159539839968203978060,204,22,50,1 33,33_0,COMPLETED,BoTorch,0.248312078019504833825692458049,100,42,61,2 34,34_0,COMPLETED,BoTorch,0.215803950987746961942548296065,234,40,71,1 35,35_0,COMPLETED,BoTorch,0.253563390847711955800036776054,285,36,72,2 36,36_0,COMPLETED,BoTorch,0.214053513378344550943666035892,141,30,50,1 37,37_0,COMPLETED,BoTorch,0.217554388597149261919128093723,322,17,50,1 38,38_0,COMPLETED,BoTorch,0.251812953238309544801154515881,100,36,82,1 39,39_0,COMPLETED,BoTorch,0.240810202550637675322775521636,229,25,73,2 40,40_0,COMPLETED,BoTorch,0.202050512628156986316696475114,213,27,50,2 41,41_0,COMPLETED,BoTorch,0.239809952488121980174184955104,133,19,78,2 42,42_0,COMPLETED,BoTorch,0.205051262815703960740165712195,143,12,50,3 43,43_0,COMPLETED,BoTorch,0.214553638409602398517961319158,103,11,50,1 44,44_0,COMPLETED,BoTorch,0.226056514128532115570635596669,100,21,50,1 45,45_0,COMPLETED,BoTorch,0.204051012753188265591575145663,202,10,50,1 46,46_0,COMPLETED,BoTorch,0.231307826956739237544979914674,100,14,50,5 47,47_0,COMPLETED,BoTorch,0.222055513878469668043180718087,290,10,50,3 48,48_0,COMPLETED,BoTorch,0.222055513878469668043180718087,100,26,50,5 49,49_0,COMPLETED,BoTorch,0.211052763190797687542499261326,246,20,50,3 50,50_0,COMPLETED,BoTorch,0.209552388097024255841915874043,303,29,50,1 51,51_0,COMPLETED,BoTorch,0.234308577144286100946146689239,100,10,50,4 52,52_0,COMPLETED,BoTorch,0.229557389347336826546097654500,419,50,50,1 53,53_0,COMPLETED,BoTorch,0.200550137534383554616113087832,244,28,50,1 54,54_0,COMPLETED,BoTorch,0.234558639659914969222143099614,100,10,50,2 55,55_0,COMPLETED,BoTorch,0.214553638409602398517961319158,112,24,50,3 56,56_0,COMPLETED,BoTorch,0.243810952738184538723942296201,408,15,50,3 57,57_0,COMPLETED,BoTorch,0.226056514128532115570635596669,271,38,50,5 58,58_0,COMPLETED,BoTorch,0.233058264566141537521559712332,197,10,50,3 59,59_0,COMPLETED,BoTorch,0.214553638409602398517961319158,195,10,50,2 60,60_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,10,50,1 61,61_0,COMPLETED,BoTorch,0.201050262565641402190408371098,245,19,50,1 62,62_0,COMPLETED,BoTorch,0.233058264566141537521559712332,365,50,50,5 63,63_0,COMPLETED,BoTorch,0.197549387346836691214946313266,147,10,50,2 64,64_0,COMPLETED,BoTorch,0.207301825456364108291040793119,234,16,50,2 65,65_0,COMPLETED,BoTorch,0.220305076269067257044298457913,343,10,50,1 66,66_0,COMPLETED,BoTorch,0.234808702175543837498139509989,201,50,50,5 67,67_0,COMPLETED,BoTorch,0.220055013753438388768302047538,504,10,50,1 68,68_0,COMPLETED,BoTorch,0.226306576644161094868934469559,194,22,50,4 69,69_0,COMPLETED,BoTorch,0.204051012753188265591575145663,233,33,50,4 70,70_0,COMPLETED,BoTorch,0.245311327831957970424525683484,230,36,50,5 71,71_0,COMPLETED,BoTorch,0.208302075518879692417328897136,242,21,50,1 72,72_0,COMPLETED,BoTorch,0.213553388347086814391673215141,236,27,50,4 73,73_0,COMPLETED,BoTorch,0.234558639659914969222143099614,278,38,50,5 74,74_0,COMPLETED,BoTorch,0.219304826206551672918010353897,100,50,50,5 75,75_0,COMPLETED,BoTorch,0.267066766691672952127589724114,668,50,50,5 76,76_0,COMPLETED,BoTorch,0.228807201800450110695805960859,220,19,50,1 77,77_0,COMPLETED,BoTorch,0.226556639159789963144930879935,159,45,50,5 78,78_0,COMPLETED,BoTorch,0.219804951237809409470003174647,535,12,50,1 79,79_0,COMPLETED,BoTorch,0.262815703925981525301835972641,689,37,50,3 80,80_0,COMPLETED,BoTorch,0.242560640160039975299355319294,362,44,50,5 81,74_0,COMPLETED,BoTorch,0.218054513628407109493423376989,100,50,50,5 82,82_0,COMPLETED,BoTorch,0.205301325331332829016162122571,257,18,50,1 83,83_0,COMPLETED,BoTorch,0.233058264566141537521559712332,260,20,50,1 84,84_0,COMPLETED,BoTorch,0.243810952738184538723942296201,713,45,50,4 85,85_0,COMPLETED,BoTorch,0.219054763690922693619711481006,241,21,50,2 86,86_0,COMPLETED,BoTorch,0.255813953488372103350911856978,562,50,57,4 87,87_0,COMPLETED,BoTorch,0.247561890472618117975400764408,563,50,57,4 88,88_0,COMPLETED,BoTorch,0.274318579644911242354510250152,766,50,91,4 89,86_0,COMPLETED,BoTorch,0.255813953488372103350911856978,562,50,57,4 90,90_0,COMPLETED,BoTorch,0.243060765191297822873650602560,697,10,50,1 91,91_0,COMPLETED,BoTorch,0.235058764691172816796438382880,493,50,50,3 92,92_0,COMPLETED,BoTorch,0.251312828207051808249161695130,721,44,50,4 93,93_0,COMPLETED,BoTorch,0.206551637909477392440749099478,278,10,50,1 94,94_0,COMPLETED,BoTorch,0.214303575893973530241964908782,336,23,50,1 95,95_0,COMPLETED,BoTorch,0.232058014503625953395271608315,310,20,50,1 96,96_0,COMPLETED,BoTorch,0.224056014003500836295756926120,252,13,50,2 97,97_0,COMPLETED,BoTorch,0.222305576394098536319177128462,319,23,50,1 98,98_0,COMPLETED,BoTorch,0.203050762690672681465287041647,127,50,50,1 99,99_0,COMPLETED,BoTorch,0.220305076269067257044298457913,273,50,50,1 100,100_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,34,50,4 101,101_0,COMPLETED,BoTorch,0.217804451112778241217426966614,265,50,50,3 102,102_0,COMPLETED,BoTorch,0.210802700675168819266502850951,123,50,50,1 103,103_0,COMPLETED,BoTorch,0.219804951237809409470003174647,221,42,50,3 104,104_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,50,50,4 105,105_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,36,50,4 106,106_0,COMPLETED,BoTorch,0.258564641160290098476082221168,100,50,104,5 107,107_0,COMPLETED,BoTorch,0.246811702925731402125109070766,103,42,83,5 108,108_0,COMPLETED,BoTorch,0.226056514128532115570635596669,222,50,50,1 109,109_0,COMPLETED,BoTorch,0.274568642160540110630506660527,100,38,118,5 110,110_0,COMPLETED,BoTorch,0.204551137784446113165870428929,258,10,50,1 111,111_0,COMPLETED,BoTorch,0.263315828957239261853828793392,100,50,88,5 112,112_0,COMPLETED,BoTorch,0.222305576394098536319177128462,203,22,50,3 113,100_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,34,50,4 114,114_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,27,50,4 115,115_0,COMPLETED,BoTorch,0.277319329832458105755677024717,322,10,149,4 116,116_0,COMPLETED,BoTorch,0.228307076769192263121510677593,100,50,50,3 117,117_0,COMPLETED,BoTorch,0.224306076519129815594055799011,100,50,50,1 118,118_0,COMPLETED,BoTorch,0.203800950237559397315578735288,235,30,50,1 119,119_0,COMPLETED,BoTorch,0.310327581895473825213116469968,1000,10,50,5 120,120_0,COMPLETED,BoTorch,0.239059764941235264323893261462,100,50,50,2 121,119_0,COMPLETED,BoTorch,0.310327581895473825213116469968,1000,10,50,5 122,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1 123,123_0,COMPLETED,BoTorch,0.219804951237809409470003174647,201,50,50,1 124,124_0,COMPLETED,BoTorch,0.220805201300325104618593741179,162,50,50,2 125,125_0,COMPLETED,BoTorch,0.211052763190797687542499261326,196,23,50,2 126,126_0,COMPLETED,BoTorch,0.242560640160039975299355319294,714,35,50,5 127,127_0,COMPLETED,BoTorch,0.197049262315578843640651030000,227,28,50,1 128,128_0,COMPLETED,BoTorch,0.223055763940985252169468822103,334,50,50,2 129,129_0,COMPLETED,BoTorch,0.241310327581895522897070804902,299,50,50,3 130,130_0,COMPLETED,BoTorch,0.225056264066016531444347492652,221,14,50,1 131,131_0,COMPLETED,BoTorch,0.224056014003500836295756926120,268,32,50,3 132,132_0,COMPLETED,BoTorch,0.293573393348337097208400336967,100,10,250,5 133,133_0,COMPLETED,BoTorch,0.207051762940735128992741920229,242,10,50,1 134,134_0,COMPLETED,BoTorch,0.305326331582895682537071024854,266,42,225,1 135,135_0,COMPLETED,BoTorch,0.300825206301575387435320863005,265,42,225,1 136,136_0,COMPLETED,BoTorch,0.278319579894973689881965128734,100,10,185,5 137,137_0,COMPLETED,BoTorch,0.240060015003750959472483827994,281,28,50,2 138,138_0,COMPLETED,BoTorch,0.294823705926481660632987313875,226,11,241,5 139,139_0,COMPLETED,BoTorch,0.221305326331582841170586561930,201,21,50,1 140,140_0,COMPLETED,BoTorch,0.268317079269817404529874238506,101,31,229,5 141,141_0,COMPLETED,BoTorch,0.294073518379594944782695620233,100,20,194,5 142,142_0,COMPLETED,BoTorch,0.352088022005501377620362291054,163,12,249,5 143,143_0,COMPLETED,BoTorch,0.282820705176294096006017753098,222,19,250,4 144,144_0,COMPLETED,BoTorch,0.195298824706176543664071232342,227,30,50,1 145,145_0,COMPLETED,BoTorch,0.221555388847211820468885434821,251,10,50,1 146,146_0,COMPLETED,BoTorch,0.208052013003250824141332486761,239,10,50,1 147,147_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,29,50,1 148,148_0,COMPLETED,BoTorch,0.277319329832458105755677024717,679,31,65,5 149,149_0,COMPLETED,BoTorch,0.242810702675668954597654192185,338,27,96,2 150,150_0,COMPLETED,BoTorch,0.200800200050012533914411960723,233,10,50,1 151,151_0,COMPLETED,BoTorch,0.199299824956239102213828573440,243,10,50,1 152,146_0,COMPLETED,BoTorch,0.207051762940735128992741920229,239,10,50,1 153,74_0,COMPLETED,BoTorch,0.218054513628407109493423376989,100,50,50,5 154,154_0,COMPLETED,BoTorch,0.193798449612403111963487845060,226,28,50,1 155,155_0,COMPLETED,BoTorch,0.238809702425606396047896851087,148,10,61,3 156,156_0,COMPLETED,BoTorch,0.223305826456614120445465232478,257,12,71,1 157,157_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,30,50,1 158,158_0,COMPLETED,BoTorch,0.236309077269317380221025359788,190,19,50,1 159,159_0,COMPLETED,BoTorch,0.226806701675418831420927290310,148,10,50,3 160,160_0,COMPLETED,BoTorch,0.229557389347336826546097654500,185,38,66,3 161,161_0,COMPLETED,BoTorch,0.200800200050012533914411960723,232,31,50,1 162,162_0,COMPLETED,BoTorch,0.261065266316579114302953712468,100,10,92,3 163,163_0,COMPLETED,BoTorch,0.206051512878219544866453816212,236,19,54,2 164,164_0,COMPLETED,BoTorch,0.240810202550637675322775521636,104,28,86,3 165,165_0,COMPLETED,BoTorch,0.239309827456864243622192134353,107,29,85,3 166,166_0,COMPLETED,BoTorch,0.247561890472618117975400764408,200,14,97,1 167,167_0,COMPLETED,BoTorch,0.234308577144286100946146689239,214,50,96,3 168,160_0,COMPLETED,BoTorch,0.229557389347336826546097654500,185,38,66,3 169,169_0,COMPLETED,BoTorch,0.216804201050262546068836400082,186,43,51,3 170,160_0,COMPLETED,BoTorch,0.229557389347336826546097654500,185,38,66,3 171,171_0,COMPLETED,BoTorch,0.251312828207051808249161695130,599,50,50,1 172,172_0,COMPLETED,BoTorch,0.281820455113778400857427186565,1000,50,50,1 173,173_0,COMPLETED,BoTorch,0.239059764941235264323893261462,298,38,65,3 174,174_0,COMPLETED,BoTorch,0.228307076769192263121510677593,245,23,77,2 175,175_0,COMPLETED,BoTorch,0.202300575143785965614995348005,238,32,50,1 176,176_0,COMPLETED,BoTorch,0.224556139034758683870052209386,270,10,50,1 177,177_0,COMPLETED,BoTorch,0.236809202300575116773018180538,489,38,64,1 178,178_0,COMPLETED,BoTorch,0.218804701175293825343715070630,356,10,50,2 179,179_0,COMPLETED,BoTorch,0.215053763440860246092256602424,240,31,50,1 180,180_0,COMPLETED,BoTorch,0.209302325581395387565919463668,466,18,50,2 181,181_0,COMPLETED,BoTorch,0.274568642160540110630506660527,690,47,59,1 182,182_0,COMPLETED,BoTorch,0.202050512628156986316696475114,238,31,50,1 183,183_0,COMPLETED,BoTorch,0.205301325331332829016162122571,108,28,51,1 184,184_0,COMPLETED,BoTorch,0.265566391597899520427006336831,700,42,64,1 185,185_0,COMPLETED,BoTorch,0.223305826456614120445465232478,222,10,50,2 186,186_0,COMPLETED,BoTorch,0.256064016004000971626908267353,872,23,50,1 187,187_0,COMPLETED,BoTorch,0.286321580395098806981479810929,677,42,58,5 188,188_0,COMPLETED,BoTorch,0.236809202300575116773018180538,289,10,50,3 189,189_0,COMPLETED,BoTorch,0.223805951487871968019760515745,358,16,65,2 190,190_0,COMPLETED,BoTorch,0.206801700425106260716745509853,239,31,50,1 191,191_0,COMPLETED,BoTorch,0.207551887971992976567037203495,223,32,50,1 192,192_0,COMPLETED,BoTorch,0.235058764691172816796438382880,280,10,50,1 193,193_0,COMPLETED,BoTorch,0.209302325581395387565919463668,219,32,50,1 194,194_0,COMPLETED,BoTorch,0.255563890972743235074915446603,791,25,55,1 195,191_0,COMPLETED,BoTorch,0.207551887971992976567037203495,223,32,50,1 196,196_0,COMPLETED,BoTorch,0.230307576894223542396389348141,263,10,50,1 197,197_0,COMPLETED,BoTorch,0.223305826456614120445465232478,221,32,50,1 198,198_0,COMPLETED,BoTorch,0.207551887971992976567037203495,223,33,50,1 199,199_0,COMPLETED,BoTorch,0.227556889222305547271218983951,222,33,50,1 200,193_0,COMPLETED,BoTorch,0.209302325581395387565919463668,219,32,50,1 201,201_0,COMPLETED,BoTorch,0.210802700675168819266502850951,268,10,50,1 202,202_0,COMPLETED,BoTorch,0.193798449612403111963487845060,226,32,50,1 203,203_0,COMPLETED,BoTorch,0.244811202800700122850230400218,320,10,74,1 204,204_0,COMPLETED,BoTorch,0.235558889722430553348431203631,353,10,86,2 205,205_0,COMPLETED,BoTorch,0.259064766191547835028075041919,336,25,134,2 206,206_0,COMPLETED,BoTorch,0.203550887721930529039582324913,217,32,50,1 207,207_0,COMPLETED,BoTorch,0.269067266816704231402468394663,232,39,135,3 208,208_0,COMPLETED,BoTorch,0.210802700675168819266502850951,216,25,50,1 209,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1 210,210_0,COMPLETED,BoTorch,0.208302075518879692417328897136,213,32,50,1 211,211_0,COMPLETED,BoTorch,0.242060515128782238747362498543,258,35,108,2 212,212_0,COMPLETED,BoTorch,0.221305326331582841170586561930,100,10,50,1 213,213_0,COMPLETED,BoTorch,0.231307826956739237544979914674,100,19,50,5 214,214_0,COMPLETED,BoTorch,0.238809702425606396047896851087,157,46,50,4 215,215_0,COMPLETED,BoTorch,0.283820955238809680132305857114,844,31,105,2 216,216_0,COMPLETED,BoTorch,0.211052763190797687542499261326,216,32,50,1 217,217_0,COMPLETED,BoTorch,0.205301325331332829016162122571,218,32,50,1 218,218_0,COMPLETED,BoTorch,0.215053763440860246092256602424,240,26,50,1 219,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1 220,217_0,COMPLETED,BoTorch,0.205301325331332829016162122571,218,32,50,1 221,221_0,COMPLETED,BoTorch,0.233308327081770405797556122707,100,10,50,5 222,222_0,COMPLETED,BoTorch,0.202800700175043813189290631271,237,28,50,1 223,223_0,COMPLETED,BoTorch,0.264566141535383825278415770299,178,33,106,4 224,216_0,COMPLETED,BoTorch,0.211052763190797687542499261326,216,32,50,1 225,225_0,COMPLETED,BoTorch,0.216554138534633677792839989706,215,32,50,1 226,225_0,COMPLETED,BoTorch,0.216554138534633677792839989706,215,32,50,1 227,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1 228,228_0,COMPLETED,BoTorch,0.246561640410102533849112660391,695,45,70,2 229,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1 230,230_0,COMPLETED,BoTorch,0.219804951237809409470003174647,212,33,50,1 231,212_0,COMPLETED,BoTorch,0.221305326331582841170586561930,100,10,50,1 232,232_0,COMPLETED,BoTorch,0.247311827956989249699404354033,246,50,114,1 233,122_0,COMPLETED,BoTorch,0.208302075518879692417328897136,214,32,50,1 234,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1 235,235_0,COMPLETED,BoTorch,0.235808952238059532646730076522,100,22,50,2 236,236_0,COMPLETED,BoTorch,0.207801950487621955865336076386,239,27,50,1 237,237_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,33,50,1 238,206_0,COMPLETED,BoTorch,0.203550887721930529039582324913,217,32,50,1 239,239_0,COMPLETED,BoTorch,0.210802700675168819266502850951,267,25,50,2 240,240_0,COMPLETED,BoTorch,0.238559639909977527771900440712,100,16,50,2 241,241_0,COMPLETED,BoTorch,0.225306326581645399720343903027,180,10,50,1 242,242_0,COMPLETED,BoTorch,0.244811202800700122850230400218,100,31,104,2 243,243_0,RUNNING,BoTorch,,210,27,50,1 244,244_0,COMPLETED,BoTorch,0.262565641410352546003537099750,503,41,126,1 245,245_0,COMPLETED,BoTorch,0.240060015003750959472483827994,352,10,57,3 246,246_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,28,50,2 247,247_0,RUNNING,BoTorch,,223,28,50,1 248,248_0,RUNNING,BoTorch,,233,27,50,1 249,249_0,COMPLETED,BoTorch,0.219554888722180541194006764272,191,24,50,3 250,225_0,COMPLETED,BoTorch,0.216554138534633677792839989706,215,32,50,1 251,251_0,COMPLETED,BoTorch,0.254313578394598671650328469696,713,50,63,5 252,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1 253,253_0,COMPLETED,BoTorch,0.239809952488121980174184955104,321,25,50,5 254,216_0,COMPLETED,BoTorch,0.211052763190797687542499261326,216,32,50,1 255,255_0,COMPLETED,BoTorch,0.209052263065766408267620590777,219,33,50,1 256,256_0,COMPLETED,BoTorch,0.230307576894223542396389348141,220,33,50,1 257,257_0,COMPLETED,BoTorch,0.229807451862965694822094064875,263,29,50,1 258,256_0,COMPLETED,BoTorch,0.230307576894223542396389348141,220,33,50,1 259,259_0,COMPLETED,BoTorch,0.270817704426106531379048192321,768,17,105,1 260,256_0,COMPLETED,BoTorch,0.230307576894223542396389348141,220,33,50,1 261,261_0,COMPLETED,BoTorch,0.237559389847461832623309874180,100,47,73,4 262,256_0,COMPLETED,BoTorch,0.228807201800450110695805960859,220,33,50,1 263,256_0,COMPLETED,BoTorch,0.232558139534883689947264429065,220,33,50,1 264,264_0,COMPLETED,BoTorch,0.225306326581645399720343903027,221,33,50,1 265,265_0,COMPLETED,BoTorch,0.205051262815703960740165712195,218,33,50,1 266,266_0,COMPLETED,BoTorch,0.237059264816204096071317053429,279,50,57,2 267,267_0,COMPLETED,BoTorch,0.260815203800950246026957302092,164,35,94,3 268,268_0,COMPLETED,BoTorch,0.195798949737434391238366515609,227,29,50,1 269,269_0,COMPLETED,BoTorch,0.217554388597149261919128093723,275,10,50,2 270,270_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,30,50,1 271,147_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,29,50,1 272,272_0,COMPLETED,BoTorch,0.237559389847461832623309874180,281,43,50,1 273,273_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,29,50,1 274,274_0,COMPLETED,BoTorch,0.249562390597649397250279434957,695,35,55,1 275,275_0,COMPLETED,BoTorch,0.216054013503375830218544706440,267,25,50,1 276,276_0,COMPLETED,BoTorch,0.208052013003250824141332486761,229,29,50,1 277,277_0,COMPLETED,BoTorch,0.213303325831457835093374342250,176,50,50,2 278,278_0,COMPLETED,BoTorch,0.225556389097274267996340313402,453,50,58,2 279,147_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,29,50,1 280,268_0,COMPLETED,BoTorch,0.195798949737434391238366515609,227,29,50,1 281,281_0,COMPLETED,BoTorch,0.204051012753188265591575145663,258,25,50,1 282,266_0,COMPLETED,BoTorch,0.237059264816204096071317053429,279,50,57,2 283,283_0,COMPLETED,BoTorch,0.208552138034508671715627770027,224,28,50,1 284,284_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,28,50,1 285,283_0,COMPLETED,BoTorch,0.208552138034508671715627770027,224,28,50,1 286,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1 287,284_0,COMPLETED,BoTorch,0.203300825206301549741283452022,225,28,50,1 288,284_0,COMPLETED,BoTorch,0.203300825206301549741283452022,225,28,50,1 289,289_0,COMPLETED,BoTorch,0.246061515378844686274817377125,315,27,121,3 290,290_0,COMPLETED,BoTorch,0.208552138034508671715627770027,224,27,50,1 291,291_0,COMPLETED,BoTorch,0.222055513878469668043180718087,255,23,60,3 292,127_0,COMPLETED,BoTorch,0.195548887221805411940067642718,227,28,50,1 293,284_0,COMPLETED,BoTorch,0.203300825206301549741283452022,225,28,50,1 294,294_0,COMPLETED,BoTorch,0.283320830207551832558010573848,821,17,75,2 295,295_0,COMPLETED,BoTorch,0.224806201550387552146048619761,230,28,50,1 296,290_0,COMPLETED,BoTorch,0.208552138034508671715627770027,224,27,50,1 297,297_0,COMPLETED,BoTorch,0.209552388097024255841915874043,229,27,50,1 298,298_0,COMPLETED,BoTorch,0.261315328832208093601252585358,319,13,120,2 299,299_0,COMPLETED,BoTorch,0.199049762440610122915529700549,228,27,50,1 300,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1 301,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1 302,302_0,RUNNING,BoTorch,,126,44,61,4 303,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 304,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 305,305_0,COMPLETED,BoTorch,0.221055263815953972894590151554,318,15,87,1 306,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1 307,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 308,299_0,COMPLETED,BoTorch,0.199049762440610122915529700549,228,27,50,1 309,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 310,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1 311,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1 312,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 313,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1 314,314_0,COMPLETED,BoTorch,0.220055013753438388768302047538,146,31,74,2 315,315_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,27,50,1 316,316_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,27,50,1 317,317_0,COMPLETED,BoTorch,0.216054013503375830218544706440,231,27,50,1 318,318_0,COMPLETED,BoTorch,0.214053513378344550943666035892,237,21,50,3 319,319_0,COMPLETED,BoTorch,0.206801700425106260716745509853,248,25,50,1 320,320_0,COMPLETED,BoTorch,0.288572143035758954532354891853,903,40,66,4 321,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1 322,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1 323,315_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,27,50,1 324,315_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,27,50,1 325,325_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,27,50,1 326,326_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,27,50,1 327,327_0,COMPLETED,BoTorch,0.235558889722430553348431203631,188,43,94,2 328,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1 329,329_0,COMPLETED,BoTorch,0.269067266816704231402468394663,452,17,108,2 330,330_0,COMPLETED,BoTorch,0.200300075018754686340116677457,244,26,50,1 331,331_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,27,50,1 332,332_0,COMPLETED,BoTorch,0.216054013503375830218544706440,151,50,57,3 333,316_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,27,50,1 334,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1 335,335_0,COMPLETED,BoTorch,0.246311577894473665573116250016,130,46,79,5 336,336_0,COMPLETED,BoTorch,0.258314578644661119177783348277,344,50,96,4 337,316_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,27,50,1 338,338_0,COMPLETED,BoTorch,0.202050512628156986316696475114,238,26,50,1 339,339_0,COMPLETED,BoTorch,0.282320580145036248431722469832,280,21,186,5 340,340_0,COMPLETED,BoTorch,0.232558139534883689947264429065,199,28,65,2 341,338_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,26,50,1 342,338_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,26,50,1 343,338_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,26,50,1 344,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1 345,345_0,COMPLETED,BoTorch,0.202550637659414833890991758381,237,26,50,1 346,346_0,COMPLETED,BoTorch,0.243810952738184538723942296201,421,37,88,3 347,338_0,COMPLETED,BoTorch,0.202050512628156986316696475114,238,26,50,1 348,348_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,25,50,1 349,345_0,COMPLETED,BoTorch,0.202800700175043813189290631271,237,26,50,1 350,338_0,COMPLETED,BoTorch,0.202050512628156986316696475114,238,26,50,1 351,351_0,COMPLETED,BoTorch,0.267566891722930688679582544864,538,12,120,2 352,345_0,COMPLETED,BoTorch,0.208302075518879692417328897136,237,26,50,1 353,353_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,26,50,1 354,354_0,COMPLETED,BoTorch,0.231557889472368105820976325049,476,45,65,3 355,355_0,COMPLETED,BoTorch,0.239059764941235264323893261462,100,40,50,2 356,356_0,COMPLETED,BoTorch,0.209552388097024255841915874043,239,26,50,1 357,357_0,COMPLETED,BoTorch,0.249812453113278265526275845332,219,27,84,2 358,358_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,26,50,1 359,338_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,26,50,1 360,358_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,26,50,1 361,361_0,COMPLETED,BoTorch,0.224806201550387552146048619761,141,23,59,1 362,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1 363,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1 364,364_0,COMPLETED,BoTorch,0.255313828457114255776616573712,127,32,126,4 365,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1 366,366_0,COMPLETED,BoTorch,0.284821205301325375280896423646,144,14,132,4 367,367_0,COMPLETED,BoTorch,0.210802700675168819266502850951,216,34,58,1 368,326_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,27,50,1 369,369_0,COMPLETED,BoTorch,0.246811702925731402125109070766,385,41,114,4 370,370_0,COMPLETED,BoTorch,0.202800700175043813189290631271,237,27,50,1 371,371_0,COMPLETED,BoTorch,0.219054763690922693619711481006,183,50,50,1 372,117_0,COMPLETED,BoTorch,0.224306076519129815594055799011,100,50,50,1 373,373_0,COMPLETED,BoTorch,0.224306076519129815594055799011,101,27,50,4 374,370_0,COMPLETED,BoTorch,0.202800700175043813189290631271,237,27,50,1 375,375_0,COMPLETED,BoTorch,0.221805451362840688744881845196,100,32,50,3 376,236_0,COMPLETED,BoTorch,0.207801950487621955865336076386,239,27,50,1 377,326_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,27,50,1 378,378_0,COMPLETED,BoTorch,0.227056764191047810719226163201,100,39,50,3 379,379_0,COMPLETED,BoTorch,0.211802950737684403392790954967,253,25,50,1 380,380_0,COMPLETED,BoTorch,0.246311577894473665573116250016,306,13,100,1 381,381_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,27,50,1 382,381_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,27,50,1 383,383_0,COMPLETED,BoTorch,0.225556389097274267996340313402,249,26,50,1 384,384_0,COMPLETED,BoTorch,0.249812453113278265526275845332,500,39,56,2 385,325_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,27,50,1 386,386_0,COMPLETED,BoTorch,0.275068767191797958204801943793,536,47,92,4 387,325_0,COMPLETED,BoTorch,0.203800950237559397315578735288,236,27,50,1 388,326_0,COMPLETED,BoTorch,0.201800450112528118040700064739,238,27,50,1 389,212_0,COMPLETED,BoTorch,0.221305326331582841170586561930,100,10,50,1 390,381_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,27,50,1 391,325_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,27,50,1 392,392_0,COMPLETED,BoTorch,0.227806951737934526569517856842,404,31,64,1 393,325_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,27,50,1 394,394_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,28,50,1 395,395_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,28,50,1 396,396_0,COMPLETED,BoTorch,0.199299824956239102213828573440,243,28,50,1 397,394_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,28,50,1 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411,411_0,COMPLETED,BoTorch,0.222805701425356383893472411728,506,10,50,1 412,412_0,COMPLETED,BoTorch,0.226806701675418831420927290310,441,10,50,1 413,413_0,COMPLETED,BoTorch,0.225056264066016531444347492652,414,38,52,1 414,413_0,COMPLETED,BoTorch,0.225056264066016531444347492652,414,38,52,1 415,415_0,COMPLETED,BoTorch,0.214303575893973530241964908782,446,16,50,1 416,416_0,COMPLETED,BoTorch,0.232558139534883689947264429065,525,10,50,1 417,417_0,COMPLETED,BoTorch,0.266316579144786236277298030473,283,33,91,5 418,412_0,COMPLETED,BoTorch,0.226806701675418831420927290310,441,10,50,1 419,419_0,COMPLETED,BoTorch,0.237809452363090811921608747070,499,10,64,1 420,420_0,COMPLETED,BoTorch,0.223305826456614120445465232478,484,26,50,1 421,421_0,COMPLETED,BoTorch,0.230307576894223542396389348141,442,10,60,1 422,422_0,COMPLETED,BoTorch,0.229057264316078978971802371234,419,16,50,1 423,297_0,COMPLETED,BoTorch,0.208052013003250824141332486761,229,27,50,1 424,303_0,COMPLETED,BoTorch,0.226806701675418831420927290310,230,27,50,1 425,317_0,COMPLETED,BoTorch,0.215053763440860246092256602424,231,27,50,1 426,426_0,COMPLETED,BoTorch,0.239559889972493111898188544728,128,44,61,4 427,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1 428,303_0,COMPLETED,BoTorch,0.224056014003500836295756926120,230,27,50,1 429,429_0,COMPLETED,BoTorch,0.205551387846961697292158532946,107,42,51,3 430,299_0,COMPLETED,BoTorch,0.199049762440610122915529700549,228,27,50,1 431,297_0,COMPLETED,BoTorch,0.209552388097024255841915874043,229,27,50,1 432,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1 433,433_0,COMPLETED,BoTorch,0.229557389347336826546097654500,468,49,62,2 434,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1 435,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1 436,297_0,COMPLETED,BoTorch,0.208052013003250824141332486761,229,27,50,1 437,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1 438,438_0,COMPLETED,BoTorch,0.197049262315578843640651030000,227,27,50,1 439,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1 440,440_0,COMPLETED,BoTorch,0.265816454113528388703002747206,100,36,149,2 441,441_0,COMPLETED,BoTorch,0.291572893223305817933521666419,1000,46,60,4 442,442_0,COMPLETED,BoTorch,0.262565641410352546003537099750,329,31,109,2 443,443_0,COMPLETED,BoTorch,0.222555638909727404595173538837,101,50,50,1 444,444_0,COMPLETED,BoTorch,0.201800450112528118040700064739,234,49,52,1 445,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1 446,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1 447,447_0,COMPLETED,BoTorch,0.218304576144035977769419787364,235,50,69,2 448,448_0,COMPLETED,BoTorch,0.214303575893973530241964908782,236,41,73,2 449,438_0,COMPLETED,BoTorch,0.195548887221805411940067642718,227,27,50,1 450,438_0,COMPLETED,BoTorch,0.195548887221805411940067642718,227,27,50,1 451,451_0,COMPLETED,BoTorch,0.214803700925231266793957729533,215,25,50,1 452,452_0,COMPLETED,BoTorch,0.219804951237809409470003174647,100,26,50,1 453,208_0,COMPLETED,BoTorch,0.208052013003250824141332486761,216,25,50,1 454,454_0,COMPLETED,BoTorch,0.243060765191297822873650602560,628,25,63,2 455,208_0,COMPLETED,BoTorch,0.207801950487621955865336076386,216,25,50,1 456,456_0,COMPLETED,BoTorch,0.244561140285071254574233989842,100,50,100,3 457,208_0,COMPLETED,BoTorch,0.207801950487621955865336076386,216,25,50,1 458,451_0,COMPLETED,BoTorch,0.214303575893973530241964908782,215,25,50,1 459,459_0,COMPLETED,BoTorch,0.229307326831707958270101244125,189,17,50,1 460,460_0,COMPLETED,BoTorch,0.217304326081520393643131683348,260,10,50,2 461,461_0,COMPLETED,BoTorch,0.235808952238059532646730076522,105,10,90,2 462,462_0,COMPLETED,BoTorch,0.251312828207051808249161695130,100,13,69,5 463,208_0,COMPLETED,BoTorch,0.207801950487621955865336076386,216,25,50,1 464,451_0,COMPLETED,BoTorch,0.214303575893973530241964908782,215,25,50,1 465,465_0,COMPLETED,BoTorch,0.257314328582145535051495244261,100,46,99,3 466,466_0,COMPLETED,BoTorch,0.226806701675418831420927290310,115,36,82,2 467,467_0,COMPLETED,BoTorch,0.249312328082020528974283024581,261,30,107,3 468,468_0,COMPLETED,BoTorch,0.238809702425606396047896851087,100,50,79,1 469,469_0,COMPLETED,BoTorch,0.231057764441110258246681041783,105,35,80,2 470,470_0,COMPLETED,BoTorch,0.262815703925981525301835972641,270,27,106,3 471,471_0,COMPLETED,BoTorch,0.261315328832208093601252585358,267,28,105,3 472,472_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,28,50,1 473,472_0,COMPLETED,BoTorch,0.203800950237559397315578735288,236,28,50,1 474,474_0,COMPLETED,BoTorch,0.252563140785196260651446209522,118,45,102,3 475,475_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,28,50,1 476,476_0,COMPLETED,BoTorch,0.262065516379094809451544279000,148,46,98,3 477,477_0,COMPLETED,BoTorch,0.201050262565641402190408371098,293,48,50,2 478,472_0,COMPLETED,BoTorch,0.203800950237559397315578735288,236,28,50,1 479,472_0,COMPLETED,BoTorch,0.203800950237559397315578735288,236,28,50,1 480,480_0,COMPLETED,BoTorch,0.242560640160039975299355319294,157,46,60,4 481,303_0,COMPLETED,BoTorch,0.224056014003500836295756926120,230,27,50,1 482,482_0,COMPLETED,BoTorch,0.216054013503375830218544706440,174,27,51,1 483,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 484,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 485,485_0,COMPLETED,BoTorch,0.229057264316078978971802371234,108,31,58,2 486,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 487,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1 488,317_0,COMPLETED,BoTorch,0.211552888222055535116794544592,231,27,50,1 489,303_0,COMPLETED,BoTorch,0.224056014003500836295756926120,230,27,50,1 490,490_0,COMPLETED,BoTorch,0.236059014753688400922726486897,260,26,50,3 491,491_0,COMPLETED,BoTorch,0.245061265316329102148529273109,100,10,68,5 492,297_0,COMPLETED,BoTorch,0.208052013003250824141332486761,229,27,50,1 493,303_0,COMPLETED,BoTorch,0.224056014003500836295756926120,230,27,50,1 494,303_0,COMPLETED,BoTorch,0.226806701675418831420927290310,230,27,50,1 495,495_0,COMPLETED,BoTorch,0.249062265566391549675984151690,193,29,114,3 496,381_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,27,50,1 497,495_0,COMPLETED,BoTorch,0.247061765441360381423407943657,193,29,114,3 498,498_0,COMPLETED,BoTorch,0.250562640660165092398870001489,100,50,71,1 499,316_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,27,50,1 500,500_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,26,50,1 501,501_0,RUNNING,BoTorch,,169,27,87,2 502,502_0,RUNNING,BoTorch,,185,31,125,3 503,248_0,RUNNING,BoTorch,,233,27,50,1 504,504_0,RUNNING,BoTorch,,166,28,69,1 505,248_0,RUNNING,BoTorch,,233,27,50,1 506,506_0,RUNNING,BoTorch,,189,28,108,3 507,507_0,RUNNING,BoTorch,,158,27,50,3 508,501_0,RUNNING,BoTorch,,169,27,87,2 509,509_0,RUNNING,BoTorch,,162,26,73,2
start_time,end_time,run_time,program_string,n_samples,n_permutations,update_interval,n_consecutive_deviations,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727501118,1727501151,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 276 n_permutations 27 update_interval 149 n_consecutive_deviations 4,276,27,149,4,0.2728182045511378,0,None,i7186,29,0.018454613653413353 1727501118,1727501152,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 135 n_permutations 30 update_interval 239 n_consecutive_deviations 2,135,30,239,2,0.2865716429107277,0,None,i7186,30,0.01870204393203564 1727501118,1727501154,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 519 n_permutations 37 update_interval 156 n_consecutive_deviations 5,519,37,156,5,0.30757689422355594,0,None,i7186,32,0.023880970242560636 1727501118,1727501155,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 249 n_permutations 31 update_interval 230 n_consecutive_deviations 1,249,31,230,1,0.3143285821455364,0,None,i7186,33,0.021838793031591232 1727501118,1727501155,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 915 n_permutations 30 update_interval 103 n_consecutive_deviations 1,915,30,103,1,0.30032508127031754,0,None,i7186,33,0.017079269817454366 1727501118,1727501156,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 827 n_permutations 46 update_interval 184 n_consecutive_deviations 3,827,46,184,3,0.3115778944736184,0,None,i7186,34,0.023595184510413317 1727501139,1727501170,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 460 n_permutations 32 update_interval 72 n_consecutive_deviations 3,460,32,72,3,0.24656164041010253,0,None,i7186,27,0.01317829457364341 1727501138,1727501174,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 662 n_permutations 44 update_interval 250 n_consecutive_deviations 1,662,44,250,1,0.3085771442860715,0,None,i7186,32,0.023809523809523808 1727501156,1727501217,61,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 245 n_permutations 31 update_interval 54 n_consecutive_deviations 2,245,31,54,2,0.20955238809702426,0,None,i7177,24,0.008823634480048583 1727501166,1727501220,54,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 338 n_permutations 13 update_interval 87 n_consecutive_deviations 5,338,13,87,5,0.2610652663165791,0,None,i7173,27,0.019042260565141286 1727501166,1727501220,54,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 380 n_permutations 33 update_interval 166 n_consecutive_deviations 2,380,33,166,2,0.2648162040510128,0,None,i7173,27,0.02094968186491067 1727501156,1727501221,65,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 261 n_permutations 27 update_interval 202 n_consecutive_deviations 2,261,27,202,2,0.2835708927231808,0,None,i7177,28,0.02107879911154259 1727501156,1727501222,66,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 499 n_permutations 36 update_interval 160 n_consecutive_deviations 4,499,36,160,4,0.29032258064516125,0,None,i7177,29,0.021974243560890224 1727501166,1727501223,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 396 n_permutations 44 update_interval 240 n_consecutive_deviations 3,396,44,240,3,0.2928232058014504,0,None,i7173,30,0.026852867062919575 1727501156,1727501223,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 438 n_permutations 11 update_interval 186 n_consecutive_deviations 2,438,11,186,2,0.2915728932233058,0,None,i7177,30,0.02189609902475619 1727501156,1727501223,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 573 n_permutations 31 update_interval 219 n_consecutive_deviations 1,573,31,219,1,0.2998249562390598,0,None,i7177,30,0.019004751187796945 1727501156,1727501223,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 699 n_permutations 37 update_interval 209 n_consecutive_deviations 5,699,37,209,5,0.28857214303575895,0,None,i7177,30,0.027179871891049683 1727501156,1727501223,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 871 n_permutations 50 update_interval 138 n_consecutive_deviations 1,871,50,138,1,0.31107776944236054,0,None,i7177,30,0.019460747539826136 1727501156,1727501224,68,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 830 n_permutations 29 update_interval 205 n_consecutive_deviations 3,830,29,205,3,0.32608152038009497,0,None,i7177,31,0.022559211231379276 1727501156,1727501226,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 958 n_permutations 14 update_interval 228 n_consecutive_deviations 3,958,14,228,3,0.3248312078019505,0,None,i7177,33,0.02882538816522312 1727501358,1727501388,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 209 n_permutations 40 update_interval 50 n_consecutive_deviations 2,209,40,50,2,0.22905726431607898,0,None,i7186,25,0.007789683269874073 1727501358,1727501388,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 164 n_permutations 26 update_interval 50 n_consecutive_deviations 2,164,26,50,2,0.20805201300325082,0,None,i7186,26,0.007230974410269234 1727501359,1727501389,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 282 n_permutations 32 update_interval 50 n_consecutive_deviations 1,282,32,50,1,0.2180545136284071,0,None,i7186,26,0.006520860984476888 1727501358,1727501389,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 164 n_permutations 31 update_interval 50 n_consecutive_deviations 3,164,31,50,3,0.22505626406601653,0,None,i7186,27,0.009062048120725833 1727501358,1727501389,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 304 n_permutations 26 update_interval 50 n_consecutive_deviations 2,304,26,50,2,0.21255313828457112,0,None,i7186,27,0.008101081874242145 1727501379,1727501407,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 32 update_interval 50 n_consecutive_deviations 2,100,32,50,2,0.23580895223805953,0,None,i7186,25,0.006657402055431891 1727501378,1727501408,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 208 n_permutations 18 update_interval 51 n_consecutive_deviations 2,208,18,51,2,0.2253063265816454,0,None,i7186,26,0.008168708843877636 1727501378,1727501409,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 186 n_permutations 32 update_interval 80 n_consecutive_deviations 2,186,32,80,2,0.23305826456614154,0,None,i7186,27,0.01168149180152181 1727501396,1727501424,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 204 n_permutations 22 update_interval 50 n_consecutive_deviations 1,204,22,50,1,0.21055263815953984,0,None,i7174,20,0.007071439991145328 1727501396,1727501424,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 126 n_permutations 18 update_interval 50 n_consecutive_deviations 3,126,18,50,3,0.2090522630657664,0,None,i7174,20,0.008167136123653555 1727501396,1727501424,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 161 n_permutations 41 update_interval 50 n_consecutive_deviations 3,161,41,50,3,0.21880470117529383,0,None,i7174,20,0.008136649547002136 1727501396,1727501425,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 234 n_permutations 40 update_interval 71 n_consecutive_deviations 1,234,40,71,1,0.21580395098774696,0,None,i7174,21,0.009469033925147953 1727501396,1727501425,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 129 n_permutations 26 update_interval 83 n_consecutive_deviations 2,129,26,83,2,0.24031007751937983,0,None,i7174,21,0.010568431581579605 1727501396,1727501425,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 42 update_interval 61 n_consecutive_deviations 2,100,42,61,2,0.24831207801950483,0,None,i7174,21,0.008374434034040425 1727501396,1727501425,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 307 n_permutations 39 update_interval 50 n_consecutive_deviations 2,307,39,50,2,0.22230557639409854,0,None,i7174,22,0.007917073608024646 1727501398,1727501426,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 141 n_permutations 30 update_interval 50 n_consecutive_deviations 1,141,30,50,1,0.21405351337834455,0,None,i7186,24,0.0062008255687110185 1727501398,1727501430,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 285 n_permutations 36 update_interval 72 n_consecutive_deviations 2,285,36,72,2,0.25356339084771196,0,None,i7186,28,0.011421973140343909 1727501418,1727501447,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 322 n_permutations 17 update_interval 50 n_consecutive_deviations 1,322,17,50,1,0.21755438859714926,0,None,i7186,25,0.007072601483704259 1727501418,1727501448,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 25 update_interval 73 n_consecutive_deviations 2,229,25,73,2,0.24081020255063768,0,None,i7186,26,0.00955000654925636 1727501418,1727501448,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 36 update_interval 82 n_consecutive_deviations 1,100,36,82,1,0.25181295323830954,0,None,i7186,27,0.009514573765392569 1727501498,1727501529,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 213 n_permutations 27 update_interval 50 n_consecutive_deviations 2,213,27,50,2,0.202050512628157,0,None,i7186,27,0.007997453908931779 1727501511,1727501540,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 21 update_interval 50 n_consecutive_deviations 1,100,21,50,1,0.22605651412853212,0,None,i7186,25,0.0059407709070124675 1727501511,1727501540,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 103 n_permutations 11 update_interval 50 n_consecutive_deviations 1,103,11,50,1,0.2145536384096024,0,None,i7186,25,0.006892852245319394 1727501511,1727501540,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 143 n_permutations 12 update_interval 50 n_consecutive_deviations 3,143,12,50,3,0.20505126281570396,0,None,i7186,26,0.008915494179667366 1727501511,1727501541,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 133 n_permutations 19 update_interval 78 n_consecutive_deviations 2,133,19,78,2,0.23980995248812198,0,None,i7186,26,0.010310269875161098 1727501618,1727501648,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 202 n_permutations 10 update_interval 50 n_consecutive_deviations 1,202,10,50,1,0.20405101275318827,0,None,i7186,26,0.007062249433326073 1727501632,1727501661,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 14 update_interval 50 n_consecutive_deviations 5,100,14,50,5,0.23130782695673924,0,None,i7186,26,0.009776253587206324 1727501632,1727501662,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 290 n_permutations 10 update_interval 50 n_consecutive_deviations 3,290,10,50,3,0.22205551387846967,0,None,i7186,26,0.008397099274818704 1727501638,1727501668,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 26 update_interval 50 n_consecutive_deviations 5,100,26,50,5,0.22205551387846967,0,None,i7186,26,0.009542158266839436 1727501638,1727501668,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 246 n_permutations 20 update_interval 50 n_consecutive_deviations 3,246,20,50,3,0.2110527631907977,0,None,i7186,26,0.008976202383929316 1727501659,1727501688,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 244 n_permutations 28 update_interval 50 n_consecutive_deviations 1,244,28,50,1,0.20055013753438355,0,None,i7186,26,0.00788143464437538 1727501659,1727501690,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 419 n_permutations 50 update_interval 50 n_consecutive_deviations 1,419,50,50,1,0.22955738934733683,0,None,i7186,27,0.007929867082155154 1727501659,1727501690,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 50 n_consecutive_deviations 4,100,10,50,4,0.2343085771442861,0,None,i7186,27,0.008860910879893885 1727501659,1727501690,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 303 n_permutations 29 update_interval 50 n_consecutive_deviations 1,303,29,50,1,0.20955238809702426,0,None,i7186,27,0.006862826817815565 1727501676,1727501704,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 50 n_consecutive_deviations 2,100,10,50,2,0.23455863965991497,0,None,i7181,21,0.006789197299324831 1727501676,1727501705,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 112 n_permutations 24 update_interval 50 n_consecutive_deviations 3,112,24,50,3,0.2145536384096024,0,None,i7181,22,0.008721568147138826 1727501679,1727501708,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 195 n_permutations 10 update_interval 50 n_consecutive_deviations 2,195,10,50,2,0.2145536384096024,0,None,i7186,25,0.008218400754034662 1727501679,1727501709,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 271 n_permutations 38 update_interval 50 n_consecutive_deviations 5,271,38,50,5,0.22605651412853212,0,None,i7186,26,0.010142779597338359 1727501679,1727501709,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 197 n_permutations 10 update_interval 50 n_consecutive_deviations 3,197,10,50,3,0.23305826456614154,0,None,i7186,27,0.009972005196421056 1727501679,1727501709,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 408 n_permutations 15 update_interval 50 n_consecutive_deviations 3,408,15,50,3,0.24381095273818454,0,None,i7186,27,0.01075944661841136 1727501779,1727501808,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 235 n_permutations 10 update_interval 50 n_consecutive_deviations 1,235,10,50,1,0.20680170042510626,0,None,i7186,26,0.007501875468867217 1727501783,1727501811,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 245 n_permutations 19 update_interval 50 n_consecutive_deviations 1,245,19,50,1,0.2010502625656414,0,None,i7186,25,0.007872503840245775 1727501799,1727501828,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 147 n_permutations 10 update_interval 50 n_consecutive_deviations 2,147,10,50,2,0.1975493873468367,0,None,i7186,26,0.007284608037255216 1727501799,1727501829,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 234 n_permutations 16 update_interval 50 n_consecutive_deviations 2,234,16,50,2,0.2073018254563641,0,None,i7186,26,0.008200163248359258 1727501799,1727501829,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 343 n_permutations 10 update_interval 50 n_consecutive_deviations 1,343,10,50,1,0.22030507626906726,0,None,i7186,26,0.007528667881256028 1727501799,1727501831,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 365 n_permutations 50 update_interval 50 n_consecutive_deviations 5,365,50,50,5,0.23305826456614154,0,None,i7186,28,0.01168149180152181 1727501812,1727501840,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 233 n_permutations 33 update_interval 50 n_consecutive_deviations 4,233,33,50,4,0.20405101275318827,0,None,i7173,24,0.010182778252702711 1727501813,1727501842,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 194 n_permutations 22 update_interval 50 n_consecutive_deviations 4,194,22,50,4,0.2263065766441611,0,None,i7186,26,0.010656510281416506 1727501813,1727501843,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 504 n_permutations 10 update_interval 50 n_consecutive_deviations 1,504,10,50,1,0.2200550137534384,0,None,i7186,27,0.008437109277319329 1727501813,1727501843,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 201 n_permutations 50 update_interval 50 n_consecutive_deviations 5,201,50,50,5,0.23480870217554384,0,None,i7186,26,0.010177544386096525 1727501963,1727501994,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 36 update_interval 50 n_consecutive_deviations 5,230,36,50,5,0.24531132783195797,0,None,i7186,28,0.011664680876101379 1727501979,1727502008,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 21 update_interval 50 n_consecutive_deviations 1,242,21,50,1,0.2083020755188797,0,None,i7186,25,0.007349294950856358 1727501979,1727502009,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 27 update_interval 50 n_consecutive_deviations 4,236,27,50,4,0.21355338834708681,0,None,i7186,26,0.009735388392552682 1727501993,1727502023,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 5,100,50,50,5,0.21930482620655167,0,None,i7186,26,0.009187079378540287 1727501993,1727502024,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 278 n_permutations 38 update_interval 50 n_consecutive_deviations 5,278,38,50,5,0.23455863965991497,0,None,i7186,27,0.009698853284749759 1727501993,1727502028,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 668 n_permutations 50 update_interval 50 n_consecutive_deviations 5,668,50,50,5,0.26706676669167295,0,None,i7186,30,0.01388310040473081 1727501999,1727502029,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 159 n_permutations 45 update_interval 50 n_consecutive_deviations 5,159,45,50,5,0.22655663915978996,0,None,i7186,26,0.009659391592084067 1727501999,1727502030,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 220 n_permutations 19 update_interval 50 n_consecutive_deviations 1,220,19,50,1,0.2288072018004501,0,None,i7186,27,0.006885054596982579 1727502018,1727502045,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 5,100,50,50,5,0.2180545136284071,0,None,i7173,23,0.008830332583145787 1727502019,1727502049,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 362 n_permutations 44 update_interval 50 n_consecutive_deviations 5,362,44,50,5,0.24256064016003998,0,None,i7186,26,0.010793238850253104 1727502019,1727502049,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 535 n_permutations 12 update_interval 50 n_consecutive_deviations 1,535,12,50,1,0.2198049512378094,0,None,i7186,26,0.007405360111957814 1727502019,1727502052,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 689 n_permutations 37 update_interval 50 n_consecutive_deviations 3,689,37,50,3,0.2628157039259815,0,None,i7186,29,0.012636492456447443 1727502174,1727502204,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 257 n_permutations 18 update_interval 50 n_consecutive_deviations 1,257,18,50,1,0.20530132533133283,0,None,i7186,26,0.007042083101420516 1727502179,1727502207,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 260 n_permutations 20 update_interval 50 n_consecutive_deviations 1,260,20,50,1,0.23305826456614154,0,None,i7186,25,0.006929698526326497 1727502199,1727502229,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 241 n_permutations 21 update_interval 50 n_consecutive_deviations 2,241,21,50,2,0.2190547636909227,0,None,i7186,26,0.007978409696763814 1727502199,1727502232,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 562 n_permutations 50 update_interval 57 n_consecutive_deviations 4,562,50,57,4,0.2558139534883721,0,None,i7186,29,0.012065516379094773 1727502199,1727502233,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 563 n_permutations 50 update_interval 57 n_consecutive_deviations 4,563,50,57,4,0.24756189047261812,0,None,i7186,30,0.012720922166025378 1727502199,1727502233,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 713 n_permutations 45 update_interval 50 n_consecutive_deviations 4,713,45,50,4,0.24381095273818454,0,None,i7186,30,0.012440610152538134 1727502205,1727502240,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 766 n_permutations 50 update_interval 91 n_consecutive_deviations 4,766,50,91,4,0.27431857964491124,0,None,i7186,32,0.01934694199865756 1727502219,1727502251,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 493 n_permutations 50 update_interval 50 n_consecutive_deviations 3,493,50,50,3,0.23505876469117282,0,None,i7186,28,0.010432095203288 1727502219,1727502252,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 697 n_permutations 10 update_interval 50 n_consecutive_deviations 1,697,10,50,1,0.24306076519129782,0,None,i7186,29,0.009496421724478739 1727502220,1727502253,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 562 n_permutations 50 update_interval 57 n_consecutive_deviations 4,562,50,57,4,0.2558139534883721,0,None,i7186,29,0.012065516379094773 1727502235,1727502268,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 721 n_permutations 44 update_interval 50 n_consecutive_deviations 4,721,44,50,4,0.2513128282070518,0,None,i7186,29,0.012599924174592034 1727502339,1727502369,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 278 n_permutations 10 update_interval 50 n_consecutive_deviations 1,278,10,50,1,0.2065516379094774,0,None,i7186,26,0.006697828303229653 1727502356,1727502385,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 310 n_permutations 20 update_interval 50 n_consecutive_deviations 1,310,20,50,1,0.23205801450362595,0,None,i7186,26,0.006505594652631411 1727502356,1727502385,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 336 n_permutations 23 update_interval 50 n_consecutive_deviations 1,336,23,50,1,0.21430357589397353,0,None,i7186,26,0.006896885511700505 1727502380,1727502409,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 127 n_permutations 50 update_interval 50 n_consecutive_deviations 1,127,50,50,1,0.20305076269067268,0,None,i7186,25,0.006181122745475101 1727502380,1727502410,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 319 n_permutations 23 update_interval 50 n_consecutive_deviations 1,319,23,50,1,0.22230557639409854,0,None,i7186,26,0.006878768872546005 1727502380,1727502410,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 273 n_permutations 50 update_interval 50 n_consecutive_deviations 1,273,50,50,1,0.22030507626906726,0,None,i7186,27,0.006911563956562911 1727502380,1727502410,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 252 n_permutations 13 update_interval 50 n_consecutive_deviations 2,252,13,50,2,0.22405601400350084,0,None,i7186,27,0.008035662761844307 1727502386,1727502415,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 34 update_interval 50 n_consecutive_deviations 4,100,34,50,4,0.22405601400350084,0,None,i7186,26,0.00835708927231808 1727502400,1727502428,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 123 n_permutations 50 update_interval 50 n_consecutive_deviations 1,123,50,50,1,0.21080270067516882,0,None,i7186,25,0.006531936014306607 1727502400,1727502429,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 221 n_permutations 42 update_interval 50 n_consecutive_deviations 3,221,42,50,3,0.2198049512378094,0,None,i7186,25,0.008117413968876835 1727502400,1727502429,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 265 n_permutations 50 update_interval 50 n_consecutive_deviations 3,265,50,50,3,0.21780445111277824,0,None,i7186,26,0.008482120530132533 1727502560,1727502589,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 4,100,50,50,4,0.22405601400350084,0,None,i7186,26,0.00835708927231808 1727502567,1727502595,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 36 update_interval 50 n_consecutive_deviations 4,100,36,50,4,0.22405601400350084,0,None,i7186,25,0.00835708927231808 1727502566,1727502598,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 104 n_consecutive_deviations 5,100,50,104,5,0.2585646411602901,0,None,i7186,28,0.016667210280831075 1727502580,1727502609,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 222 n_permutations 50 update_interval 50 n_consecutive_deviations 1,222,50,50,1,0.22605651412853212,0,None,i7186,26,0.006817278090014307 1727502580,1727502610,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 103 n_permutations 42 update_interval 83 n_consecutive_deviations 5,103,42,83,5,0.2468117029257314,0,None,i7186,27,0.013624095679092188 1727502597,1727502626,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 258 n_permutations 10 update_interval 50 n_consecutive_deviations 1,258,10,50,1,0.2045511377844461,0,None,i7186,26,0.0072893223305826454 1727502597,1727502628,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 88 n_consecutive_deviations 5,100,50,88,5,0.26331582895723926,0,None,i7186,28,0.015774777027590232 1727502597,1727502630,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 38 update_interval 118 n_consecutive_deviations 5,100,38,118,5,0.2745686421605401,0,None,i7186,30,0.019333780813624458 1727502620,1727502648,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 34 update_interval 50 n_consecutive_deviations 4,100,34,50,4,0.22405601400350084,0,None,i7186,25,0.00835708927231808 1727502620,1727502649,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 27 update_interval 50 n_consecutive_deviations 4,100,27,50,4,0.22155538884721182,0,None,i7186,25,0.00824225664259202 1727502620,1727502649,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 203 n_permutations 22 update_interval 50 n_consecutive_deviations 3,203,22,50,3,0.22230557639409854,0,None,i7186,25,0.009121845678811007 1727502627,1727502660,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 322 n_permutations 10 update_interval 149 n_consecutive_deviations 4,322,10,149,4,0.2773193298324581,0,None,i7186,29,0.019189007778260353 1727502778,1727502807,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 3,100,50,50,3,0.22830707676919226,0,None,i7186,26,0.007131093118107113 1727502780,1727502808,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 1,100,50,50,1,0.22430607651912982,0,None,i7186,24,0.0054234337805230525 1727502800,1727502829,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 2,100,50,50,2,0.23905976494123526,0,None,i7186,25,0.006714178544636159 1727502800,1727502829,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 235 n_permutations 30 update_interval 50 n_consecutive_deviations 1,235,30,50,1,0.2038009502375594,0,None,i7186,26,0.007182123399702384 1727502800,1727502837,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 1000 n_permutations 10 update_interval 50 n_consecutive_deviations 5,1000,10,50,5,0.3103275818954738,0,None,i7186,33,0.013263315828957241 1727502808,1727502845,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 1000 n_permutations 10 update_interval 50 n_consecutive_deviations 5,1000,10,50,5,0.3103275818954738,0,None,i7186,33,0.013263315828957241 1727502820,1727502849,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 201 n_permutations 50 update_interval 50 n_consecutive_deviations 1,201,50,50,1,0.2198049512378094,0,None,i7186,25,0.006808153651316056 1727502820,1727502850,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 32 update_interval 50 n_consecutive_deviations 1,214,32,50,1,0.2073018254563641,0,None,i7186,26,0.0067907601900475114 1727502838,1727502867,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 162 n_permutations 50 update_interval 50 n_consecutive_deviations 2,162,50,50,2,0.2208052013003251,0,None,i7186,25,0.007018421271984663 1727502838,1727502867,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 196 n_permutations 23 update_interval 50 n_consecutive_deviations 2,196,23,50,2,0.2110527631907977,0,None,i7186,25,0.007833776625974676 1727502838,1727502873,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 714 n_permutations 35 update_interval 50 n_consecutive_deviations 5,714,35,50,5,0.24256064016003998,0,None,i7186,32,0.014262494194977316 1727502860,1727502891,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 227 n_permutations 28 update_interval 50 n_consecutive_deviations 1,227,28,50,1,0.19704926231557884,0,None,i7186,25,0.007061289131806762 1727502860,1727502891,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 334 n_permutations 50 update_interval 50 n_consecutive_deviations 2,334,50,50,2,0.22305576394098525,0,None,i7186,26,0.007902919126007917 1727503040,1727503070,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 299 n_permutations 50 update_interval 50 n_consecutive_deviations 3,299,50,50,3,0.24131032758189552,0,None,i7186,26,0.008902225556389096 1727503049,1727503077,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 221 n_permutations 14 update_interval 50 n_consecutive_deviations 1,221,14,50,1,0.22505626406601653,0,None,i7186,25,0.006833675632022759 1727503049,1727503078,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 268 n_permutations 32 update_interval 50 n_consecutive_deviations 3,268,32,50,3,0.22405601400350084,0,None,i7186,26,0.008890520502466043 1727503060,1727503094,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 250 n_consecutive_deviations 5,100,10,250,5,0.2935733933483371,0,None,i7186,30,0.02488122030507627 1727503079,1727503109,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 10 update_interval 50 n_consecutive_deviations 1,242,10,50,1,0.20705176294073513,0,None,i7186,26,0.00749756404618396 1727503079,1727503114,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 266 n_permutations 42 update_interval 225 n_consecutive_deviations 1,266,42,225,1,0.3053263315828957,0,None,i7186,31,0.019799067413912304 1727503079,1727503114,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 265 n_permutations 42 update_interval 225 n_consecutive_deviations 1,265,42,225,1,0.3008252063015754,0,None,i7186,32,0.02131782945736434 1727503101,1727503131,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 281 n_permutations 28 update_interval 50 n_consecutive_deviations 2,281,28,50,2,0.24006001500375096,0,None,i7186,26,0.007306372047557344 1727503101,1727503134,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 185 n_consecutive_deviations 5,100,10,185,5,0.2783195798949737,0,None,i7186,29,0.021387699866143008 1727503101,1727503137,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 11 update_interval 241 n_consecutive_deviations 5,226,11,241,5,0.29482370592648166,0,None,i7186,33,0.02892389764107693 1727503109,1727503138,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 201 n_permutations 21 update_interval 50 n_consecutive_deviations 1,201,21,50,1,0.22130532633158284,0,None,i7186,25,0.007010085854797034 1727503121,1727503154,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 101 n_permutations 31 update_interval 229 n_consecutive_deviations 5,101,31,229,5,0.2683170792698174,0,None,i7186,30,0.01966281043945197 1727503139,1727503173,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 222 n_permutations 19 update_interval 250 n_consecutive_deviations 4,222,19,250,4,0.2828207051762941,0,None,i7186,31,0.025649269460222196 1727503139,1727503174,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 20 update_interval 194 n_consecutive_deviations 5,100,20,194,5,0.29407351837959494,0,None,i7186,31,0.02484549708855785 1727503139,1727503177,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 163 n_permutations 12 update_interval 249 n_consecutive_deviations 5,163,12,249,5,0.3520880220055014,0,None,i7186,34,0.026347495964900314 1727503341,1727503369,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 251 n_permutations 10 update_interval 50 n_consecutive_deviations 1,251,10,50,1,0.22155538884721182,0,None,i7186,25,0.007374650680213913 1727503341,1727503371,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 227 n_permutations 30 update_interval 50 n_consecutive_deviations 1,227,30,50,1,0.19529882470617654,0,None,i7186,26,0.006870948506357359 1727503350,1727503379,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 239 n_permutations 10 update_interval 50 n_consecutive_deviations 1,239,10,50,1,0.20805201300325082,0,None,i7186,26,0.00788833572029371 1727503361,1727503389,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 225 n_permutations 29 update_interval 50 n_consecutive_deviations 1,225,29,50,1,0.20230057514378597,0,None,i7186,25,0.006977934959930458 1727503361,1727503394,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 679 n_permutations 31 update_interval 65 n_consecutive_deviations 5,679,31,65,5,0.2773193298324581,0,None,i7186,30,0.015191297824456114 1727503380,1727503409,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 233 n_permutations 10 update_interval 50 n_consecutive_deviations 1,233,10,50,1,0.20080020005001253,0,None,i7186,25,0.007351837959489872 1727503380,1727503411,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 338 n_permutations 27 update_interval 96 n_consecutive_deviations 2,338,27,96,2,0.24281070267566895,0,None,i7186,27,0.012093932574052602 1727503401,1727503430,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 243 n_permutations 10 update_interval 50 n_consecutive_deviations 1,243,10,50,1,0.1992998249562391,0,None,i7186,26,0.007903761654699389 1727503401,1727503430,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 239 n_permutations 10 update_interval 50 n_consecutive_deviations 1,239,10,50,1,0.20705176294073513,0,None,i7186,26,0.007906522085066722 1727503410,1727503439,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 5,100,50,50,5,0.2180545136284071,0,None,i7186,25,0.008830332583145787 1727503421,1727503450,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 28 update_interval 50 n_consecutive_deviations 1,226,28,50,1,0.1937984496124031,0,None,i7186,25,0.006894031200107719 1727503441,1727503470,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 225 n_permutations 30 update_interval 50 n_consecutive_deviations 1,225,30,50,1,0.20230057514378597,0,None,i7186,25,0.006977934959930458 1727503441,1727503470,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 148 n_permutations 10 update_interval 61 n_consecutive_deviations 3,148,10,61,3,0.2388097024256064,0,None,i7186,26,0.0103359173126615 1727503441,1727503470,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 257 n_permutations 12 update_interval 71 n_consecutive_deviations 1,257,12,71,1,0.22330582645661412,0,None,i7186,26,0.009513742071881607 1727503461,1727503489,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 190 n_permutations 19 update_interval 50 n_consecutive_deviations 1,190,19,50,1,0.23630907726931738,0,None,i7186,24,0.006649203284427663 1727503461,1727503490,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 148 n_permutations 10 update_interval 50 n_consecutive_deviations 3,148,10,50,3,0.22680670167541883,0,None,i7186,25,0.009023995129217087 1727503713,1727503743,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 185 n_permutations 38 update_interval 66 n_consecutive_deviations 3,185,38,66,3,0.22955738934733683,0,None,i7186,26,0.012886034008502125 1727503721,1727503749,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 232 n_permutations 31 update_interval 50 n_consecutive_deviations 1,232,31,50,1,0.20080020005001253,0,None,i7186,25,0.006683489054081702 1727503741,1727503770,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 19 update_interval 54 n_consecutive_deviations 2,236,19,54,2,0.20605151287821954,0,None,i7186,26,0.008895080913085414 1727503741,1727503771,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 104 n_permutations 28 update_interval 86 n_consecutive_deviations 3,104,28,86,3,0.24081020255063768,0,None,i7186,26,0.012938718550605393 1727503741,1727503772,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 92 n_consecutive_deviations 3,100,10,92,3,0.2610652663165791,0,None,i7186,27,0.012694840376760858 1727503761,1727503792,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 200 n_permutations 14 update_interval 97 n_consecutive_deviations 1,200,14,97,1,0.24756189047261812,0,None,i7186,27,0.011949957186266263 1727503761,1727503793,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 107 n_permutations 29 update_interval 85 n_consecutive_deviations 3,107,29,85,3,0.23930982745686424,0,None,i7186,27,0.013420021672084687 1727503773,1727503804,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 50 update_interval 96 n_consecutive_deviations 3,214,50,96,3,0.2343085771442861,0,None,i7186,27,0.01455721073125424 1727503781,1727503810,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 185 n_permutations 38 update_interval 66 n_consecutive_deviations 3,185,38,66,3,0.22955738934733683,0,None,i7186,26,0.012886034008502125 1727503801,1727503831,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 185 n_permutations 38 update_interval 66 n_consecutive_deviations 3,185,38,66,3,0.22955738934733683,0,None,i7186,26,0.012886034008502125 1727503801,1727503831,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 186 n_permutations 43 update_interval 51 n_consecutive_deviations 3,186,43,51,3,0.21680420105026255,0,None,i7186,26,0.009886192478352146 1727503821,1727503853,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 599 n_permutations 50 update_interval 50 n_consecutive_deviations 1,599,50,50,1,0.2513128282070518,0,None,i7186,28,0.008137451029424021 1727503821,1727503855,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 1000 n_permutations 50 update_interval 50 n_consecutive_deviations 1,1000,50,50,1,0.2818204551137784,0,None,i7186,31,0.008002000500125032 1727503833,1727503863,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 298 n_permutations 38 update_interval 65 n_consecutive_deviations 3,298,38,65,3,0.23905976494123526,0,None,i7186,27,0.010887857099409988 1727503841,1727503871,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 245 n_permutations 23 update_interval 77 n_consecutive_deviations 2,245,23,77,2,0.22830707676919226,0,None,i7186,26,0.011488983356950349 1727504102,1727504131,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 32 update_interval 50 n_consecutive_deviations 1,238,32,50,1,0.20230057514378597,0,None,i7186,25,0.007850176829921765 1727504122,1727504150,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 270 n_permutations 10 update_interval 50 n_consecutive_deviations 1,270,10,50,1,0.22455613903475868,0,None,i7186,25,0.007073802348892308 1727504122,1727504152,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 489 n_permutations 38 update_interval 64 n_consecutive_deviations 1,489,38,64,1,0.23680920230057512,0,None,i7186,27,0.009002250562640661 1727504134,1727504164,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 356 n_permutations 10 update_interval 50 n_consecutive_deviations 2,356,10,50,2,0.21880470117529383,0,None,i7186,26,0.00829619169498257 1727504142,1727504171,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 240 n_permutations 31 update_interval 50 n_consecutive_deviations 1,240,31,50,1,0.21505376344086025,0,None,i7186,25,0.0074887142838341155 1727504162,1727504192,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 466 n_permutations 18 update_interval 50 n_consecutive_deviations 2,466,18,50,2,0.2093023255813954,0,None,i7186,26,0.009404525044304553 1727504162,1727504195,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 690 n_permutations 47 update_interval 59 n_consecutive_deviations 1,690,47,59,1,0.2745686421605401,0,None,i7186,29,0.010203939873857353 1727504182,1727504211,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 108 n_permutations 28 update_interval 51 n_consecutive_deviations 1,108,28,51,1,0.20530132533133283,0,None,i7186,25,0.0069303040045725715 1727504182,1727504212,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 31 update_interval 50 n_consecutive_deviations 1,238,31,50,1,0.202050512628157,0,None,i7186,26,0.007854642231986569 1727504195,1727504224,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 222 n_permutations 10 update_interval 50 n_consecutive_deviations 2,222,10,50,2,0.22330582645661412,0,None,i7186,25,0.008050089445438283 1727504195,1727504227,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 700 n_permutations 42 update_interval 64 n_consecutive_deviations 1,700,42,64,1,0.2655663915978995,0,None,i7186,29,0.009903791737408034 1727504222,1727504255,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 677 n_permutations 42 update_interval 58 n_consecutive_deviations 5,677,42,58,5,0.2863215803950988,0,None,i7186,30,0.015460386835839392 1727504222,1727504256,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 872 n_permutations 23 update_interval 50 n_consecutive_deviations 1,872,23,50,1,0.25606401600400097,0,None,i7186,30,0.007874417583987834 1727504242,1727504271,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 289 n_permutations 10 update_interval 50 n_consecutive_deviations 3,289,10,50,3,0.23680920230057512,0,None,i7186,25,0.008102025506376594 1727504242,1727504272,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 358 n_permutations 16 update_interval 65 n_consecutive_deviations 2,358,16,65,2,0.22380595148787197,0,None,i7186,27,0.010197671369061778 1727504255,1727504284,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 239 n_permutations 31 update_interval 50 n_consecutive_deviations 1,239,31,50,1,0.20680170042510626,0,None,i7186,25,0.007769799592755332 1727504436,1727504466,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 223 n_permutations 32 update_interval 50 n_consecutive_deviations 1,223,32,50,1,0.20755188797199298,0,None,i7186,26,0.006682439840729413 1727504442,1727504471,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 280 n_permutations 10 update_interval 50 n_consecutive_deviations 1,280,10,50,1,0.23505876469117282,0,None,i7186,25,0.006562124402068259 1727504462,1727504491,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 219 n_permutations 32 update_interval 50 n_consecutive_deviations 1,219,32,50,1,0.2093023255813954,0,None,i7186,25,0.007091936918655893 1727504462,1727504495,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 791 n_permutations 25 update_interval 55 n_consecutive_deviations 1,791,25,55,1,0.25556389097274324,0,None,i7186,29,0.010167015438070042 1727504482,1727504511,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 263 n_permutations 10 update_interval 50 n_consecutive_deviations 1,263,10,50,1,0.23030757689422354,0,None,i7186,25,0.006976320351274259 1727504482,1727504512,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 223 n_permutations 32 update_interval 50 n_consecutive_deviations 1,223,32,50,1,0.20755188797199298,0,None,i7186,26,0.006682439840729413 1727504496,1727504525,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 221 n_permutations 32 update_interval 50 n_consecutive_deviations 1,221,32,50,1,0.22330582645661412,0,None,i7186,25,0.006862371330537553 1727504502,1727504531,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 223 n_permutations 33 update_interval 50 n_consecutive_deviations 1,223,33,50,1,0.20755188797199298,0,None,i7186,25,0.006682439840729413 1727504522,1727504552,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 222 n_permutations 33 update_interval 50 n_consecutive_deviations 1,222,33,50,1,0.22755688922230555,0,None,i7186,25,0.006683122393501602 1727504522,1727504552,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 219 n_permutations 32 update_interval 50 n_consecutive_deviations 1,219,32,50,1,0.2093023255813954,0,None,i7186,25,0.007091936918655893 1727504542,1727504572,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 268 n_permutations 10 update_interval 50 n_consecutive_deviations 1,268,10,50,1,0.21080270067516882,0,None,i7186,26,0.007185129615737267 1727504557,1727504586,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 32 update_interval 50 n_consecutive_deviations 1,226,32,50,1,0.1937984496124031,0,None,i7186,25,0.006894031200107719 1727504557,1727504586,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 320 n_permutations 10 update_interval 74 n_consecutive_deviations 1,320,10,74,1,0.24481120280070012,0,None,i7186,26,0.009454744638540588 1727504562,1727504593,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 353 n_permutations 10 update_interval 86 n_consecutive_deviations 2,353,10,86,2,0.23555888972243055,0,None,i7186,27,0.011951517291087479 1727504582,1727504615,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 336 n_permutations 25 update_interval 134 n_consecutive_deviations 2,336,25,134,2,0.25906476619154784,0,None,i7186,29,0.015951904642827374 1727504858,1727504888,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 217 n_permutations 32 update_interval 50 n_consecutive_deviations 1,217,32,50,1,0.20355088772193053,0,None,i7186,26,0.006958088728531338 1727504883,1727504911,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 25 update_interval 50 n_consecutive_deviations 1,216,25,50,1,0.21080270067516882,0,None,i7186,25,0.006953351241036065 1727504883,1727504915,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 232 n_permutations 39 update_interval 135 n_consecutive_deviations 3,232,39,135,3,0.26906726681670423,0,None,i7186,29,0.019623326884352664 1727504888,1727504918,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 32 update_interval 50 n_consecutive_deviations 1,214,32,50,1,0.2073018254563641,0,None,i7186,26,0.0067907601900475114 1727504903,1727504932,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 213 n_permutations 32 update_interval 50 n_consecutive_deviations 1,213,32,50,1,0.2083020755188797,0,None,i7186,25,0.0069936839048471795 1727504918,1727504947,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 50 n_consecutive_deviations 1,100,10,50,1,0.22130532633158284,0,None,i7186,25,0.005924016215321437 1727504918,1727504949,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 258 n_permutations 35 update_interval 108 n_consecutive_deviations 2,258,35,108,2,0.24206051512878224,0,None,i7186,27,0.012898385886794277 1727504943,1727504972,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 157 n_permutations 46 update_interval 50 n_consecutive_deviations 4,157,46,50,4,0.2388097024256064,0,None,i7186,25,0.008957795004306632 1727504943,1727504973,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 19 update_interval 50 n_consecutive_deviations 5,100,19,50,5,0.23130782695673924,0,None,i7186,26,0.009776253587206324 1727504963,1727504997,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 844 n_permutations 31 update_interval 105 n_consecutive_deviations 2,844,31,105,2,0.2838209552388097,0,None,i7186,31,0.016276796471845236 1727504979,1727505008,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 218 n_permutations 32 update_interval 50 n_consecutive_deviations 1,218,32,50,1,0.20530132533133283,0,None,i7186,25,0.007157527086689705 1727504979,1727505009,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 32 update_interval 50 n_consecutive_deviations 1,216,32,50,1,0.2110527631907977,0,None,i7186,26,0.006839011340136622 1727505003,1727505031,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 32 update_interval 50 n_consecutive_deviations 1,214,32,50,1,0.2073018254563641,0,None,i7186,25,0.0067907601900475114 1727505003,1727505031,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 240 n_permutations 26 update_interval 50 n_consecutive_deviations 1,240,26,50,1,0.21505376344086025,0,None,i7186,25,0.0074887142838341155 1727505023,1727505052,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 218 n_permutations 32 update_interval 50 n_consecutive_deviations 1,218,32,50,1,0.20530132533133283,0,None,i7186,26,0.007157527086689705 1727505039,1727505068,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 50 n_consecutive_deviations 5,100,10,50,5,0.2333083270817704,0,None,i7186,25,0.00996590611067401 1727505383,1727505413,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 237 n_permutations 28 update_interval 50 n_consecutive_deviations 1,237,28,50,1,0.2028007001750438,0,None,i7186,25,0.007983814135352018 1727505401,1727505429,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 32 update_interval 50 n_consecutive_deviations 1,216,32,50,1,0.2110527631907977,0,None,i7186,25,0.006839011340136622 1727505401,1727505432,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 178 n_permutations 33 update_interval 106 n_consecutive_deviations 4,178,33,106,4,0.2645661415353838,0,None,i7186,27,0.01451324369553927 1727505424,1727505452,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 215 n_permutations 32 update_interval 50 n_consecutive_deviations 1,215,32,50,1,0.21655413853463368,0,None,i7186,25,0.006751687921980495 1727505424,1727505453,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 215 n_permutations 32 update_interval 50 n_consecutive_deviations 1,215,32,50,1,0.21655413853463368,0,None,i7186,26,0.006751687921980495 1727505431,1727505459,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 32 update_interval 50 n_consecutive_deviations 1,214,32,50,1,0.2073018254563641,0,None,i7186,25,0.0067907601900475114 1727505443,1727505475,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 695 n_permutations 45 update_interval 70 n_consecutive_deviations 2,695,45,70,2,0.24656164041010253,0,None,i7186,28,0.011980267794221282 1727505461,1727505489,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 32 update_interval 50 n_consecutive_deviations 1,214,32,50,1,0.2073018254563641,0,None,i7186,25,0.0067907601900475114 1727505484,1727505512,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 50 n_consecutive_deviations 1,100,10,50,1,0.22130532633158284,0,None,i7186,24,0.005924016215321437 1727505484,1727505512,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 212 n_permutations 33 update_interval 50 n_consecutive_deviations 1,212,33,50,1,0.2198049512378094,0,None,i7186,25,0.006700087720342785 1727505504,1727505535,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 246 n_permutations 50 update_interval 114 n_consecutive_deviations 1,246,50,114,1,0.24731182795698925,0,None,i7186,28,0.012331207801950487 1727505521,1727505550,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 32 update_interval 50 n_consecutive_deviations 1,214,32,50,1,0.2073018254563641,0,None,i7186,25,0.0067907601900475114 1727505522,1727505550,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 32 update_interval 50 n_consecutive_deviations 1,214,32,50,1,0.2083020755188797,0,None,i7186,25,0.006882673049214685 1727505544,1727505573,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 22 update_interval 50 n_consecutive_deviations 2,100,22,50,2,0.23580895223805953,0,None,i7186,25,0.006657402055431891 1727505544,1727505573,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 239 n_permutations 27 update_interval 50 n_consecutive_deviations 1,239,27,50,1,0.20780195048762196,0,None,i7186,25,0.007751937984496123 1727505551,1727505579,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 33 update_interval 50 n_consecutive_deviations 1,214,33,50,1,0.2073018254563641,0,None,i7186,25,0.0067907601900475114 1727506064,1727506093,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 267 n_permutations 25 update_interval 50 n_consecutive_deviations 2,267,25,50,2,0.21080270067516882,0,None,i7186,25,0.008134108998947849 1727506064,1727506094,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 217 n_permutations 32 update_interval 50 n_consecutive_deviations 1,217,32,50,1,0.20355088772193053,0,None,i7186,26,0.006958088728531338 1727506084,1727506113,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 180 n_permutations 10 update_interval 50 n_consecutive_deviations 1,180,10,50,1,0.2253063265816454,0,None,i7186,25,0.006829576246520646 1727506084,1727506113,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 16 update_interval 50 n_consecutive_deviations 2,100,16,50,2,0.23855963990997753,0,None,i7186,25,0.006612308814908645 1727506094,1727506124,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 31 update_interval 104 n_consecutive_deviations 2,100,31,104,2,0.24481120280070012,0,None,i7186,26,0.011030535411630687 1727506104,1727506133,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 210 n_permutations 27 update_interval 50 n_consecutive_deviations 1,210,27,50,1,0.23480870217554384,0,None,i7186,25,0.005986790815350897 1727506124,1727506156,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 503 n_permutations 41 update_interval 126 n_consecutive_deviations 1,503,41,126,1,0.26256564141035255,0,None,i7186,28,0.014049808748483418 1727506144,1727506175,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 352 n_permutations 10 update_interval 57 n_consecutive_deviations 3,352,10,57,3,0.24006001500375096,0,None,i7186,27,0.010575012174096154 1727506154,1727506183,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 28 update_interval 50 n_consecutive_deviations 2,100,28,50,2,0.2370592648162041,0,None,i7186,25,0.006636905127921324 1727506164,1727506193,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 223 n_permutations 28 update_interval 50 n_consecutive_deviations 1,223,28,50,1,0.20755188797199298,0,None,i7186,24,0.006682439840729413 1727506185,1727506213,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 191 n_permutations 24 update_interval 50 n_consecutive_deviations 3,191,24,50,3,0.21955488872218054,0,None,i7186,25,0.008986289125472858 1727506185,1727506214,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 233 n_permutations 27 update_interval 50 n_consecutive_deviations 1,233,27,50,1,0.19879969992498125,0,None,i7186,26,0.007264111109744649 1727506205,1727506233,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 215 n_permutations 32 update_interval 50 n_consecutive_deviations 1,215,32,50,1,0.21655413853463368,0,None,i7186,25,0.006751687921980495 1727506215,1727506249,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 713 n_permutations 50 update_interval 63 n_consecutive_deviations 5,713,50,63,5,0.25431357839459867,0,None,i7186,30,0.01614987080103359 1727506225,1727506253,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 32 update_interval 50 n_consecutive_deviations 1,214,32,50,1,0.2073018254563641,0,None,i7186,25,0.0067907601900475114 1727506245,1727506273,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 32 update_interval 50 n_consecutive_deviations 1,216,32,50,1,0.2110527631907977,0,None,i7186,25,0.006839011340136622 1727506245,1727506275,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 321 n_permutations 25 update_interval 50 n_consecutive_deviations 5,321,25,50,5,0.23980995248812198,0,None,i7186,26,0.012184864397917662 1727506517,1727506546,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 219 n_permutations 33 update_interval 50 n_consecutive_deviations 1,219,33,50,1,0.2090522630657664,0,None,i7186,25,0.006981584105703846 1727506545,1727506574,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 220 n_permutations 33 update_interval 50 n_consecutive_deviations 1,220,33,50,1,0.23030757689422354,0,None,i7186,25,0.006747588536478382 1727506545,1727506574,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 263 n_permutations 29 update_interval 50 n_consecutive_deviations 1,263,29,50,1,0.2298074518629657,0,None,i7186,25,0.006868383762607319 1727506565,1727506594,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 220 n_permutations 33 update_interval 50 n_consecutive_deviations 1,220,33,50,1,0.23030757689422354,0,None,i7186,26,0.006747588536478382 1727506577,1727506611,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 768 n_permutations 17 update_interval 105 n_consecutive_deviations 1,768,17,105,1,0.27081770442610653,0,None,i7186,31,0.015462198883054097 1727506585,1727506613,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 220 n_permutations 33 update_interval 50 n_consecutive_deviations 1,220,33,50,1,0.23030757689422354,0,None,i7186,25,0.006747588536478382 1727506605,1727506634,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 220 n_permutations 33 update_interval 50 n_consecutive_deviations 1,220,33,50,1,0.2288072018004501,0,None,i7186,25,0.006772184849491061 1727506605,1727506636,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 47 update_interval 73 n_consecutive_deviations 4,100,47,73,4,0.23755938984746183,0,None,i7186,27,0.011552888222055515 1727506625,1727506653,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 220 n_permutations 33 update_interval 50 n_consecutive_deviations 1,220,33,50,1,0.2325581395348837,0,None,i7186,25,0.006710694066959363 1727506637,1727506666,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 221 n_permutations 33 update_interval 50 n_consecutive_deviations 1,221,33,50,1,0.2253063265816454,0,None,i7186,25,0.006829576246520646 1727506645,1727506675,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 218 n_permutations 33 update_interval 50 n_consecutive_deviations 1,218,33,50,1,0.20505126281570396,0,None,i7186,26,0.007161626472191818 1727506665,1727506694,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 279 n_permutations 50 update_interval 57 n_consecutive_deviations 2,279,50,57,2,0.2370592648162041,0,None,i7186,25,0.007638702128362278 1727506685,1727506716,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 164 n_permutations 35 update_interval 94 n_consecutive_deviations 3,164,35,94,3,0.26081520380095025,0,None,i7186,27,0.013610545493516235 1727507126,1727507155,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 227 n_permutations 29 update_interval 50 n_consecutive_deviations 1,227,29,50,1,0.1957989497374344,0,None,i7186,25,0.006863254275107237 1727507146,1727507174,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 275 n_permutations 10 update_interval 50 n_consecutive_deviations 2,275,10,50,2,0.21755438859714926,0,None,i7186,25,0.007858446093004732 1727507166,1727507195,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 30 update_interval 50 n_consecutive_deviations 1,226,30,50,1,0.19454863715928983,0,None,i7186,25,0.0066770423949270895 1727507166,1727507195,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 225 n_permutations 29 update_interval 50 n_consecutive_deviations 1,225,29,50,1,0.20230057514378597,0,None,i7186,25,0.006977934959930458 1727507180,1727507208,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 281 n_permutations 43 update_interval 50 n_consecutive_deviations 1,281,43,50,1,0.23755938984746183,0,None,i7186,25,0.006035090862267806 1727507206,1727507235,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 232 n_permutations 29 update_interval 50 n_consecutive_deviations 1,232,29,50,1,0.20055013753438355,0,None,i7186,25,0.00668727788007608 1727507206,1727507238,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 695 n_permutations 35 update_interval 55 n_consecutive_deviations 1,695,35,55,1,0.2495623905976494,0,None,i7186,29,0.00912437411678501 1727507226,1727507254,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 267 n_permutations 25 update_interval 50 n_consecutive_deviations 1,267,25,50,1,0.21605401350337583,0,None,i7186,25,0.006981253510098836 1727507240,1727507270,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 29 update_interval 50 n_consecutive_deviations 1,229,29,50,1,0.20805201300325082,0,None,i7186,26,0.0067790385096274065 1727507246,1727507275,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 176 n_permutations 50 update_interval 50 n_consecutive_deviations 2,176,50,50,2,0.21330332583145784,0,None,i7186,25,0.008242445226691288 1727507266,1727507296,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 453 n_permutations 50 update_interval 58 n_consecutive_deviations 2,453,50,58,2,0.22555638909727427,0,None,i7186,27,0.010408852213053264 1727507286,1727507315,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 225 n_permutations 29 update_interval 50 n_consecutive_deviations 1,225,29,50,1,0.20230057514378597,0,None,i7186,25,0.006977934959930458 1727507301,1727507329,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 227 n_permutations 29 update_interval 50 n_consecutive_deviations 1,227,29,50,1,0.1957989497374344,0,None,i7186,25,0.006863254275107237 1727507306,1727507334,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 258 n_permutations 25 update_interval 50 n_consecutive_deviations 1,258,25,50,1,0.20405101275318827,0,None,i7186,25,0.007178024014200272 1727507326,1727507356,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 279 n_permutations 50 update_interval 57 n_consecutive_deviations 2,279,50,57,2,0.2370592648162041,0,None,i7186,27,0.007638702128362278 1727507687,1727507716,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 224 n_permutations 28 update_interval 50 n_consecutive_deviations 1,224,28,50,1,0.20855213803450867,0,None,i7186,26,0.00687870380293486 1727507706,1727507735,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 225 n_permutations 28 update_interval 50 n_consecutive_deviations 1,225,28,50,1,0.20230057514378597,0,None,i7186,25,0.006977934959930458 1727507723,1727507752,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 224 n_permutations 28 update_interval 50 n_consecutive_deviations 1,224,28,50,1,0.20855213803450867,0,None,i7186,26,0.00687870380293486 1727507727,1727507756,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 27 update_interval 50 n_consecutive_deviations 1,226,27,50,1,0.19454863715928983,0,None,i7186,25,0.0066770423949270895 1727507746,1727507775,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 225 n_permutations 28 update_interval 50 n_consecutive_deviations 1,225,28,50,1,0.20330082520630155,0,None,i7186,25,0.006747840806355435 1727507753,1727507782,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 225 n_permutations 28 update_interval 50 n_consecutive_deviations 1,225,28,50,1,0.20330082520630155,0,None,i7186,25,0.006747840806355435 1727507767,1727507798,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 315 n_permutations 27 update_interval 121 n_consecutive_deviations 3,315,27,121,3,0.2460615153788447,0,None,i7186,28,0.01649370676002334 1727507783,1727507811,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 224 n_permutations 27 update_interval 50 n_consecutive_deviations 1,224,27,50,1,0.20855213803450867,0,None,i7186,24,0.00687870380293486 1727507807,1727507837,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 255 n_permutations 23 update_interval 60 n_consecutive_deviations 3,255,23,60,3,0.22205551387846967,0,None,i7186,26,0.009330110305354114 1727507814,1727507842,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 227 n_permutations 28 update_interval 50 n_consecutive_deviations 1,227,28,50,1,0.1955488872218054,0,None,i7186,25,0.0068671013907322985 1727507827,1727507855,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 225 n_permutations 28 update_interval 50 n_consecutive_deviations 1,225,28,50,1,0.20330082520630155,0,None,i7186,25,0.006747840806355435 1727507844,1727507877,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 821 n_permutations 17 update_interval 75 n_consecutive_deviations 2,821,17,75,2,0.28332083020755183,0,None,i7186,30,0.01434358589647412 1727507867,1727507895,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 28 update_interval 50 n_consecutive_deviations 1,230,28,50,1,0.22480620155038755,0,None,i7186,25,0.006044989508246628 1727507874,1727507902,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 224 n_permutations 27 update_interval 50 n_consecutive_deviations 1,224,27,50,1,0.20855213803450867,0,None,i7186,25,0.00687870380293486 1727508206,1727508238,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20955238809702426,0,None,i7186,26,0.006651662915728932 1727508227,1727508257,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 319 n_permutations 13 update_interval 120 n_consecutive_deviations 2,319,13,120,2,0.2613153288322081,0,None,i7186,27,0.01522380595148787 1727508236,1727508265,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 228 n_permutations 27 update_interval 50 n_consecutive_deviations 1,228,27,50,1,0.19904976244061012,0,None,i7186,25,0.0069196986746686675 1727508247,1727508276,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20680170042510626,0,None,i7186,25,0.006798574643660915 1727508266,1727508295,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20680170042510626,0,None,i7186,25,0.006798574643660915 1727508287,1727508316,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,25,0.006141241192651103 1727508287,1727508317,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 126 n_permutations 44 update_interval 61 n_consecutive_deviations 4,126,44,61,4,0.22905726431607898,0,None,i7186,26,0.011158194954143942 1727508307,1727508335,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,25,0.006141241192651103 1727508327,1727508357,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 318 n_permutations 15 update_interval 87 n_consecutive_deviations 1,318,15,87,1,0.22105526381595397,0,None,i7186,26,0.01026476131227929 1727508348,1727508376,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20680170042510626,0,None,i7186,25,0.006798574643660915 1727508348,1727508376,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,25,0.006141241192651103 1727508367,1727508397,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 228 n_permutations 27 update_interval 50 n_consecutive_deviations 1,228,27,50,1,0.19904976244061012,0,None,i7186,26,0.0069196986746686675 1727508387,1727508416,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,25,0.006141241192651103 1727508387,1727508416,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20680170042510626,0,None,i7186,25,0.006798574643660915 1727508408,1727508437,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 240 n_permutations 26 update_interval 50 n_consecutive_deviations 1,240,26,50,1,0.21230307576894225,0,None,i7186,25,0.007407024169835562 1727508779,1727508808,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,25,0.006141241192651103 1727508808,1727508837,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 233 n_permutations 27 update_interval 50 n_consecutive_deviations 1,233,27,50,1,0.1992998249562391,0,None,i7186,25,0.007138881494567189 1727508808,1727508837,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 146 n_permutations 31 update_interval 74 n_consecutive_deviations 2,146,31,74,2,0.2200550137534384,0,None,i7186,25,0.009170770953607967 1727508828,1727508857,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 232 n_permutations 27 update_interval 50 n_consecutive_deviations 1,232,27,50,1,0.20055013753438355,0,None,i7186,26,0.00668727788007608 1727508839,1727508868,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 234 n_permutations 27 update_interval 50 n_consecutive_deviations 1,234,27,50,1,0.2063015753938484,0,None,i7186,25,0.007141129544681253 1727508848,1727508877,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 231 n_permutations 27 update_interval 50 n_consecutive_deviations 1,231,27,50,1,0.21605401350337583,0,None,i7186,25,0.006654007251812953 1727508868,1727508898,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 237 n_permutations 21 update_interval 50 n_consecutive_deviations 3,237,21,50,3,0.21405351337834455,0,None,i7186,26,0.009301238353066527 1727508888,1727508916,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 248 n_permutations 25 update_interval 50 n_consecutive_deviations 1,248,25,50,1,0.20680170042510626,0,None,i7186,24,0.008057569948042567 1727508908,1727508943,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 903 n_permutations 40 update_interval 66 n_consecutive_deviations 4,903,40,66,4,0.28857214303575895,0,None,i7186,31,0.014722430607651911 1727508928,1727508956,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 233 n_permutations 27 update_interval 50 n_consecutive_deviations 1,233,27,50,1,0.1992998249562391,0,None,i7186,25,0.007138881494567189 1727508948,1727508978,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 232 n_permutations 27 update_interval 50 n_consecutive_deviations 1,232,27,50,1,0.20055013753438355,0,None,i7186,25,0.00668727788007608 1727508948,1727508979,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 233 n_permutations 27 update_interval 50 n_consecutive_deviations 1,233,27,50,1,0.1992998249562391,0,None,i7186,26,0.007138881494567189 1727508968,1727508996,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 232 n_permutations 27 update_interval 50 n_consecutive_deviations 1,232,27,50,1,0.20055013753438355,0,None,i7186,24,0.00668727788007608 1727509331,1727509360,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 27 update_interval 50 n_consecutive_deviations 1,236,27,50,1,0.20980245061265312,0,None,i7186,25,0.007716214767977709 1727509351,1727509380,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 27 update_interval 50 n_consecutive_deviations 1,238,27,50,1,0.2063015753938484,0,None,i7186,25,0.00792016185864648 1727509372,1727509400,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 233 n_permutations 27 update_interval 50 n_consecutive_deviations 1,233,27,50,1,0.1992998249562391,0,None,i7186,25,0.007138881494567189 1727509372,1727509402,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 188 n_permutations 43 update_interval 94 n_consecutive_deviations 2,188,43,94,2,0.23555888972243055,0,None,i7186,27,0.012313684481726494 1727509391,1727509424,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 452 n_permutations 17 update_interval 108 n_consecutive_deviations 2,452,17,108,2,0.26906726681670423,0,None,i7186,29,0.016947418672850028 1727509412,1727509441,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 244 n_permutations 26 update_interval 50 n_consecutive_deviations 1,244,26,50,1,0.2003000750187547,0,None,i7186,25,0.007747550922818424 1727509432,1727509460,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 241 n_permutations 27 update_interval 50 n_consecutive_deviations 1,241,27,50,1,0.22280570142535638,0,None,i7186,25,0.007484013860608008 1727509442,1727509470,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 151 n_permutations 50 update_interval 57 n_consecutive_deviations 3,151,50,57,3,0.21605401350337583,0,None,i7186,25,0.009257749219913675 1727509472,1727509500,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 233 n_permutations 27 update_interval 50 n_consecutive_deviations 1,233,27,50,1,0.1992998249562391,0,None,i7186,25,0.007138881494567189 1727509472,1727509501,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 234 n_permutations 27 update_interval 50 n_consecutive_deviations 1,234,27,50,1,0.2063015753938484,0,None,i7186,26,0.007141129544681253 1727509492,1727509521,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 130 n_permutations 46 update_interval 79 n_consecutive_deviations 5,130,46,79,5,0.24631157789447367,0,None,i7186,26,0.013186629990831039 1727509502,1727509534,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 344 n_permutations 50 update_interval 96 n_consecutive_deviations 4,344,50,96,4,0.2583145786446611,0,None,i7186,28,0.016678082564119293 1727509533,1727509562,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 234 n_permutations 27 update_interval 50 n_consecutive_deviations 1,234,27,50,1,0.2063015753938484,0,None,i7186,25,0.007141129544681253 1727509912,1727509942,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 26 update_interval 50 n_consecutive_deviations 1,238,26,50,1,0.202050512628157,0,None,i7186,26,0.007997453908931779 1727509932,1727509964,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 280 n_permutations 21 update_interval 186 n_consecutive_deviations 5,280,21,186,5,0.28232058014503625,0,None,i7186,28,0.01997721652635381 1727509952,1727509982,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 199 n_permutations 28 update_interval 65 n_consecutive_deviations 2,199,28,65,2,0.2325581395348837,0,None,i7186,26,0.00998420336791515 1727509972,1727510001,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 26 update_interval 50 n_consecutive_deviations 1,238,26,50,1,0.2063015753938484,0,None,i7186,25,0.00792016185864648 1727509985,1727510013,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 26 update_interval 50 n_consecutive_deviations 1,238,26,50,1,0.2063015753938484,0,None,i7186,25,0.00792016185864648 1727509993,1727510022,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 26 update_interval 50 n_consecutive_deviations 1,238,26,50,1,0.2063015753938484,0,None,i7186,26,0.00792016185864648 1727510012,1727510041,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 240 n_permutations 26 update_interval 50 n_consecutive_deviations 1,240,26,50,1,0.21230307576894225,0,None,i7186,25,0.007407024169835562 1727510032,1727510061,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 237 n_permutations 26 update_interval 50 n_consecutive_deviations 1,237,26,50,1,0.20255063765941483,0,None,i7186,25,0.008136293332592408 1727510052,1727510083,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 421 n_permutations 37 update_interval 88 n_consecutive_deviations 3,421,37,88,3,0.24381095273818454,0,None,i7186,27,0.013727569823490356 1727510073,1727510101,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 26 update_interval 50 n_consecutive_deviations 1,238,26,50,1,0.202050512628157,0,None,i7186,25,0.007997453908931779 1727510093,1727510121,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 25 update_interval 50 n_consecutive_deviations 1,242,25,50,1,0.20855213803450867,0,None,i7186,25,0.007345056603133834 1727510106,1727510135,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 237 n_permutations 26 update_interval 50 n_consecutive_deviations 1,237,26,50,1,0.2028007001750438,0,None,i7186,26,0.007983814135352018 1727510113,1727510141,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 26 update_interval 50 n_consecutive_deviations 1,238,26,50,1,0.202050512628157,0,None,i7186,25,0.007854642231986569 1727510133,1727510165,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 538 n_permutations 12 update_interval 120 n_consecutive_deviations 2,538,12,120,2,0.2675668917229307,0,None,i7186,29,0.015597649412353089 1727510534,1727510563,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 237 n_permutations 26 update_interval 50 n_consecutive_deviations 1,237,26,50,1,0.2083020755188797,0,None,i7186,25,0.0080297852240838 1727510554,1727510582,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 26 update_interval 50 n_consecutive_deviations 1,242,26,50,1,0.20855213803450867,0,None,i7186,25,0.007345056603133834 1727510574,1727510607,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 476 n_permutations 45 update_interval 65 n_consecutive_deviations 3,476,45,65,3,0.2315578894723681,0,None,i7186,28,0.012069193769030493 1727510588,1727510616,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 40 update_interval 50 n_consecutive_deviations 2,100,40,50,2,0.23905976494123526,0,None,i7186,24,0.006714178544636159 1727510614,1727510644,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 239 n_permutations 26 update_interval 50 n_consecutive_deviations 1,239,26,50,1,0.20955238809702426,0,None,i7186,25,0.00772068017004251 1727510618,1727510648,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 219 n_permutations 27 update_interval 84 n_consecutive_deviations 2,219,27,84,2,0.24981245311327827,0,None,i7186,26,0.011881758318367472 1727510648,1727510677,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 241 n_permutations 26 update_interval 50 n_consecutive_deviations 1,241,26,50,1,0.22280570142535638,0,None,i7186,25,0.007484013860608008 1727510654,1727510683,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 26 update_interval 50 n_consecutive_deviations 1,238,26,50,1,0.2063015753938484,0,None,i7186,25,0.00792016185864648 1727510674,1727510703,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 241 n_permutations 26 update_interval 50 n_consecutive_deviations 1,241,26,50,1,0.22280570142535638,0,None,i7186,25,0.007484013860608008 1727510695,1727510724,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 141 n_permutations 23 update_interval 59 n_consecutive_deviations 1,141,23,59,1,0.22480620155038755,0,None,i7186,25,0.007191453035672712 1727510708,1727510737,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 240 n_permutations 26 update_interval 50 n_consecutive_deviations 1,240,26,50,1,0.21230307576894225,0,None,i7186,25,0.007407024169835562 1727510735,1727510764,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 240 n_permutations 26 update_interval 50 n_consecutive_deviations 1,240,26,50,1,0.21230307576894225,0,None,i7186,26,0.007407024169835562 1727510739,1727510769,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 127 n_permutations 32 update_interval 126 n_consecutive_deviations 4,127,32,126,4,0.25531382845711426,0,None,i7186,27,0.018409364245823363 1727510769,1727510798,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 240 n_permutations 26 update_interval 50 n_consecutive_deviations 1,240,26,50,1,0.21230307576894225,0,None,i7186,25,0.007407024169835562 1727511256,1727511289,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 144 n_permutations 14 update_interval 132 n_consecutive_deviations 4,144,14,132,4,0.2848212053013254,0,None,i7186,29,0.022318079519879967 1727511281,1727511310,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 34 update_interval 58 n_consecutive_deviations 1,216,34,58,1,0.21080270067516882,0,None,i7186,25,0.008798117896821143 1727511296,1727511325,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 27 update_interval 50 n_consecutive_deviations 1,238,27,50,1,0.2063015753938484,0,None,i7186,25,0.00792016185864648 1727511311,1727511343,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 385 n_permutations 41 update_interval 114 n_consecutive_deviations 4,385,41,114,4,0.2468117029257314,0,None,i7186,28,0.01795903521334879 1727511336,1727511365,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 237 n_permutations 27 update_interval 50 n_consecutive_deviations 1,237,27,50,1,0.2028007001750438,0,None,i7186,26,0.007983814135352018 1727511356,1727511384,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 183 n_permutations 50 update_interval 50 n_consecutive_deviations 1,183,50,50,1,0.2190547636909227,0,None,i7186,25,0.0066071205301325335 1727511372,1727511400,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 1,100,50,50,1,0.22430607651912982,0,None,i7186,25,0.0054234337805230525 1727511396,1727511425,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 237 n_permutations 27 update_interval 50 n_consecutive_deviations 1,237,27,50,1,0.2028007001750438,0,None,i7186,25,0.007983814135352018 1727511396,1727511425,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 101 n_permutations 27 update_interval 50 n_consecutive_deviations 4,101,27,50,4,0.22430607651912982,0,None,i7186,25,0.009280097802228333 1727511416,1727511445,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 32 update_interval 50 n_consecutive_deviations 3,100,32,50,3,0.2218054513628407,0,None,i7186,25,0.007779722708454891 1727511432,1727511461,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 239 n_permutations 27 update_interval 50 n_consecutive_deviations 1,239,27,50,1,0.20780195048762196,0,None,i7186,25,0.007751937984496123 1727511456,1727511486,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 27 update_interval 50 n_consecutive_deviations 1,238,27,50,1,0.2063015753938484,0,None,i7186,26,0.00792016185864648 1727511476,1727511506,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 39 update_interval 50 n_consecutive_deviations 3,100,39,50,3,0.2270567641910478,0,None,i7186,26,0.0075427947896064915 1727511493,1727511521,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 253 n_permutations 25 update_interval 50 n_consecutive_deviations 1,253,25,50,1,0.2118029507376844,0,None,i7186,25,0.007545746085644218 1727511997,1727512028,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 306 n_permutations 13 update_interval 100 n_consecutive_deviations 1,306,13,100,1,0.24631157789447367,0,None,i7186,27,0.0123624656164041 1727512017,1727512045,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 235 n_permutations 27 update_interval 50 n_consecutive_deviations 1,235,27,50,1,0.20680170042510626,0,None,i7186,25,0.007132930773677026 1727512036,1727512064,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 235 n_permutations 27 update_interval 50 n_consecutive_deviations 1,235,27,50,1,0.20680170042510626,0,None,i7186,25,0.007132930773677026 1727512057,1727512085,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 249 n_permutations 26 update_interval 50 n_consecutive_deviations 1,249,26,50,1,0.22555638909727427,0,None,i7186,25,0.007434894437895189 1727512066,1727512096,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 500 n_permutations 39 update_interval 56 n_consecutive_deviations 2,500,39,56,2,0.24981245311327827,0,None,i7186,27,0.00956336645136894 1727512077,1727512106,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 27 update_interval 50 n_consecutive_deviations 1,236,27,50,1,0.20980245061265312,0,None,i7186,25,0.007716214767977709 1727512096,1727512129,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 536 n_permutations 47 update_interval 92 n_consecutive_deviations 4,536,47,92,4,0.27506876719179796,0,None,i7186,29,0.019307458443558256 1727512118,1727512147,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 27 update_interval 50 n_consecutive_deviations 1,236,27,50,1,0.2038009502375594,0,None,i7186,25,0.007686132059330622 1727512137,1727512166,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 238 n_permutations 27 update_interval 50 n_consecutive_deviations 1,238,27,50,1,0.20180045011252812,0,None,i7186,25,0.008002000500125032 1727512156,1727512185,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 50 n_consecutive_deviations 1,100,10,50,1,0.22130532633158284,0,None,i7186,25,0.005924016215321437 1727512177,1727512205,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 235 n_permutations 27 update_interval 50 n_consecutive_deviations 1,235,27,50,1,0.20680170042510626,0,None,i7186,24,0.007132930773677026 1727512197,1727512227,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 27 update_interval 50 n_consecutive_deviations 1,236,27,50,1,0.20980245061265312,0,None,i7186,26,0.007716214767977709 1727512217,1727512246,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 404 n_permutations 31 update_interval 64 n_consecutive_deviations 1,404,31,64,1,0.22780695173793453,0,None,i7186,26,0.010100085997109032 1727512238,1727512267,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 27 update_interval 50 n_consecutive_deviations 1,236,27,50,1,0.20980245061265312,0,None,i7186,25,0.007716214767977709 1727512718,1727512747,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 28 update_interval 50 n_consecutive_deviations 1,242,28,50,1,0.20855213803450867,0,None,i7186,25,0.007345056603133834 1727512729,1727512757,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 241 n_permutations 28 update_interval 50 n_consecutive_deviations 1,241,28,50,1,0.22280570142535638,0,None,i7186,25,0.007484013860608008 1727512758,1727512788,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 243 n_permutations 28 update_interval 50 n_consecutive_deviations 1,243,28,50,1,0.1992998249562391,0,None,i7186,25,0.007765099169529223 1727512778,1727512806,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 28 update_interval 50 n_consecutive_deviations 1,242,28,50,1,0.20855213803450867,0,None,i7186,25,0.007345056603133834 1727512789,1727512817,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 241 n_permutations 28 update_interval 50 n_consecutive_deviations 1,241,28,50,1,0.22280570142535638,0,None,i7186,25,0.007484013860608008 1727512798,1727512829,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 280 n_permutations 27 update_interval 80 n_consecutive_deviations 1,280,27,80,1,0.26531632908227054,0,None,i7186,27,0.00965626021890088 1727512818,1727512846,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 50 n_consecutive_deviations 1,100,50,50,1,0.22430607651912982,0,None,i7186,24,0.0054234337805230525 1727512850,1727512878,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 28 update_interval 50 n_consecutive_deviations 1,242,28,50,1,0.20855213803450867,0,None,i7186,25,0.007345056603133834 1727512878,1727512907,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 243 n_permutations 28 update_interval 50 n_consecutive_deviations 1,243,28,50,1,0.1992998249562391,0,None,i7186,25,0.007765099169529223 1727512899,1727512932,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 501 n_permutations 23 update_interval 156 n_consecutive_deviations 1,501,23,156,1,0.2650662665666417,0,None,i7186,30,0.017129282320580145 1727512910,1727512938,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 243 n_permutations 28 update_interval 50 n_consecutive_deviations 1,243,28,50,1,0.1992998249562391,0,None,i7186,25,0.007765099169529223 1727512918,1727512947,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 28 update_interval 50 n_consecutive_deviations 1,242,28,50,1,0.20855213803450867,0,None,i7186,24,0.007345056603133834 1727512939,1727512969,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 348 n_permutations 10 update_interval 50 n_consecutive_deviations 1,348,10,50,1,0.2200550137534384,0,None,i7186,26,0.007400973050280113 1727512958,1727512987,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 242 n_permutations 28 update_interval 50 n_consecutive_deviations 1,242,28,50,1,0.20855213803450867,0,None,i7186,25,0.007345056603133834 1727513782,1727513812,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 446 n_permutations 10 update_interval 50 n_consecutive_deviations 1,446,10,50,1,0.21430357589397353,0,None,i7186,26,0.00838444905343983 1727513801,1727513829,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 214 n_permutations 25 update_interval 50 n_consecutive_deviations 1,214,25,50,1,0.2083020755188797,0,None,i7186,24,0.006882673049214685 1727513822,1727513852,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 445 n_permutations 10 update_interval 50 n_consecutive_deviations 1,445,10,50,1,0.21605401350337583,0,None,i7186,26,0.008690948247265897 1727513842,1727513871,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 506 n_permutations 10 update_interval 50 n_consecutive_deviations 1,506,10,50,1,0.22280570142535638,0,None,i7186,26,0.008217740709687225 1727513862,1727513892,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 441 n_permutations 10 update_interval 50 n_consecutive_deviations 1,441,10,50,1,0.22680670167541883,0,None,i7186,27,0.008139289724391882 1727513881,1727513911,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 414 n_permutations 38 update_interval 52 n_consecutive_deviations 1,414,38,52,1,0.22505626406601653,0,None,i7186,26,0.008337084271067767 1727513902,1727513932,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 414 n_permutations 38 update_interval 52 n_consecutive_deviations 1,414,38,52,1,0.22505626406601653,0,None,i7186,27,0.008337084271067767 1727513922,1727513951,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 446 n_permutations 16 update_interval 50 n_consecutive_deviations 1,446,16,50,1,0.21430357589397353,0,None,i7186,26,0.00838444905343983 1727513942,1727513973,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 525 n_permutations 10 update_interval 50 n_consecutive_deviations 1,525,10,50,1,0.2325581395348837,0,None,i7186,27,0.008026516433029826 1727513962,1727513992,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 283 n_permutations 33 update_interval 91 n_consecutive_deviations 5,283,33,91,5,0.26631657914478624,0,None,i7186,27,0.015649745769775774 1727513982,1727514012,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 441 n_permutations 10 update_interval 50 n_consecutive_deviations 1,441,10,50,1,0.22680670167541883,0,None,i7186,27,0.008139289724391882 1727514002,1727514032,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 499 n_permutations 10 update_interval 64 n_consecutive_deviations 1,499,10,64,1,0.2378094523630908,0,None,i7186,27,0.009397698261774745 1727514022,1727514053,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 484 n_permutations 26 update_interval 50 n_consecutive_deviations 1,484,26,50,1,0.22330582645661412,0,None,i7186,27,0.008050089445438283 1727514042,1727514072,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 442 n_permutations 10 update_interval 60 n_consecutive_deviations 1,442,10,60,1,0.23030757689422354,0,None,i7186,27,0.00914673112722625 1727514062,1727514092,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 419 n_permutations 16 update_interval 50 n_consecutive_deviations 1,419,16,50,1,0.22905726431607898,0,None,i7186,27,0.008095161045163252 1727514599,1727514628,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20805201300325082,0,None,i7186,25,0.0067790385096274065 1727514623,1727514653,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22680670167541883,0,None,i7186,25,0.006104467293293912 1727514643,1727514672,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 231 n_permutations 27 update_interval 50 n_consecutive_deviations 1,231,27,50,1,0.21505376344086025,0,None,i7186,26,0.006567026371977609 1727514659,1727514688,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 128 n_permutations 44 update_interval 61 n_consecutive_deviations 4,128,44,61,4,0.2395598899724931,0,None,i7186,25,0.010588173359129255 1727514683,1727514712,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20680170042510626,0,None,i7186,26,0.006798574643660915 1727514689,1727514718,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22405601400350084,0,None,i7186,25,0.00605586179153484 1727514720,1727514749,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 107 n_permutations 42 update_interval 51 n_consecutive_deviations 3,107,42,51,3,0.2055513878469617,0,None,i7186,25,0.008556060583773395 1727514743,1727514771,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 228 n_permutations 27 update_interval 50 n_consecutive_deviations 1,228,27,50,1,0.19904976244061012,0,None,i7186,25,0.0069196986746686675 1727514763,1727514792,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20955238809702426,0,None,i7186,25,0.006651662915728932 1727514780,1727514808,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20680170042510626,0,None,i7186,25,0.006798574643660915 1727514803,1727514834,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 468 n_permutations 49 update_interval 62 n_consecutive_deviations 2,468,49,62,2,0.22955738934733683,0,None,i7186,27,0.010573156109540205 1727514810,1727514838,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20680170042510626,0,None,i7186,25,0.006798574643660915 1727514840,1727514870,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20680170042510626,0,None,i7186,26,0.006798574643660915 1727514863,1727514891,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20805201300325082,0,None,i7186,25,0.0067790385096274065 1727515404,1727515433,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 27 update_interval 50 n_consecutive_deviations 1,226,27,50,1,0.19454863715928983,0,None,i7186,25,0.0066770423949270895 1727515444,1727515472,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 27 update_interval 50 n_consecutive_deviations 1,226,27,50,1,0.19454863715928983,0,None,i7186,25,0.0066770423949270895 1727515444,1727515474,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 227 n_permutations 27 update_interval 50 n_consecutive_deviations 1,227,27,50,1,0.19704926231557884,0,None,i7186,26,0.007061289131806762 1727515464,1727515495,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 36 update_interval 149 n_consecutive_deviations 2,100,36,149,2,0.2658164541135284,0,None,i7186,27,0.015043760940235058 1727515484,1727515519,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 1000 n_permutations 46 update_interval 60 n_consecutive_deviations 4,1000,46,60,4,0.2915728932233058,0,None,i7186,32,0.012975466088744407 1727515524,1727515556,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 329 n_permutations 31 update_interval 109 n_consecutive_deviations 2,329,31,109,2,0.26256564141035255,0,None,i7186,28,0.014049808748483418 1727515534,1727515562,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 101 n_permutations 50 update_interval 50 n_consecutive_deviations 1,101,50,50,1,0.2225556389097274,0,None,i7186,24,0.005666957279860506 1727515564,1727515593,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 27 update_interval 50 n_consecutive_deviations 1,226,27,50,1,0.19454863715928983,0,None,i7186,25,0.0066770423949270895 1727515564,1727515596,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 234 n_permutations 49 update_interval 52 n_consecutive_deviations 1,234,49,52,1,0.20180045011252812,0,None,i7186,28,0.006985873452490107 1727515595,1727515623,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 226 n_permutations 27 update_interval 50 n_consecutive_deviations 1,226,27,50,1,0.19454863715928983,0,None,i7186,24,0.0066770423949270895 1727515624,1727515654,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 235 n_permutations 50 update_interval 69 n_consecutive_deviations 2,235,50,69,2,0.21830457614403598,0,None,i7186,26,0.010861689781419714 1727515644,1727515674,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 41 update_interval 73 n_consecutive_deviations 2,236,41,73,2,0.21430357589397353,0,None,i7186,27,0.011252813203300824 1727515655,1727515683,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 227 n_permutations 27 update_interval 50 n_consecutive_deviations 1,227,27,50,1,0.1955488872218054,0,None,i7186,25,0.0068671013907322985 1727515684,1727515713,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 227 n_permutations 27 update_interval 50 n_consecutive_deviations 1,227,27,50,1,0.1955488872218054,0,None,i7186,25,0.0068671013907322985 1727516259,1727516288,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 215 n_permutations 25 update_interval 50 n_consecutive_deviations 1,215,25,50,1,0.21480370092523127,0,None,i7186,26,0.006888818978938283 1727516285,1727516312,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 26 update_interval 50 n_consecutive_deviations 1,100,26,50,1,0.2198049512378094,0,None,i7186,24,0.005704128734886424 1727516305,1727516334,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 25 update_interval 50 n_consecutive_deviations 1,216,25,50,1,0.20805201300325082,0,None,i7186,25,0.006997717171228291 1727516319,1727516351,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 628 n_permutations 25 update_interval 63 n_consecutive_deviations 2,628,25,63,2,0.24306076519129782,0,None,i7186,28,0.010779721957516405 1727516345,1727516374,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 25 update_interval 50 n_consecutive_deviations 1,216,25,50,1,0.20780195048762196,0,None,i7186,26,0.007001750437609401 1727516365,1727516394,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 100 n_consecutive_deviations 3,100,50,100,3,0.24456114028507125,0,None,i7186,26,0.013244977911144453 1727516385,1727516414,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 25 update_interval 50 n_consecutive_deviations 1,216,25,50,1,0.20780195048762196,0,None,i7186,26,0.007001750437609401 1727516405,1727516433,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 215 n_permutations 25 update_interval 50 n_consecutive_deviations 1,215,25,50,1,0.21430357589397353,0,None,i7186,25,0.006896885511700505 1727516440,1727516469,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 189 n_permutations 17 update_interval 50 n_consecutive_deviations 1,189,17,50,1,0.22930732683170796,0,None,i7186,25,0.00665488952883382 1727516465,1727516493,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 260 n_permutations 10 update_interval 50 n_consecutive_deviations 2,260,10,50,2,0.2173043260815204,0,None,i7186,25,0.008325610814468323 1727516470,1727516499,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 105 n_permutations 10 update_interval 90 n_consecutive_deviations 2,105,10,90,2,0.23580895223805953,0,None,i7186,25,0.011944162511216038 1727516501,1727516531,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 13 update_interval 69 n_consecutive_deviations 5,100,13,69,5,0.2513128282070518,0,None,i7186,26,0.012599924174592034 1727516525,1727516553,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 216 n_permutations 25 update_interval 50 n_consecutive_deviations 1,216,25,50,1,0.20780195048762196,0,None,i7186,25,0.007001750437609401 1727516545,1727516574,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 215 n_permutations 25 update_interval 50 n_consecutive_deviations 1,215,25,50,1,0.21430357589397353,0,None,i7186,25,0.006896885511700505 1727516561,1727516590,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 46 update_interval 99 n_consecutive_deviations 3,100,46,99,3,0.25731432858214554,0,None,i7186,26,0.013261936173698597 1727517211,1727517242,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 115 n_permutations 36 update_interval 82 n_consecutive_deviations 2,115,36,82,2,0.22680670167541883,0,None,i7186,25,0.01012448234009722 1727517231,1727517261,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 261 n_permutations 30 update_interval 107 n_consecutive_deviations 3,261,30,107,3,0.24931232808202053,0,None,i7186,26,0.014021362483478012 1727517251,1727517281,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 79 n_consecutive_deviations 1,100,50,79,1,0.2388097024256064,0,None,i7186,26,0.008576612238165924 1727517271,1727517300,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 105 n_permutations 35 update_interval 80 n_consecutive_deviations 2,105,35,80,2,0.23105776444111026,0,None,i7186,25,0.010534684953289604 1727517291,1727517322,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 270 n_permutations 27 update_interval 106 n_consecutive_deviations 3,270,27,106,3,0.2628157039259815,0,None,i7186,27,0.015163790947736932 1727517316,1727517347,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 267 n_permutations 28 update_interval 105 n_consecutive_deviations 3,267,28,105,3,0.2613153288322081,0,None,i7186,27,0.01522380595148787 1727517346,1727517375,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 28 update_interval 50 n_consecutive_deviations 1,236,28,50,1,0.20980245061265312,0,None,i7186,25,0.007716214767977709 1727517371,1727517400,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 28 update_interval 50 n_consecutive_deviations 1,236,28,50,1,0.2038009502375594,0,None,i7186,25,0.007823384417532954 1727517391,1727517423,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 118 n_permutations 45 update_interval 102 n_consecutive_deviations 3,118,45,102,3,0.25256314078519626,0,None,i7186,28,0.012559591510780921 1727517407,1727517435,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 234 n_permutations 28 update_interval 50 n_consecutive_deviations 1,234,28,50,1,0.2063015753938484,0,None,i7186,25,0.007141129544681253 1727517431,1727517463,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 148 n_permutations 46 update_interval 98 n_consecutive_deviations 3,148,46,98,3,0.2620655163790948,0,None,i7186,28,0.01460942158616577 1727517451,1727517480,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 293 n_permutations 48 update_interval 50 n_consecutive_deviations 2,293,48,50,2,0.2010502625656414,0,None,i7186,25,0.007734389737785324 1727517471,1727517501,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 28 update_interval 50 n_consecutive_deviations 1,236,28,50,1,0.2038009502375594,0,None,i7186,25,0.007686132059330622 1727517491,1727517519,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 236 n_permutations 28 update_interval 50 n_consecutive_deviations 1,236,28,50,1,0.2038009502375594,0,None,i7186,24,0.007823384417532954 1727518414,1727518443,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 157 n_permutations 46 update_interval 60 n_consecutive_deviations 4,157,46,60,4,0.24256064016003998,0,None,i7186,25,0.010793238850253104 1727518454,1727518483,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22405601400350084,0,None,i7186,25,0.00605586179153484 1727518463,1727518492,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 174 n_permutations 27 update_interval 51 n_consecutive_deviations 1,174,27,51,1,0.21605401350337583,0,None,i7186,25,0.00709760773526715 1727518494,1727518523,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,25,0.006141241192651103 1727518514,1727518542,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,25,0.006141241192651103 1727518524,1727518553,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 108 n_permutations 31 update_interval 58 n_consecutive_deviations 2,108,31,58,2,0.22905726431607898,0,None,i7186,25,0.008257064266066517 1727518554,1727518583,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,25,0.006141241192651103 1727518574,1727518602,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22430607651912982,0,None,i7186,24,0.006141241192651103 1727518594,1727518623,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 231 n_permutations 27 update_interval 50 n_consecutive_deviations 1,231,27,50,1,0.21155288822205554,0,None,i7186,25,0.006620885990728451 1727518614,1727518643,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22405601400350084,0,None,i7186,25,0.00605586179153484 1727518645,1727518674,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 260 n_permutations 26 update_interval 50 n_consecutive_deviations 3,260,26,50,3,0.2360590147536884,0,None,i7186,26,0.00882285788838514 1727518674,1727518703,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 10 update_interval 68 n_consecutive_deviations 5,100,10,68,5,0.2450612653163291,0,None,i7186,25,0.011672035655972815 1727518695,1727518724,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 229 n_permutations 27 update_interval 50 n_consecutive_deviations 1,229,27,50,1,0.20805201300325082,0,None,i7186,26,0.0067790385096274065 1727518714,1727518742,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22405601400350084,0,None,i7186,24,0.00605586179153484 1727518735,1727518763,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 230 n_permutations 27 update_interval 50 n_consecutive_deviations 1,230,27,50,1,0.22680670167541883,0,None,i7186,25,0.006104467293293912 1727519702,1727519738,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 193 n_permutations 29 update_interval 114 n_consecutive_deviations 3,193,29,114,3,0.24906226556639155,0,None,i7186,28,0.017080357045783187 1727519729,1727519757,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 235 n_permutations 27 update_interval 50 n_consecutive_deviations 1,235,27,50,1,0.20680170042510626,0,None,i7186,25,0.007132930773677026 1727519749,1727519781,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 193 n_permutations 29 update_interval 114 n_consecutive_deviations 3,193,29,114,3,0.24706176544136038,0,None,i7186,28,0.01716733531208889 1727519769,1727519797,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 100 n_permutations 50 update_interval 71 n_consecutive_deviations 1,100,50,71,1,0.2505626406601651,0,None,i7186,25,0.0071154152174407225 1727519793,1727519822,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 234 n_permutations 27 update_interval 50 n_consecutive_deviations 1,234,27,50,1,0.2063015753938484,0,None,i7186,25,0.007141129544681253 1727519823,1727519851,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 232 n_permutations 26 update_interval 50 n_consecutive_deviations 1,232,26,50,1,0.20055013753438355,0,None,i7186,24,0.00668727788007608
timestamp,ram_usage_mb,cpu_usage_percent 1727501100,475.75,49.8 1727501100,475.75,50.0 1727501100,475.97265625,49.7 1727501100,475.97265625,55.6 1727501100,475.97265625,39.4 1727501100,475.97265625,50.0 1727501100,475.97265625,55.6 1727501146,480.82421875,49.8 1727501146,480.82421875,54.3 1727501146,480.82421875,49.2 1727501146,480.82421875,55.6 1727501147,480.82421875,49.7 1727501147,480.82421875,53.3 1727501147,480.82421875,48.1 1727501147,480.82421875,37.5 1727501149,480.828125,49.8 1727501149,480.828125,54.5 1727501149,480.828125,48.1 1727501149,480.828125,40.6 1727501150,480.828125,49.7 1727501150,480.828125,54.3 1727501150,480.828125,47.7 1727501150,480.828125,58.7 1727501151,480.828125,49.7 1727501151,480.828125,38.2 1727501151,480.828125,52.5 1727501151,480.828125,39.4 1727501154,484.109375,49.9 1727501154,484.109375,38.2 1727501154,484.109375,53.8 1727501154,484.109375,37.1 1727501158,484.109375,49.9 1727501158,484.109375,54.2 1727501158,484.109375,47.7 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This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
submitit INFO (2024-09-28 07:25:16,130) - Starting with JobEnvironment(job_id=2274141, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-28 07:25:16,132) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ImageBasedDriftDetector_OutdoorObjects/0/single_runs/2274141/2274141_submitted.pkl
Parameters: {"n_samples": 249, "n_permutations": 31, "update_interval": 230, "n_consecutive_deviations": 1}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 249 n_permutations 31 update_interval 230 n_consecutive_deviations 1
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: 1727501018.0
Last modification: 1724759185.0
Hostname: i7186
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: main_omniopt.py
UID: 2063769
GID: 200270
Status-Change-Time: 1727277242.0
Size: 1729 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727501019.0
Last modification: 1727277242.0
Hostname: i7186
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ImageBasedDriftDetector n_samples 249 n_permutations 31 update_interval 230 n_consecutive_deviations 1
stdout:
RESULT: 0.3143285821455364
OO-Info: runtime: 33
OO-Info: lpd: 0.021838793031591232
Result: 0.3143285821455364
EXIT_CODE: 0
submitit INFO (2024-09-28 07:25:55,229) - Job completed successfully
submitit INFO (2024-09-28 07:25:55,233) - 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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[
1727502620,
538.48046875,
51.4
],
[
1727502620,
538.48046875,
38.7
],
[
1727502853,
540.24609375,
50.2
],
[
1727502853,
540.24609375,
54.3
],
[
1727502853,
540.24609375,
50.6
],
[
1727502853,
540.24609375,
39.4
],
[
1727503143,
544.8359375,
50.2
],
[
1727503143,
544.8359375,
53.2
],
[
1727503143,
544.8359375,
50
],
[
1727503143,
544.8359375,
57.8
],
[
1727503457,
553.62109375,
50.2
],
[
1727503457,
553.62109375,
55.6
],
[
1727503457,
553.62109375,
48.6
],
[
1727503457,
553.62109375,
55.3
],
[
1727503837,
549.03125,
50.2
],
[
1727503837,
549.03125,
55.3
],
[
1727503837,
549.03125,
48.9
],
[
1727503837,
549.03125,
57.8
],
[
1727504247,
564.171875,
50.2
],
[
1727504247,
564.171875,
55.6
],
[
1727504247,
564.171875,
47.6
],
[
1727504247,
564.171875,
55.6
],
[
1727504576,
567.57421875,
50.2
],
[
1727504576,
567.57421875,
46.3
],
[
1727504576,
567.57421875,
49.8
],
[
1727504576,
567.57421875,
53.7
],
[
1727505024,
453.2890625,
50.2
],
[
1727505024,
453.2890625,
46.9
],
[
1727505024,
453.2890625,
50.4
],
[
1727505024,
453.2890625,
57.8
],
[
1727505550,
462.609375,
50.2
],
[
1727505550,
462.609375,
38.2
],
[
1727505550,
462.609375,
50.4
],
[
1727505550,
462.609375,
56.8
],
[
1727506241,
467.7265625,
50.3
],
[
1727506241,
467.7265625,
50
],
[
1727506241,
467.7265625,
50.6
],
[
1727506241,
467.7265625,
40.6
],
[
1727506675,
466.37890625,
50.2
],
[
1727506675,
466.37890625,
52.4
],
[
1727506675,
466.37890625,
50
],
[
1727506675,
466.37890625,
55.6
],
[
1727507319,
477.09765625,
50.2
],
[
1727507319,
477.09765625,
38.2
],
[
1727507319,
477.09765625,
50
],
[
1727507319,
477.09765625,
57.8
],
[
1727507872,
429.55859375,
50.2
],
[
1727507872,
429.55859375,
54.3
],
[
1727507872,
429.55859375,
48.6
],
[
1727507872,
429.55859375,
52.4
],
[
1727508408,
437.63671875,
50.2
],
[
1727508408,
437.63671875,
52.3
],
[
1727508408,
437.63671875,
50.2
],
[
1727508408,
437.63671875,
44.1
],
[
1727508966,
444.6171875,
50.2
],
[
1727508966,
444.6171875,
36.1
],
[
1727508966,
444.6171875,
51.3
],
[
1727508966,
444.6171875,
41.9
],
[
1727509521,
441.0078125,
50.2
],
[
1727509521,
441.0078125,
54.3
],
[
1727509521,
441.0078125,
48.3
],
[
1727509521,
441.0078125,
55.6
],
[
1727510131,
447.29296875,
50.2
],
[
1727510131,
447.29296875,
39.4
],
[
1727510131,
447.29296875,
50.4
],
[
1727510131,
447.29296875,
56.8
],
[
1727510757,
461.29296875,
50.2
],
[
1727510757,
461.29296875,
48.7
],
[
1727510757,
461.29296875,
49.2
],
[
1727510757,
461.29296875,
56.8
],
[
1727511493,
457.1328125,
50.2
],
[
1727511493,
457.1328125,
51.2
],
[
1727511493,
457.1328125,
49.3
],
[
1727511493,
457.1328125,
58.7
],
[
1727512236,
456.42578125,
50.2
],
[
1727512236,
456.42578125,
55.6
],
[
1727512236,
456.42578125,
48.8
],
[
1727512236,
456.42578125,
58.7
],
[
1727512962,
462.6953125,
50.2
],
[
1727512962,
462.6953125,
51
],
[
1727512962,
462.6953125,
49.8
],
[
1727512962,
462.6953125,
56.5
],
[
1727514062,
475.1796875,
50.2
],
[
1727514062,
475.1796875,
38.2
],
[
1727514062,
475.1796875,
50.8
],
[
1727514062,
475.1796875,
40.6
],
[
1727514856,
486.19140625,
50.2
],
[
1727514856,
486.19140625,
55.3
],
[
1727514856,
486.19140625,
50.2
],
[
1727514856,
486.19140625,
38.7
],
[
1727515674,
501.7578125,
50.2
],
[
1727515674,
501.7578125,
56.2
],
[
1727515674,
501.7578125,
50.2
],
[
1727515674,
501.7578125,
42.9
],
[
1727516563,
485.72265625,
50.2
],
[
1727516563,
485.72265625,
46.2
],
[
1727516563,
485.72265625,
50.6
],
[
1727516563,
485.72265625,
44.4
],
[
1727517497,
499.53125,
50.2
],
[
1727517497,
499.53125,
53.1
],
[
1727517497,
499.53125,
50.3
],
[
1727517497,
499.53125,
40.6
],
[
1727518736,
483.0234375,
50.2
],
[
1727518736,
483.0234375,
42.9
],
[
1727518736,
483.0234375,
49.9
],
[
1727518736,
483.0234375,
56.8
],
[
1727519833,
527.46875,
50.3
],
[
1727519833,
527.46875,
55.3
],
[
1727519857,
527.51953125,
49.7
],
[
1727519857,
527.51953125,
55.3
]
];
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)
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showlegend: false
};
let subDiv = document.createElement("div");
plotDiv.appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
$("#plotResultPairs").data("loaded", "true");
}
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downArrow.classList.add("scroll-arrow");
downArrow.classList.add("invert_in_dark_mode");
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checkScrollPosition();
if (typeof apply_theme_based_on_system_preferences === 'function') {
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function plotGPUUsage() {
if ($("#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;
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var div = document.createElement('div');
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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));
$("#plotJobStatusDistribution").data("loaded", "true");
}
function _colorize_table_entries_by_generation_method () {
document.querySelectorAll('[data-column-id="generation_method"]').forEach(el => {
let color = el.textContent.includes("Manual") ? "green" :
el.textContent.includes("Sobol") ? "orange" :
el.textContent.includes("SAASBO") ? "pink" :
el.textContent.includes("Uniform") ? "lightblue" :
el.textContent.includes("Legacy_GPEI") ? "Sienna" :
el.textContent.includes("BO_MIXED") ? "Aqua" :
el.textContent.includes("RANDOMFOREST") ? "DarkSeaGreen" :
el.textContent.includes("EXTERNAL_GENERATOR") ? "Purple" :
el.textContent.includes("BoTorch") ? "yellow" : "";
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document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
el.textContent.includes("RUNNING") ? "orange" :
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el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
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function _colorize_table_entries_by_results() {
result_names.forEach((name, index) => {
let minMax = result_min_max[index];
let selector_query = `[data-column-id="${name}"]`;
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el.classList.add("invert_in_dark_mode");
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});
}
function _colorize_table_entries_by_generation_node_or_hostname() {
["hostname", "generation_node"].forEach(element => {
let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
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colorMap[value] = `hsl(${hue}, 70%, 60%)`;
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let value = el.textContent.trim();
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el.style.backgroundColor = colorMap[value];
el.classList.add("invert_in_dark_mode");
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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);
}
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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_permutations</th><th>update_interval</th><th>n_consecutive_dev…</th><th>result </th></tr></thead><tbody><tr><td> 226</td><td>32</td><td>50</td><td>1</td><td>0.193798 </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_permutations</td><td>range</td><td>10</td><td>50</td><td></td><td>int </td></tr><tr><td> update_interval</td><td>range</td><td>50</td><td>250</td><td></td><td>int </td></tr><tr><td> n_consecutive_deviations</td><td>range</td><td>1</td><td>5</td><td></td><td>int </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>497</td>
<td>13</td>
<td>510</td>
</tr>
</tbody>
</table>
<h1> Results</h1>
<div id='tab_results_csv_table'></div>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,result,n_samples,n_permutations,update_interval,n_consecutive_deviations
0,0_0,COMPLETED,Sobol,0.314328582145536383762873811065,249,31,230,1
1,1_0,COMPLETED,Sobol,0.272818204551137810653926862869,276,27,149,4
2,2_0,COMPLETED,Sobol,0.300325081270317539861025579739,915,30,103,1
3,3_0,COMPLETED,Sobol,0.311577894473618388637703446875,827,46,184,3
4,4_0,COMPLETED,Sobol,0.286571642910727675257476221304,135,30,239,2
5,5_0,COMPLETED,Sobol,0.307576894223555941110248568293,519,37,156,5
6,6_0,COMPLETED,Sobol,0.246561640410102533849112660391,460,32,72,3
7,7_0,COMPLETED,Sobol,0.308577144286071525236536672310,662,44,250,1
8,8_0,COMPLETED,Sobol,0.291572893223305817933521666419,438,11,186,2
9,9_0,COMPLETED,Sobol,0.290322580645161254508934689511,499,36,160,4
10,10_0,COMPLETED,Sobol,0.311077769442360541063408163609,871,50,138,1
11,11_0,COMPLETED,Sobol,0.288572143035758954532354891853,699,37,209,5
12,12_0,COMPLETED,Sobol,0.326081520380094969091544498951,830,29,205,3
13,13_0,COMPLETED,Sobol,0.299824956239059803309032758989,573,31,219,1
14,14_0,COMPLETED,Sobol,0.209552388097024255841915874043,245,31,54,2
15,15_0,COMPLETED,Sobol,0.324831207801950516689259984560,958,14,228,3
16,16_0,COMPLETED,Sobol,0.283570892723180811856309446739,261,27,202,2
17,17_0,COMPLETED,Sobol,0.292823205801450381358108643326,396,44,240,3
18,18_0,COMPLETED,Sobol,0.264816204051012804576714643190,380,33,166,2
19,19_0,COMPLETED,Sobol,0.261065266316579114302953712468,338,13,87,5
20,20_0,COMPLETED,BoTorch,0.208052013003250824141332486761,164,26,50,2
21,21_0,COMPLETED,BoTorch,0.229057264316078978971802371234,209,40,50,2
22,22_0,COMPLETED,BoTorch,0.212553138284571119243082648609,304,26,50,2
23,23_0,COMPLETED,BoTorch,0.225056264066016531444347492652,164,31,50,3
24,24_0,COMPLETED,BoTorch,0.218054513628407109493423376989,282,32,50,1
25,25_0,COMPLETED,BoTorch,0.233058264566141537521559712332,186,32,80,2
26,26_0,COMPLETED,BoTorch,0.235808952238059532646730076522,100,32,50,2
27,27_0,COMPLETED,BoTorch,0.225306326581645399720343903027,208,18,51,2
28,28_0,COMPLETED,BoTorch,0.222305576394098536319177128462,307,39,50,2
29,29_0,COMPLETED,BoTorch,0.240310077519379827748480238370,129,26,83,2
30,30_0,COMPLETED,BoTorch,0.218804701175293825343715070630,161,41,50,3
31,31_0,COMPLETED,BoTorch,0.209052263065766408267620590777,126,18,50,3
32,32_0,COMPLETED,BoTorch,0.210552638159539839968203978060,204,22,50,1
33,33_0,COMPLETED,BoTorch,0.248312078019504833825692458049,100,42,61,2
34,34_0,COMPLETED,BoTorch,0.215803950987746961942548296065,234,40,71,1
35,35_0,COMPLETED,BoTorch,0.253563390847711955800036776054,285,36,72,2
36,36_0,COMPLETED,BoTorch,0.214053513378344550943666035892,141,30,50,1
37,37_0,COMPLETED,BoTorch,0.217554388597149261919128093723,322,17,50,1
38,38_0,COMPLETED,BoTorch,0.251812953238309544801154515881,100,36,82,1
39,39_0,COMPLETED,BoTorch,0.240810202550637675322775521636,229,25,73,2
40,40_0,COMPLETED,BoTorch,0.202050512628156986316696475114,213,27,50,2
41,41_0,COMPLETED,BoTorch,0.239809952488121980174184955104,133,19,78,2
42,42_0,COMPLETED,BoTorch,0.205051262815703960740165712195,143,12,50,3
43,43_0,COMPLETED,BoTorch,0.214553638409602398517961319158,103,11,50,1
44,44_0,COMPLETED,BoTorch,0.226056514128532115570635596669,100,21,50,1
45,45_0,COMPLETED,BoTorch,0.204051012753188265591575145663,202,10,50,1
46,46_0,COMPLETED,BoTorch,0.231307826956739237544979914674,100,14,50,5
47,47_0,COMPLETED,BoTorch,0.222055513878469668043180718087,290,10,50,3
48,48_0,COMPLETED,BoTorch,0.222055513878469668043180718087,100,26,50,5
49,49_0,COMPLETED,BoTorch,0.211052763190797687542499261326,246,20,50,3
50,50_0,COMPLETED,BoTorch,0.209552388097024255841915874043,303,29,50,1
51,51_0,COMPLETED,BoTorch,0.234308577144286100946146689239,100,10,50,4
52,52_0,COMPLETED,BoTorch,0.229557389347336826546097654500,419,50,50,1
53,53_0,COMPLETED,BoTorch,0.200550137534383554616113087832,244,28,50,1
54,54_0,COMPLETED,BoTorch,0.234558639659914969222143099614,100,10,50,2
55,55_0,COMPLETED,BoTorch,0.214553638409602398517961319158,112,24,50,3
56,56_0,COMPLETED,BoTorch,0.243810952738184538723942296201,408,15,50,3
57,57_0,COMPLETED,BoTorch,0.226056514128532115570635596669,271,38,50,5
58,58_0,COMPLETED,BoTorch,0.233058264566141537521559712332,197,10,50,3
59,59_0,COMPLETED,BoTorch,0.214553638409602398517961319158,195,10,50,2
60,60_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,10,50,1
61,61_0,COMPLETED,BoTorch,0.201050262565641402190408371098,245,19,50,1
62,62_0,COMPLETED,BoTorch,0.233058264566141537521559712332,365,50,50,5
63,63_0,COMPLETED,BoTorch,0.197549387346836691214946313266,147,10,50,2
64,64_0,COMPLETED,BoTorch,0.207301825456364108291040793119,234,16,50,2
65,65_0,COMPLETED,BoTorch,0.220305076269067257044298457913,343,10,50,1
66,66_0,COMPLETED,BoTorch,0.234808702175543837498139509989,201,50,50,5
67,67_0,COMPLETED,BoTorch,0.220055013753438388768302047538,504,10,50,1
68,68_0,COMPLETED,BoTorch,0.226306576644161094868934469559,194,22,50,4
69,69_0,COMPLETED,BoTorch,0.204051012753188265591575145663,233,33,50,4
70,70_0,COMPLETED,BoTorch,0.245311327831957970424525683484,230,36,50,5
71,71_0,COMPLETED,BoTorch,0.208302075518879692417328897136,242,21,50,1
72,72_0,COMPLETED,BoTorch,0.213553388347086814391673215141,236,27,50,4
73,73_0,COMPLETED,BoTorch,0.234558639659914969222143099614,278,38,50,5
74,74_0,COMPLETED,BoTorch,0.219304826206551672918010353897,100,50,50,5
75,75_0,COMPLETED,BoTorch,0.267066766691672952127589724114,668,50,50,5
76,76_0,COMPLETED,BoTorch,0.228807201800450110695805960859,220,19,50,1
77,77_0,COMPLETED,BoTorch,0.226556639159789963144930879935,159,45,50,5
78,78_0,COMPLETED,BoTorch,0.219804951237809409470003174647,535,12,50,1
79,79_0,COMPLETED,BoTorch,0.262815703925981525301835972641,689,37,50,3
80,80_0,COMPLETED,BoTorch,0.242560640160039975299355319294,362,44,50,5
81,74_0,COMPLETED,BoTorch,0.218054513628407109493423376989,100,50,50,5
82,82_0,COMPLETED,BoTorch,0.205301325331332829016162122571,257,18,50,1
83,83_0,COMPLETED,BoTorch,0.233058264566141537521559712332,260,20,50,1
84,84_0,COMPLETED,BoTorch,0.243810952738184538723942296201,713,45,50,4
85,85_0,COMPLETED,BoTorch,0.219054763690922693619711481006,241,21,50,2
86,86_0,COMPLETED,BoTorch,0.255813953488372103350911856978,562,50,57,4
87,87_0,COMPLETED,BoTorch,0.247561890472618117975400764408,563,50,57,4
88,88_0,COMPLETED,BoTorch,0.274318579644911242354510250152,766,50,91,4
89,86_0,COMPLETED,BoTorch,0.255813953488372103350911856978,562,50,57,4
90,90_0,COMPLETED,BoTorch,0.243060765191297822873650602560,697,10,50,1
91,91_0,COMPLETED,BoTorch,0.235058764691172816796438382880,493,50,50,3
92,92_0,COMPLETED,BoTorch,0.251312828207051808249161695130,721,44,50,4
93,93_0,COMPLETED,BoTorch,0.206551637909477392440749099478,278,10,50,1
94,94_0,COMPLETED,BoTorch,0.214303575893973530241964908782,336,23,50,1
95,95_0,COMPLETED,BoTorch,0.232058014503625953395271608315,310,20,50,1
96,96_0,COMPLETED,BoTorch,0.224056014003500836295756926120,252,13,50,2
97,97_0,COMPLETED,BoTorch,0.222305576394098536319177128462,319,23,50,1
98,98_0,COMPLETED,BoTorch,0.203050762690672681465287041647,127,50,50,1
99,99_0,COMPLETED,BoTorch,0.220305076269067257044298457913,273,50,50,1
100,100_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,34,50,4
101,101_0,COMPLETED,BoTorch,0.217804451112778241217426966614,265,50,50,3
102,102_0,COMPLETED,BoTorch,0.210802700675168819266502850951,123,50,50,1
103,103_0,COMPLETED,BoTorch,0.219804951237809409470003174647,221,42,50,3
104,104_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,50,50,4
105,105_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,36,50,4
106,106_0,COMPLETED,BoTorch,0.258564641160290098476082221168,100,50,104,5
107,107_0,COMPLETED,BoTorch,0.246811702925731402125109070766,103,42,83,5
108,108_0,COMPLETED,BoTorch,0.226056514128532115570635596669,222,50,50,1
109,109_0,COMPLETED,BoTorch,0.274568642160540110630506660527,100,38,118,5
110,110_0,COMPLETED,BoTorch,0.204551137784446113165870428929,258,10,50,1
111,111_0,COMPLETED,BoTorch,0.263315828957239261853828793392,100,50,88,5
112,112_0,COMPLETED,BoTorch,0.222305576394098536319177128462,203,22,50,3
113,100_0,COMPLETED,BoTorch,0.224056014003500836295756926120,100,34,50,4
114,114_0,COMPLETED,BoTorch,0.221555388847211820468885434821,100,27,50,4
115,115_0,COMPLETED,BoTorch,0.277319329832458105755677024717,322,10,149,4
116,116_0,COMPLETED,BoTorch,0.228307076769192263121510677593,100,50,50,3
117,117_0,COMPLETED,BoTorch,0.224306076519129815594055799011,100,50,50,1
118,118_0,COMPLETED,BoTorch,0.203800950237559397315578735288,235,30,50,1
119,119_0,COMPLETED,BoTorch,0.310327581895473825213116469968,1000,10,50,5
120,120_0,COMPLETED,BoTorch,0.239059764941235264323893261462,100,50,50,2
121,119_0,COMPLETED,BoTorch,0.310327581895473825213116469968,1000,10,50,5
122,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1
123,123_0,COMPLETED,BoTorch,0.219804951237809409470003174647,201,50,50,1
124,124_0,COMPLETED,BoTorch,0.220805201300325104618593741179,162,50,50,2
125,125_0,COMPLETED,BoTorch,0.211052763190797687542499261326,196,23,50,2
126,126_0,COMPLETED,BoTorch,0.242560640160039975299355319294,714,35,50,5
127,127_0,COMPLETED,BoTorch,0.197049262315578843640651030000,227,28,50,1
128,128_0,COMPLETED,BoTorch,0.223055763940985252169468822103,334,50,50,2
129,129_0,COMPLETED,BoTorch,0.241310327581895522897070804902,299,50,50,3
130,130_0,COMPLETED,BoTorch,0.225056264066016531444347492652,221,14,50,1
131,131_0,COMPLETED,BoTorch,0.224056014003500836295756926120,268,32,50,3
132,132_0,COMPLETED,BoTorch,0.293573393348337097208400336967,100,10,250,5
133,133_0,COMPLETED,BoTorch,0.207051762940735128992741920229,242,10,50,1
134,134_0,COMPLETED,BoTorch,0.305326331582895682537071024854,266,42,225,1
135,135_0,COMPLETED,BoTorch,0.300825206301575387435320863005,265,42,225,1
136,136_0,COMPLETED,BoTorch,0.278319579894973689881965128734,100,10,185,5
137,137_0,COMPLETED,BoTorch,0.240060015003750959472483827994,281,28,50,2
138,138_0,COMPLETED,BoTorch,0.294823705926481660632987313875,226,11,241,5
139,139_0,COMPLETED,BoTorch,0.221305326331582841170586561930,201,21,50,1
140,140_0,COMPLETED,BoTorch,0.268317079269817404529874238506,101,31,229,5
141,141_0,COMPLETED,BoTorch,0.294073518379594944782695620233,100,20,194,5
142,142_0,COMPLETED,BoTorch,0.352088022005501377620362291054,163,12,249,5
143,143_0,COMPLETED,BoTorch,0.282820705176294096006017753098,222,19,250,4
144,144_0,COMPLETED,BoTorch,0.195298824706176543664071232342,227,30,50,1
145,145_0,COMPLETED,BoTorch,0.221555388847211820468885434821,251,10,50,1
146,146_0,COMPLETED,BoTorch,0.208052013003250824141332486761,239,10,50,1
147,147_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,29,50,1
148,148_0,COMPLETED,BoTorch,0.277319329832458105755677024717,679,31,65,5
149,149_0,COMPLETED,BoTorch,0.242810702675668954597654192185,338,27,96,2
150,150_0,COMPLETED,BoTorch,0.200800200050012533914411960723,233,10,50,1
151,151_0,COMPLETED,BoTorch,0.199299824956239102213828573440,243,10,50,1
152,146_0,COMPLETED,BoTorch,0.207051762940735128992741920229,239,10,50,1
153,74_0,COMPLETED,BoTorch,0.218054513628407109493423376989,100,50,50,5
154,154_0,COMPLETED,BoTorch,0.193798449612403111963487845060,226,28,50,1
155,155_0,COMPLETED,BoTorch,0.238809702425606396047896851087,148,10,61,3
156,156_0,COMPLETED,BoTorch,0.223305826456614120445465232478,257,12,71,1
157,157_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,30,50,1
158,158_0,COMPLETED,BoTorch,0.236309077269317380221025359788,190,19,50,1
159,159_0,COMPLETED,BoTorch,0.226806701675418831420927290310,148,10,50,3
160,160_0,COMPLETED,BoTorch,0.229557389347336826546097654500,185,38,66,3
161,161_0,COMPLETED,BoTorch,0.200800200050012533914411960723,232,31,50,1
162,162_0,COMPLETED,BoTorch,0.261065266316579114302953712468,100,10,92,3
163,163_0,COMPLETED,BoTorch,0.206051512878219544866453816212,236,19,54,2
164,164_0,COMPLETED,BoTorch,0.240810202550637675322775521636,104,28,86,3
165,165_0,COMPLETED,BoTorch,0.239309827456864243622192134353,107,29,85,3
166,166_0,COMPLETED,BoTorch,0.247561890472618117975400764408,200,14,97,1
167,167_0,COMPLETED,BoTorch,0.234308577144286100946146689239,214,50,96,3
168,160_0,COMPLETED,BoTorch,0.229557389347336826546097654500,185,38,66,3
169,169_0,COMPLETED,BoTorch,0.216804201050262546068836400082,186,43,51,3
170,160_0,COMPLETED,BoTorch,0.229557389347336826546097654500,185,38,66,3
171,171_0,COMPLETED,BoTorch,0.251312828207051808249161695130,599,50,50,1
172,172_0,COMPLETED,BoTorch,0.281820455113778400857427186565,1000,50,50,1
173,173_0,COMPLETED,BoTorch,0.239059764941235264323893261462,298,38,65,3
174,174_0,COMPLETED,BoTorch,0.228307076769192263121510677593,245,23,77,2
175,175_0,COMPLETED,BoTorch,0.202300575143785965614995348005,238,32,50,1
176,176_0,COMPLETED,BoTorch,0.224556139034758683870052209386,270,10,50,1
177,177_0,COMPLETED,BoTorch,0.236809202300575116773018180538,489,38,64,1
178,178_0,COMPLETED,BoTorch,0.218804701175293825343715070630,356,10,50,2
179,179_0,COMPLETED,BoTorch,0.215053763440860246092256602424,240,31,50,1
180,180_0,COMPLETED,BoTorch,0.209302325581395387565919463668,466,18,50,2
181,181_0,COMPLETED,BoTorch,0.274568642160540110630506660527,690,47,59,1
182,182_0,COMPLETED,BoTorch,0.202050512628156986316696475114,238,31,50,1
183,183_0,COMPLETED,BoTorch,0.205301325331332829016162122571,108,28,51,1
184,184_0,COMPLETED,BoTorch,0.265566391597899520427006336831,700,42,64,1
185,185_0,COMPLETED,BoTorch,0.223305826456614120445465232478,222,10,50,2
186,186_0,COMPLETED,BoTorch,0.256064016004000971626908267353,872,23,50,1
187,187_0,COMPLETED,BoTorch,0.286321580395098806981479810929,677,42,58,5
188,188_0,COMPLETED,BoTorch,0.236809202300575116773018180538,289,10,50,3
189,189_0,COMPLETED,BoTorch,0.223805951487871968019760515745,358,16,65,2
190,190_0,COMPLETED,BoTorch,0.206801700425106260716745509853,239,31,50,1
191,191_0,COMPLETED,BoTorch,0.207551887971992976567037203495,223,32,50,1
192,192_0,COMPLETED,BoTorch,0.235058764691172816796438382880,280,10,50,1
193,193_0,COMPLETED,BoTorch,0.209302325581395387565919463668,219,32,50,1
194,194_0,COMPLETED,BoTorch,0.255563890972743235074915446603,791,25,55,1
195,191_0,COMPLETED,BoTorch,0.207551887971992976567037203495,223,32,50,1
196,196_0,COMPLETED,BoTorch,0.230307576894223542396389348141,263,10,50,1
197,197_0,COMPLETED,BoTorch,0.223305826456614120445465232478,221,32,50,1
198,198_0,COMPLETED,BoTorch,0.207551887971992976567037203495,223,33,50,1
199,199_0,COMPLETED,BoTorch,0.227556889222305547271218983951,222,33,50,1
200,193_0,COMPLETED,BoTorch,0.209302325581395387565919463668,219,32,50,1
201,201_0,COMPLETED,BoTorch,0.210802700675168819266502850951,268,10,50,1
202,202_0,COMPLETED,BoTorch,0.193798449612403111963487845060,226,32,50,1
203,203_0,COMPLETED,BoTorch,0.244811202800700122850230400218,320,10,74,1
204,204_0,COMPLETED,BoTorch,0.235558889722430553348431203631,353,10,86,2
205,205_0,COMPLETED,BoTorch,0.259064766191547835028075041919,336,25,134,2
206,206_0,COMPLETED,BoTorch,0.203550887721930529039582324913,217,32,50,1
207,207_0,COMPLETED,BoTorch,0.269067266816704231402468394663,232,39,135,3
208,208_0,COMPLETED,BoTorch,0.210802700675168819266502850951,216,25,50,1
209,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1
210,210_0,COMPLETED,BoTorch,0.208302075518879692417328897136,213,32,50,1
211,211_0,COMPLETED,BoTorch,0.242060515128782238747362498543,258,35,108,2
212,212_0,COMPLETED,BoTorch,0.221305326331582841170586561930,100,10,50,1
213,213_0,COMPLETED,BoTorch,0.231307826956739237544979914674,100,19,50,5
214,214_0,COMPLETED,BoTorch,0.238809702425606396047896851087,157,46,50,4
215,215_0,COMPLETED,BoTorch,0.283820955238809680132305857114,844,31,105,2
216,216_0,COMPLETED,BoTorch,0.211052763190797687542499261326,216,32,50,1
217,217_0,COMPLETED,BoTorch,0.205301325331332829016162122571,218,32,50,1
218,218_0,COMPLETED,BoTorch,0.215053763440860246092256602424,240,26,50,1
219,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1
220,217_0,COMPLETED,BoTorch,0.205301325331332829016162122571,218,32,50,1
221,221_0,COMPLETED,BoTorch,0.233308327081770405797556122707,100,10,50,5
222,222_0,COMPLETED,BoTorch,0.202800700175043813189290631271,237,28,50,1
223,223_0,COMPLETED,BoTorch,0.264566141535383825278415770299,178,33,106,4
224,216_0,COMPLETED,BoTorch,0.211052763190797687542499261326,216,32,50,1
225,225_0,COMPLETED,BoTorch,0.216554138534633677792839989706,215,32,50,1
226,225_0,COMPLETED,BoTorch,0.216554138534633677792839989706,215,32,50,1
227,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1
228,228_0,COMPLETED,BoTorch,0.246561640410102533849112660391,695,45,70,2
229,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1
230,230_0,COMPLETED,BoTorch,0.219804951237809409470003174647,212,33,50,1
231,212_0,COMPLETED,BoTorch,0.221305326331582841170586561930,100,10,50,1
232,232_0,COMPLETED,BoTorch,0.247311827956989249699404354033,246,50,114,1
233,122_0,COMPLETED,BoTorch,0.208302075518879692417328897136,214,32,50,1
234,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1
235,235_0,COMPLETED,BoTorch,0.235808952238059532646730076522,100,22,50,2
236,236_0,COMPLETED,BoTorch,0.207801950487621955865336076386,239,27,50,1
237,237_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,33,50,1
238,206_0,COMPLETED,BoTorch,0.203550887721930529039582324913,217,32,50,1
239,239_0,COMPLETED,BoTorch,0.210802700675168819266502850951,267,25,50,2
240,240_0,COMPLETED,BoTorch,0.238559639909977527771900440712,100,16,50,2
241,241_0,COMPLETED,BoTorch,0.225306326581645399720343903027,180,10,50,1
242,242_0,COMPLETED,BoTorch,0.244811202800700122850230400218,100,31,104,2
243,243_0,RUNNING,BoTorch,,210,27,50,1
244,244_0,COMPLETED,BoTorch,0.262565641410352546003537099750,503,41,126,1
245,245_0,COMPLETED,BoTorch,0.240060015003750959472483827994,352,10,57,3
246,246_0,COMPLETED,BoTorch,0.237059264816204096071317053429,100,28,50,2
247,247_0,RUNNING,BoTorch,,223,28,50,1
248,248_0,RUNNING,BoTorch,,233,27,50,1
249,249_0,COMPLETED,BoTorch,0.219554888722180541194006764272,191,24,50,3
250,225_0,COMPLETED,BoTorch,0.216554138534633677792839989706,215,32,50,1
251,251_0,COMPLETED,BoTorch,0.254313578394598671650328469696,713,50,63,5
252,122_0,COMPLETED,BoTorch,0.207301825456364108291040793119,214,32,50,1
253,253_0,COMPLETED,BoTorch,0.239809952488121980174184955104,321,25,50,5
254,216_0,COMPLETED,BoTorch,0.211052763190797687542499261326,216,32,50,1
255,255_0,COMPLETED,BoTorch,0.209052263065766408267620590777,219,33,50,1
256,256_0,COMPLETED,BoTorch,0.230307576894223542396389348141,220,33,50,1
257,257_0,COMPLETED,BoTorch,0.229807451862965694822094064875,263,29,50,1
258,256_0,COMPLETED,BoTorch,0.230307576894223542396389348141,220,33,50,1
259,259_0,COMPLETED,BoTorch,0.270817704426106531379048192321,768,17,105,1
260,256_0,COMPLETED,BoTorch,0.230307576894223542396389348141,220,33,50,1
261,261_0,COMPLETED,BoTorch,0.237559389847461832623309874180,100,47,73,4
262,256_0,COMPLETED,BoTorch,0.228807201800450110695805960859,220,33,50,1
263,256_0,COMPLETED,BoTorch,0.232558139534883689947264429065,220,33,50,1
264,264_0,COMPLETED,BoTorch,0.225306326581645399720343903027,221,33,50,1
265,265_0,COMPLETED,BoTorch,0.205051262815703960740165712195,218,33,50,1
266,266_0,COMPLETED,BoTorch,0.237059264816204096071317053429,279,50,57,2
267,267_0,COMPLETED,BoTorch,0.260815203800950246026957302092,164,35,94,3
268,268_0,COMPLETED,BoTorch,0.195798949737434391238366515609,227,29,50,1
269,269_0,COMPLETED,BoTorch,0.217554388597149261919128093723,275,10,50,2
270,270_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,30,50,1
271,147_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,29,50,1
272,272_0,COMPLETED,BoTorch,0.237559389847461832623309874180,281,43,50,1
273,273_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,29,50,1
274,274_0,COMPLETED,BoTorch,0.249562390597649397250279434957,695,35,55,1
275,275_0,COMPLETED,BoTorch,0.216054013503375830218544706440,267,25,50,1
276,276_0,COMPLETED,BoTorch,0.208052013003250824141332486761,229,29,50,1
277,277_0,COMPLETED,BoTorch,0.213303325831457835093374342250,176,50,50,2
278,278_0,COMPLETED,BoTorch,0.225556389097274267996340313402,453,50,58,2
279,147_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,29,50,1
280,268_0,COMPLETED,BoTorch,0.195798949737434391238366515609,227,29,50,1
281,281_0,COMPLETED,BoTorch,0.204051012753188265591575145663,258,25,50,1
282,266_0,COMPLETED,BoTorch,0.237059264816204096071317053429,279,50,57,2
283,283_0,COMPLETED,BoTorch,0.208552138034508671715627770027,224,28,50,1
284,284_0,COMPLETED,BoTorch,0.202300575143785965614995348005,225,28,50,1
285,283_0,COMPLETED,BoTorch,0.208552138034508671715627770027,224,28,50,1
286,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1
287,284_0,COMPLETED,BoTorch,0.203300825206301549741283452022,225,28,50,1
288,284_0,COMPLETED,BoTorch,0.203300825206301549741283452022,225,28,50,1
289,289_0,COMPLETED,BoTorch,0.246061515378844686274817377125,315,27,121,3
290,290_0,COMPLETED,BoTorch,0.208552138034508671715627770027,224,27,50,1
291,291_0,COMPLETED,BoTorch,0.222055513878469668043180718087,255,23,60,3
292,127_0,COMPLETED,BoTorch,0.195548887221805411940067642718,227,28,50,1
293,284_0,COMPLETED,BoTorch,0.203300825206301549741283452022,225,28,50,1
294,294_0,COMPLETED,BoTorch,0.283320830207551832558010573848,821,17,75,2
295,295_0,COMPLETED,BoTorch,0.224806201550387552146048619761,230,28,50,1
296,290_0,COMPLETED,BoTorch,0.208552138034508671715627770027,224,27,50,1
297,297_0,COMPLETED,BoTorch,0.209552388097024255841915874043,229,27,50,1
298,298_0,COMPLETED,BoTorch,0.261315328832208093601252585358,319,13,120,2
299,299_0,COMPLETED,BoTorch,0.199049762440610122915529700549,228,27,50,1
300,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1
301,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1
302,302_0,RUNNING,BoTorch,,126,44,61,4
303,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
304,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
305,305_0,COMPLETED,BoTorch,0.221055263815953972894590151554,318,15,87,1
306,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1
307,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
308,299_0,COMPLETED,BoTorch,0.199049762440610122915529700549,228,27,50,1
309,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
310,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1
311,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1
312,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
313,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1
314,314_0,COMPLETED,BoTorch,0.220055013753438388768302047538,146,31,74,2
315,315_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,27,50,1
316,316_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,27,50,1
317,317_0,COMPLETED,BoTorch,0.216054013503375830218544706440,231,27,50,1
318,318_0,COMPLETED,BoTorch,0.214053513378344550943666035892,237,21,50,3
319,319_0,COMPLETED,BoTorch,0.206801700425106260716745509853,248,25,50,1
320,320_0,COMPLETED,BoTorch,0.288572143035758954532354891853,903,40,66,4
321,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1
322,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1
323,315_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,27,50,1
324,315_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,27,50,1
325,325_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,27,50,1
326,326_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,27,50,1
327,327_0,COMPLETED,BoTorch,0.235558889722430553348431203631,188,43,94,2
328,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1
329,329_0,COMPLETED,BoTorch,0.269067266816704231402468394663,452,17,108,2
330,330_0,COMPLETED,BoTorch,0.200300075018754686340116677457,244,26,50,1
331,331_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,27,50,1
332,332_0,COMPLETED,BoTorch,0.216054013503375830218544706440,151,50,57,3
333,316_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,27,50,1
334,248_0,COMPLETED,BoTorch,0.199299824956239102213828573440,233,27,50,1
335,335_0,COMPLETED,BoTorch,0.246311577894473665573116250016,130,46,79,5
336,336_0,COMPLETED,BoTorch,0.258314578644661119177783348277,344,50,96,4
337,316_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,27,50,1
338,338_0,COMPLETED,BoTorch,0.202050512628156986316696475114,238,26,50,1
339,339_0,COMPLETED,BoTorch,0.282320580145036248431722469832,280,21,186,5
340,340_0,COMPLETED,BoTorch,0.232558139534883689947264429065,199,28,65,2
341,338_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,26,50,1
342,338_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,26,50,1
343,338_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,26,50,1
344,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1
345,345_0,COMPLETED,BoTorch,0.202550637659414833890991758381,237,26,50,1
346,346_0,COMPLETED,BoTorch,0.243810952738184538723942296201,421,37,88,3
347,338_0,COMPLETED,BoTorch,0.202050512628156986316696475114,238,26,50,1
348,348_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,25,50,1
349,345_0,COMPLETED,BoTorch,0.202800700175043813189290631271,237,26,50,1
350,338_0,COMPLETED,BoTorch,0.202050512628156986316696475114,238,26,50,1
351,351_0,COMPLETED,BoTorch,0.267566891722930688679582544864,538,12,120,2
352,345_0,COMPLETED,BoTorch,0.208302075518879692417328897136,237,26,50,1
353,353_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,26,50,1
354,354_0,COMPLETED,BoTorch,0.231557889472368105820976325049,476,45,65,3
355,355_0,COMPLETED,BoTorch,0.239059764941235264323893261462,100,40,50,2
356,356_0,COMPLETED,BoTorch,0.209552388097024255841915874043,239,26,50,1
357,357_0,COMPLETED,BoTorch,0.249812453113278265526275845332,219,27,84,2
358,358_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,26,50,1
359,338_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,26,50,1
360,358_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,26,50,1
361,361_0,COMPLETED,BoTorch,0.224806201550387552146048619761,141,23,59,1
362,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1
363,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1
364,364_0,COMPLETED,BoTorch,0.255313828457114255776616573712,127,32,126,4
365,218_0,COMPLETED,BoTorch,0.212303075768942250967086238234,240,26,50,1
366,366_0,COMPLETED,BoTorch,0.284821205301325375280896423646,144,14,132,4
367,367_0,COMPLETED,BoTorch,0.210802700675168819266502850951,216,34,58,1
368,326_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,27,50,1
369,369_0,COMPLETED,BoTorch,0.246811702925731402125109070766,385,41,114,4
370,370_0,COMPLETED,BoTorch,0.202800700175043813189290631271,237,27,50,1
371,371_0,COMPLETED,BoTorch,0.219054763690922693619711481006,183,50,50,1
372,117_0,COMPLETED,BoTorch,0.224306076519129815594055799011,100,50,50,1
373,373_0,COMPLETED,BoTorch,0.224306076519129815594055799011,101,27,50,4
374,370_0,COMPLETED,BoTorch,0.202800700175043813189290631271,237,27,50,1
375,375_0,COMPLETED,BoTorch,0.221805451362840688744881845196,100,32,50,3
376,236_0,COMPLETED,BoTorch,0.207801950487621955865336076386,239,27,50,1
377,326_0,COMPLETED,BoTorch,0.206301575393848413142450226587,238,27,50,1
378,378_0,COMPLETED,BoTorch,0.227056764191047810719226163201,100,39,50,3
379,379_0,COMPLETED,BoTorch,0.211802950737684403392790954967,253,25,50,1
380,380_0,COMPLETED,BoTorch,0.246311577894473665573116250016,306,13,100,1
381,381_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,27,50,1
382,381_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,27,50,1
383,383_0,COMPLETED,BoTorch,0.225556389097274267996340313402,249,26,50,1
384,384_0,COMPLETED,BoTorch,0.249812453113278265526275845332,500,39,56,2
385,325_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,27,50,1
386,386_0,COMPLETED,BoTorch,0.275068767191797958204801943793,536,47,92,4
387,325_0,COMPLETED,BoTorch,0.203800950237559397315578735288,236,27,50,1
388,326_0,COMPLETED,BoTorch,0.201800450112528118040700064739,238,27,50,1
389,212_0,COMPLETED,BoTorch,0.221305326331582841170586561930,100,10,50,1
390,381_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,27,50,1
391,325_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,27,50,1
392,392_0,COMPLETED,BoTorch,0.227806951737934526569517856842,404,31,64,1
393,325_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,27,50,1
394,394_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,28,50,1
395,395_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,28,50,1
396,396_0,COMPLETED,BoTorch,0.199299824956239102213828573440,243,28,50,1
397,394_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,28,50,1
398,395_0,COMPLETED,BoTorch,0.222805701425356383893472411728,241,28,50,1
399,399_0,COMPLETED,BoTorch,0.265316329082270541128707463940,280,27,80,1
400,117_0,COMPLETED,BoTorch,0.224306076519129815594055799011,100,50,50,1
401,394_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,28,50,1
402,396_0,COMPLETED,BoTorch,0.199299824956239102213828573440,243,28,50,1
403,403_0,COMPLETED,BoTorch,0.265066266566641672852711053565,501,23,156,1
404,396_0,COMPLETED,BoTorch,0.199299824956239102213828573440,243,28,50,1
405,394_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,28,50,1
406,406_0,COMPLETED,BoTorch,0.220055013753438388768302047538,348,10,50,1
407,394_0,COMPLETED,BoTorch,0.208552138034508671715627770027,242,28,50,1
408,408_0,COMPLETED,BoTorch,0.214303575893973530241964908782,446,10,50,1
409,409_0,COMPLETED,BoTorch,0.208302075518879692417328897136,214,25,50,1
410,410_0,COMPLETED,BoTorch,0.216054013503375830218544706440,445,10,50,1
411,411_0,COMPLETED,BoTorch,0.222805701425356383893472411728,506,10,50,1
412,412_0,COMPLETED,BoTorch,0.226806701675418831420927290310,441,10,50,1
413,413_0,COMPLETED,BoTorch,0.225056264066016531444347492652,414,38,52,1
414,413_0,COMPLETED,BoTorch,0.225056264066016531444347492652,414,38,52,1
415,415_0,COMPLETED,BoTorch,0.214303575893973530241964908782,446,16,50,1
416,416_0,COMPLETED,BoTorch,0.232558139534883689947264429065,525,10,50,1
417,417_0,COMPLETED,BoTorch,0.266316579144786236277298030473,283,33,91,5
418,412_0,COMPLETED,BoTorch,0.226806701675418831420927290310,441,10,50,1
419,419_0,COMPLETED,BoTorch,0.237809452363090811921608747070,499,10,64,1
420,420_0,COMPLETED,BoTorch,0.223305826456614120445465232478,484,26,50,1
421,421_0,COMPLETED,BoTorch,0.230307576894223542396389348141,442,10,60,1
422,422_0,COMPLETED,BoTorch,0.229057264316078978971802371234,419,16,50,1
423,297_0,COMPLETED,BoTorch,0.208052013003250824141332486761,229,27,50,1
424,303_0,COMPLETED,BoTorch,0.226806701675418831420927290310,230,27,50,1
425,317_0,COMPLETED,BoTorch,0.215053763440860246092256602424,231,27,50,1
426,426_0,COMPLETED,BoTorch,0.239559889972493111898188544728,128,44,61,4
427,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1
428,303_0,COMPLETED,BoTorch,0.224056014003500836295756926120,230,27,50,1
429,429_0,COMPLETED,BoTorch,0.205551387846961697292158532946,107,42,51,3
430,299_0,COMPLETED,BoTorch,0.199049762440610122915529700549,228,27,50,1
431,297_0,COMPLETED,BoTorch,0.209552388097024255841915874043,229,27,50,1
432,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1
433,433_0,COMPLETED,BoTorch,0.229557389347336826546097654500,468,49,62,2
434,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1
435,297_0,COMPLETED,BoTorch,0.206801700425106260716745509853,229,27,50,1
436,297_0,COMPLETED,BoTorch,0.208052013003250824141332486761,229,27,50,1
437,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1
438,438_0,COMPLETED,BoTorch,0.197049262315578843640651030000,227,27,50,1
439,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1
440,440_0,COMPLETED,BoTorch,0.265816454113528388703002747206,100,36,149,2
441,441_0,COMPLETED,BoTorch,0.291572893223305817933521666419,1000,46,60,4
442,442_0,COMPLETED,BoTorch,0.262565641410352546003537099750,329,31,109,2
443,443_0,COMPLETED,BoTorch,0.222555638909727404595173538837,101,50,50,1
444,444_0,COMPLETED,BoTorch,0.201800450112528118040700064739,234,49,52,1
445,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1
446,286_0,COMPLETED,BoTorch,0.194548637159289827813779538701,226,27,50,1
447,447_0,COMPLETED,BoTorch,0.218304576144035977769419787364,235,50,69,2
448,448_0,COMPLETED,BoTorch,0.214303575893973530241964908782,236,41,73,2
449,438_0,COMPLETED,BoTorch,0.195548887221805411940067642718,227,27,50,1
450,438_0,COMPLETED,BoTorch,0.195548887221805411940067642718,227,27,50,1
451,451_0,COMPLETED,BoTorch,0.214803700925231266793957729533,215,25,50,1
452,452_0,COMPLETED,BoTorch,0.219804951237809409470003174647,100,26,50,1
453,208_0,COMPLETED,BoTorch,0.208052013003250824141332486761,216,25,50,1
454,454_0,COMPLETED,BoTorch,0.243060765191297822873650602560,628,25,63,2
455,208_0,COMPLETED,BoTorch,0.207801950487621955865336076386,216,25,50,1
456,456_0,COMPLETED,BoTorch,0.244561140285071254574233989842,100,50,100,3
457,208_0,COMPLETED,BoTorch,0.207801950487621955865336076386,216,25,50,1
458,451_0,COMPLETED,BoTorch,0.214303575893973530241964908782,215,25,50,1
459,459_0,COMPLETED,BoTorch,0.229307326831707958270101244125,189,17,50,1
460,460_0,COMPLETED,BoTorch,0.217304326081520393643131683348,260,10,50,2
461,461_0,COMPLETED,BoTorch,0.235808952238059532646730076522,105,10,90,2
462,462_0,COMPLETED,BoTorch,0.251312828207051808249161695130,100,13,69,5
463,208_0,COMPLETED,BoTorch,0.207801950487621955865336076386,216,25,50,1
464,451_0,COMPLETED,BoTorch,0.214303575893973530241964908782,215,25,50,1
465,465_0,COMPLETED,BoTorch,0.257314328582145535051495244261,100,46,99,3
466,466_0,COMPLETED,BoTorch,0.226806701675418831420927290310,115,36,82,2
467,467_0,COMPLETED,BoTorch,0.249312328082020528974283024581,261,30,107,3
468,468_0,COMPLETED,BoTorch,0.238809702425606396047896851087,100,50,79,1
469,469_0,COMPLETED,BoTorch,0.231057764441110258246681041783,105,35,80,2
470,470_0,COMPLETED,BoTorch,0.262815703925981525301835972641,270,27,106,3
471,471_0,COMPLETED,BoTorch,0.261315328832208093601252585358,267,28,105,3
472,472_0,COMPLETED,BoTorch,0.209802450612653124117912284419,236,28,50,1
473,472_0,COMPLETED,BoTorch,0.203800950237559397315578735288,236,28,50,1
474,474_0,COMPLETED,BoTorch,0.252563140785196260651446209522,118,45,102,3
475,475_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,28,50,1
476,476_0,COMPLETED,BoTorch,0.262065516379094809451544279000,148,46,98,3
477,477_0,COMPLETED,BoTorch,0.201050262565641402190408371098,293,48,50,2
478,472_0,COMPLETED,BoTorch,0.203800950237559397315578735288,236,28,50,1
479,472_0,COMPLETED,BoTorch,0.203800950237559397315578735288,236,28,50,1
480,480_0,COMPLETED,BoTorch,0.242560640160039975299355319294,157,46,60,4
481,303_0,COMPLETED,BoTorch,0.224056014003500836295756926120,230,27,50,1
482,482_0,COMPLETED,BoTorch,0.216054013503375830218544706440,174,27,51,1
483,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
484,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
485,485_0,COMPLETED,BoTorch,0.229057264316078978971802371234,108,31,58,2
486,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
487,303_0,COMPLETED,BoTorch,0.224306076519129815594055799011,230,27,50,1
488,317_0,COMPLETED,BoTorch,0.211552888222055535116794544592,231,27,50,1
489,303_0,COMPLETED,BoTorch,0.224056014003500836295756926120,230,27,50,1
490,490_0,COMPLETED,BoTorch,0.236059014753688400922726486897,260,26,50,3
491,491_0,COMPLETED,BoTorch,0.245061265316329102148529273109,100,10,68,5
492,297_0,COMPLETED,BoTorch,0.208052013003250824141332486761,229,27,50,1
493,303_0,COMPLETED,BoTorch,0.224056014003500836295756926120,230,27,50,1
494,303_0,COMPLETED,BoTorch,0.226806701675418831420927290310,230,27,50,1
495,495_0,COMPLETED,BoTorch,0.249062265566391549675984151690,193,29,114,3
496,381_0,COMPLETED,BoTorch,0.206801700425106260716745509853,235,27,50,1
497,495_0,COMPLETED,BoTorch,0.247061765441360381423407943657,193,29,114,3
498,498_0,COMPLETED,BoTorch,0.250562640660165092398870001489,100,50,71,1
499,316_0,COMPLETED,BoTorch,0.206301575393848413142450226587,234,27,50,1
500,500_0,COMPLETED,BoTorch,0.200550137534383554616113087832,232,26,50,1
501,501_0,RUNNING,BoTorch,,169,27,87,2
502,502_0,RUNNING,BoTorch,,185,31,125,3
503,248_0,RUNNING,BoTorch,,233,27,50,1
504,504_0,RUNNING,BoTorch,,166,28,69,1
505,248_0,RUNNING,BoTorch,,233,27,50,1
506,506_0,RUNNING,BoTorch,,189,28,108,3
507,507_0,RUNNING,BoTorch,,158,27,50,3
508,501_0,RUNNING,BoTorch,,169,27,87,2
509,509_0,RUNNING,BoTorch,,162,26,73,2
</pre>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<script>
createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
<h1> CPU/RAM-Usage (main)</h1>
<div class='invert_in_dark_mode' id='mainWorkerCPURAM'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download »cpu_ram_usage.csv« as file</button>
<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727501100,475.75,49.8
1727501100,475.75,50.0
1727501100,475.97265625,49.7
1727501100,475.97265625,55.6
1727501100,475.97265625,39.4
1727501100,475.97265625,50.0
1727501100,475.97265625,55.6
1727501146,480.82421875,49.8
1727501146,480.82421875,54.3
1727501146,480.82421875,49.2
1727501146,480.82421875,55.6
1727501147,480.82421875,49.7
1727501147,480.82421875,53.3
1727501147,480.82421875,48.1
1727501147,480.82421875,37.5
1727501149,480.828125,49.8
1727501149,480.828125,54.5
1727501149,480.828125,48.1
1727501149,480.828125,40.6
1727501150,480.828125,49.7
1727501150,480.828125,54.3
1727501150,480.828125,47.7
1727501150,480.828125,58.7
1727501151,480.828125,49.7
1727501151,480.828125,38.2
1727501151,480.828125,52.5
1727501151,480.828125,39.4
1727501154,484.109375,49.9
1727501154,484.109375,38.2
1727501154,484.109375,53.8
1727501154,484.109375,37.1
1727501158,484.109375,49.9
1727501158,484.109375,54.2
1727501158,484.109375,47.7
1727501158,484.109375,56.5
1727501160,484.1796875,49.9
1727501160,484.1796875,38.2
1727501160,484.1796875,52.0
1727501160,484.1796875,44.1
1727501162,484.1796875,49.9
1727501162,484.1796875,55.3
1727501162,484.1796875,47.2
1727501162,484.1796875,57.8
1727501165,484.1796875,49.9
1727501165,484.1796875,56.3
1727501165,484.1796875,47.2
1727501165,484.1796875,55.6
1727501167,484.1796875,49.8
1727501167,484.1796875,55.3
1727501167,484.1796875,47.2
1727501167,484.1796875,47.4
1727501169,484.1796875,49.9
1727501169,484.1796875,57.4
1727501169,484.1796875,48.1
1727501169,484.1796875,55.6
1727501172,484.1875,49.9
1727501172,484.1875,54.3
1727501172,484.1875,46.7
1727501172,484.1875,57.8
1727501174,484.1875,49.9
1727501174,484.1875,56.2
1727501174,484.1875,46.8
1727501174,484.1875,56.8
1727501176,484.1875,49.9
1727501176,484.1875,55.3
1727501176,484.1875,47.2
1727501176,484.1875,55.6
1727501179,484.234375,49.8
1727501179,484.234375,38.2
1727501179,484.234375,52.5
1727501179,484.234375,37.5
1727501181,484.234375,49.9
1727501181,484.234375,37.5
1727501181,484.234375,52.5
1727501181,484.234375,40.6
1727501183,484.234375,49.9
1727501183,484.234375,40.0
1727501183,484.234375,52.5
1727501183,484.234375,47.2
1727501186,484.234375,49.9
1727501186,484.234375,54.3
1727501186,484.234375,45.9
1727501186,484.234375,57.4
1727501188,484.234375,49.8
1727501188,484.234375,54.3
1727501188,484.234375,51.7
1727501188,484.234375,37.5
1727501190,484.234375,49.9
1727501190,484.234375,36.4
1727501190,484.234375,52.5
1727501190,484.234375,42.4
1727501192,484.234375,49.9
1727501192,484.234375,51.2
1727501192,484.234375,51.6
1727501192,484.234375,39.4
1727501194,484.234375,49.9
1727501194,484.234375,47.5
1727501194,484.234375,52.0
1727501194,484.234375,41.9
1727501196,484.234375,49.8
1727501196,484.234375,54.2
1727501196,484.234375,48.1
1727501196,484.234375,54.5
1727501199,484.234375,49.9
1727501199,484.234375,52.3
1727501199,484.234375,52.1
1727501199,484.234375,37.5
1727501201,484.234375,49.9
1727501201,484.234375,55.3
1727501201,484.234375,50.0
1727501201,484.234375,45.7
1727501203,484.234375,49.9
1727501203,484.234375,43.6
1727501203,484.234375,50.0
1727501203,484.234375,55.6
1727501205,484.234375,49.9
1727501205,484.234375,54.3
1727501205,484.234375,47.7
1727501205,484.234375,57.8
1727501207,484.234375,49.8
1727501207,484.234375,45.0
1727501207,484.234375,52.1
1727501207,484.234375,37.5
1727501210,484.234375,49.9
1727501210,484.234375,54.2
1727501210,484.234375,45.5
1727501210,484.234375,57.4
1727501212,484.234375,49.8
1727501212,484.234375,55.3
1727501212,484.234375,47.8
1727501212,484.234375,52.4
1727501214,484.234375,49.9
1727501214,484.234375,56.5
1727501214,484.234375,46.8
1727501214,484.234375,55.6
1727501216,484.234375,49.9
1727501216,484.234375,36.4
1727501216,484.234375,50.8
1727501216,484.234375,56.8
1727501219,484.29296875,49.9
1727501219,484.29296875,41.2
1727501219,484.29296875,52.1
1727501219,484.29296875,54.5
1727501221,484.29296875,49.9
1727501221,484.29296875,40.0
1727501221,484.29296875,50.8
1727501221,484.29296875,57.8
1727501226,484.359375,49.9
1727501226,484.359375,55.3
1727501226,484.359375,50.4
1727501226,484.359375,37.5
1727501404,523.09375,50.2
1727501404,523.09375,46.3
1727501404,523.09375,50.7
1727501404,523.09375,39.4
1727501508,525.35546875,50.3
1727501508,525.35546875,56.5
1727501508,525.35546875,49.2
1727501508,525.35546875,57.8
1727501676,527.859375,50.2
1727501676,527.859375,56.5
1727501676,527.859375,47.8
1727501676,527.859375,56.8
1727501812,531.20703125,50.2
1727501812,531.20703125,37.5
1727501812,531.20703125,50.0
1727501812,531.20703125,55.6
1727502018,532.015625,50.3
1727502018,532.015625,43.2
1727502018,532.015625,51.4
1727502018,532.015625,38.7
1727502221,537.26171875,50.3
1727502221,537.26171875,55.3
1727502221,537.26171875,49.3
1727502221,537.26171875,45.9
1727502398,542.203125,50.2
1727502398,542.203125,54.3
1727502398,542.203125,48.4
1727502398,542.203125,53.2
1727502620,538.48046875,50.2
1727502620,538.48046875,50.0
1727502620,538.48046875,51.4
1727502620,538.48046875,38.7
1727502853,540.24609375,50.2
1727502853,540.24609375,54.3
1727502853,540.24609375,50.6
1727502853,540.24609375,39.4
1727503143,544.8359375,50.2
1727503143,544.8359375,53.2
1727503143,544.8359375,50.0
1727503143,544.8359375,57.8
1727503457,553.62109375,50.2
1727503457,553.62109375,55.6
1727503457,553.62109375,48.6
1727503457,553.62109375,55.3
1727503837,549.03125,50.2
1727503837,549.03125,55.3
1727503837,549.03125,48.9
1727503837,549.03125,57.8
1727504247,564.171875,50.2
1727504247,564.171875,55.6
1727504247,564.171875,47.6
1727504247,564.171875,55.6
1727504576,567.57421875,50.2
1727504576,567.57421875,46.3
1727504576,567.57421875,49.8
1727504576,567.57421875,53.7
1727505024,453.2890625,50.2
1727505024,453.2890625,46.9
1727505024,453.2890625,50.4
1727505024,453.2890625,57.8
1727505550,462.609375,50.2
1727505550,462.609375,38.2
1727505550,462.609375,50.4
1727505550,462.609375,56.8
1727506241,467.7265625,50.3
1727506241,467.7265625,50.0
1727506241,467.7265625,50.6
1727506241,467.7265625,40.6
1727506675,466.37890625,50.2
1727506675,466.37890625,52.4
1727506675,466.37890625,50.0
1727506675,466.37890625,55.6
1727507319,477.09765625,50.2
1727507319,477.09765625,38.2
1727507319,477.09765625,50.0
1727507319,477.09765625,57.8
1727507872,429.55859375,50.2
1727507872,429.55859375,54.3
1727507872,429.55859375,48.6
1727507872,429.55859375,52.4
1727508408,437.63671875,50.2
1727508408,437.63671875,52.3
1727508408,437.63671875,50.2
1727508408,437.63671875,44.1
1727508966,444.6171875,50.2
1727508966,444.6171875,36.1
1727508966,444.6171875,51.3
1727508966,444.6171875,41.9
1727509521,441.0078125,50.2
1727509521,441.0078125,54.3
1727509521,441.0078125,48.3
1727509521,441.0078125,55.6
1727510131,447.29296875,50.2
1727510131,447.29296875,39.4
1727510131,447.29296875,50.4
1727510131,447.29296875,56.8
1727510757,461.29296875,50.2
1727510757,461.29296875,48.7
1727510757,461.29296875,49.2
1727510757,461.29296875,56.8
1727511493,457.1328125,50.2
1727511493,457.1328125,51.2
1727511493,457.1328125,49.3
1727511493,457.1328125,58.7
1727512236,456.42578125,50.2
1727512236,456.42578125,55.6
1727512236,456.42578125,48.8
1727512236,456.42578125,58.7
1727512962,462.6953125,50.2
1727512962,462.6953125,51.0
1727512962,462.6953125,49.8
1727512962,462.6953125,56.5
1727514062,475.1796875,50.2
1727514062,475.1796875,38.2
1727514062,475.1796875,50.8
1727514062,475.1796875,40.6
1727514856,486.19140625,50.2
1727514856,486.19140625,55.3
1727514856,486.19140625,50.2
1727514856,486.19140625,38.7
1727515674,501.7578125,50.2
1727515674,501.7578125,56.2
1727515674,501.7578125,50.2
1727515674,501.7578125,42.9
1727516563,485.72265625,50.2
1727516563,485.72265625,46.2
1727516563,485.72265625,50.6
1727516563,485.72265625,44.4
1727517497,499.53125,50.2
1727517497,499.53125,53.1
1727517497,499.53125,50.3
1727517497,499.53125,40.6
1727518736,483.0234375,50.2
1727518736,483.0234375,42.9
1727518736,483.0234375,49.9
1727518736,483.0234375,56.8
1727519833,527.46875,50.3
1727519833,527.46875,55.3
1727519857,527.51953125,49.7
1727519857,527.51953125,55.3
</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>