OmniOpt2

Setting	Value
Model for non-random steps	BOTORCH_MODULAR
Max. nr. evaluations	500
Number random steps	20
Nr. of workers (parameter)	30
Main process memory (GB)	8
Worker memory (GB)	32

ACCURACY	recent_samples_size	n_samples	confidence	feature_proportion	n_clusters	RUNTIME
0.36183177570093455	2019	1000	0.001	0.2	4	26.830154180526733

Name	Type	Lower bound	Upper bound	Values	Type	Log Scale?
recent_samples_size	range	10	4000		int	No
n_samples	range	100	1000		int	No
confidence	choice			0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25
feature_proportion	range	0	0.2		float	No
n_clusters	range	1	4		int	No

Failed	Succeeded	Running	Total
269	239	2	510

name	min/max
ACCURACY	max
RUNTIME	min

start_time,end_time,run_time,program_string,recent_samples_size,n_samples,confidence,feature_proportion,n_clusters,ACCURACY,RUNTIME,exit_code,signal,hostname,OO_Info_runtime,OO_Info_peak_memory,OO_Info_mean_memory,OO_Info_lpd,OO_Info_portion_req_label,OO_Info_SLURM_JOB_ID
1742404107,1742404195,88,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2675 n_samples 927 confidence 0.005 feature_proportion 0.08795592188835144 n_clusters 3,2675,927,0.005,0.08795592188835144,3,0.5788909657320872,76.30770683288574,0,None,i7133,76.30770683288574,829.8203125,754.109375,-1,0,3297035
1742404137,1742404201,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 128 n_samples 322 confidence 0.01 feature_proportion 0.13066217862069607 n_clusters 3,128,322,0.01,0.13066217862069607,3,0.5477632398753894,51.339449644088745,0,None,i7082,51.339449644088745,775.15625,728.9599609375,-1,0,3297432
1742404135,1742404208,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 508 n_samples 872 confidence 0.25 feature_proportion 0.16545338928699493 n_clusters 2,508,872,0.25,0.16545338928699493,2,0.5484361370716511,56.91563415527344,0,None,i7118,56.91563415527344,807.3046875,741.2822265625,-1,0,3297313
1742404138,1742404215,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2202 n_samples 453 confidence 0.025 feature_proportion 0.06881796792149544 n_clusters 3,2202,453,0.025,0.06881796792149544,3,0.5521869158878505,60.33931088447571,0,None,i7096,60.33931088447571,781.35546875,732.2465277777778,-1,0,3297362
1742404138,1742404221,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 536 n_samples 720 confidence 0.005 feature_proportion 0.11229325532913209 n_clusters 3,536,720,0.005,0.11229325532913209,3,0.5866791277258567,69.1222071647644,0,None,i7018,69.1222071647644,836.734375,755.4869791666666,-1,0,3297593
1742404136,1742404227,91,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2329 n_samples 552 confidence 0.25 feature_proportion 0.03510399051010609 n_clusters 2,2329,552,0.25,0.03510399051010609,2,0.56401246105919,78.94550657272339,0,None,i7092,78.94550657272339,812.9921875,746.458203125,-1,0,3297385
1742404193,1742404244,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2118 n_samples 857 confidence 0.05 feature_proportion 0.10488082431256772 n_clusters 1,2118,857,0.05,0.10488082431256772,1,0.4580560747663551,37.70930814743042,0,None,i7088,37.70930814743042,711.90625,697.3600260416666,-1,0,3297894
1742404192,1742404249,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 197 n_samples 593 confidence 0.05 feature_proportion 0.09459988251328469 n_clusters 1,197,593,0.05,0.09459988251328469,1,0.5225295950155763,45.37185502052307,0,None,i7132,45.37185502052307,755.67578125,717.1953125,-1,0,3297837
1742404163,1742404258,95,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2894 n_samples 764 confidence 0.025 feature_proportion 0.17052264772355558 n_clusters 4,2894,764,0.025,0.17052264772355558,4,0.589619937694704,85.28411674499512,0,None,i7183,85.28411674499512,902.99609375,791.45703125,-1,0,3297644
1742404106,1742404264,158,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3515 n_samples 683 confidence 0.025 feature_proportion 0.14436043165624143 n_clusters 4,3515,683,0.025,0.14436043165624143,4,0.6105919003115264,139.963609457016,0,None,i7009,139.963609457016,974.25390625,829.6083984375,-1,0,3297224
1742404103,1742404265,162,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 908 n_samples 398 confidence 0.1 feature_proportion 0.19630399607121946 n_clusters 2,908,398,0.1,0.19630399607121946,2,0.6140560747663552,148.29546332359314,0,None,i7095,148.29546332359314,985.6953125,829.7725183823529,-1,0,3297088
1742404163,1742404271,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1103 n_samples 784 confidence 0.001 feature_proportion 0.13851049542427063 n_clusters 2,1103,784,0.001,0.13851049542427063,2,0.5936448598130841,97.3387541770935,0,None,i7091,97.3387541770935,921,801.3671875,-1,0,3297708
1742404138,1742404291,153,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1692 n_samples 997 confidence 0.05 feature_proportion 0.056155810505151754 n_clusters 4,1692,997,0.05,0.056155810505151754,4,0.6088847352024922,139.3249111175537,0,None,i7063,139.3249111175537,943.59765625,810.93994140625,-1,0,3297466
1742404106,1742404295,189,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1348 n_samples 625 confidence 0.01 feature_proportion 0.021565718203783037 n_clusters 1,1348,625,0.01,0.021565718203783037,1,0.6169968847352025,173.78937554359436,0,None,i7074,173.78937554359436,975.234375,838.222265625,-1,0,3297140
1742404194,1742404296,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 328 n_samples 467 confidence 0.001 feature_proportion 0.010885154083371163 n_clusters 4,328,467,0.001,0.010885154083371163,4,0.5788660436137072,88.9896628856659,0,None,i7063,88.9896628856659,837.984375,750.7858664772727,-1,0,3297926
1742404138,1742404299,161,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3484 n_samples 826 confidence 0.01 feature_proportion 0.17867283783853055 n_clusters 1,3484,826,0.01,0.17867283783853055,1,0.6088722741433021,144.49824905395508,0,None,i7043,144.49824905395508,952.34375,821.1236213235294,-1,0,3297531
1742404162,1742404308,146,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3800 n_samples 952 confidence 0.1 feature_proportion 0.014141078665852548 n_clusters 3,3800,952,0.1,0.014141078665852548,3,0.605993769470405,131.5684678554535,0,None,i7045,131.5684678554535,951.77734375,823.825927734375,-1,0,3297766
1742404193,1742404320,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2494 n_samples 281 confidence 0.005 feature_proportion 0.18967280983924867 n_clusters 4,2494,281,0.005,0.18967280983924867,4,0.6138816199376947,116.14065766334534,0,None,i7103,116.14065766334534,951.72265625,812.4196428571429,-1,0,3297864
1742404163,1742404334,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3338 n_samples 541 confidence 0.05 feature_proportion 0.04578436985611916 n_clusters 3,3338,541,0.05,0.04578436985611916,3,0.6096074766355141,156.718510389328,0,None,i7079,156.718510389328,1001.7890625,844.0891927083334,-1,0,3297730
1742404138,1742404344,206,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1221 n_samples 499 confidence 0.1 feature_proportion 0.08701784797012807 n_clusters 4,1221,499,0.1,0.08701784797012807,4,0.6142305295950156,191.9185266494751,0,None,i7022,191.9185266494751,979.66796875,830.6860795454545,-1,0,3297567
1742404206,1742404353,147,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3207 n_samples 640 confidence 0.001 feature_proportion 0.061237274482846264 n_clusters 2,3207,640,0.001,0.061237274482846264,2,0.6070404984423676,133.31421947479248,0,None,i7184,133.31421947479248,961.5703125,823.152099609375,-1,0,3297999
1742404192,1742404377,185,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1882 n_samples 695 confidence 0.1 feature_proportion 0.18530459105968478 n_clusters 3,1882,695,0.1,0.18530459105968478,3,0.6111526479750778,174.55925965309143,0,None,i7049,174.55925965309143,1033.11328125,856.8787109375,-1,0,3297946
1742404107,1742404432,325,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3105 n_samples 249 confidence 0.1 feature_proportion 0.11272933781147004 n_clusters 4,3105,249,0.1,0.11272933781147004,4,0.6216323987538941,306.627836227417,0,None,i7040,306.627836227417,1153.41796875,915.2593513257576,-1,0,3297177
1742404162,1742404437,275,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1513 n_samples 374 confidence 0.01 feature_proportion 0.12014234624803066 n_clusters 2,1513,374,0.01,0.12014234624803066,2,0.6124485981308411,259.4732012748718,0,None,i7011,259.4732012748718,1137.8359375,905.9561941964286,-1,0,3297804
1742404135,1742404461,326,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3887 n_samples 329 confidence 0.001 feature_proportion 0.15952342748641968 n_clusters 1,3887,329,0.001,0.15952342748641968,1,0.6193395638629283,309.6785490512848,0,None,i7051,309.6785490512848,1230.359375,935.7834990530303,-1,0,3297497
1742404138,1742404477,339,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2794 n_samples 156 confidence 0.05 feature_proportion 0.0034599401056766513 n_clusters 2,2794,156,0.05,0.0034599401056766513,2,0.6194641744548287,326.20347571372986,0,None,i7011,326.20347571372986,1200.23828125,937.5813616071429,-1,0,3297619
1742404162,1742404484,322,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 670 n_samples 111 confidence 0.25 feature_proportion 0.06373707838356495 n_clusters 1,670,111,0.25,0.06373707838356495,1,0.6281370716510903,307.5311357975006,0,None,i7128,307.5311357975006,1250.78125,963.8656486742424,-1,0,3297676
1742404223,1742404596,373,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1479 n_samples 234 confidence 0.025 feature_proportion 0.15445474646985533 n_clusters 3,1479,234,0.025,0.15445474646985533,3,0.6201495327102804,361.21822333335876,0,None,i7132,361.21822333335876,1253.33984375,974.2631209935897,-1,0,3298025
1742404137,1742404648,511,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1811 n_samples 192 confidence 0.001 feature_proportion 0.039921658113598824 n_clusters 1,1811,192,0.001,0.039921658113598824,1,0.6117258566978193,495.7098762989044,0,None,i7140,495.7098762989044,1404.58984375,1048.03515625,-1,0,3297269
1742404193,1742404685,492,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3678 n_samples 180 confidence 0.005 feature_proportion 0.0803744561970234 n_clusters 2,3678,180,0.005,0.0803744561970234,2,0.6218566978193146,479.50987815856934,0,None,i7023,479.50987815856934,1399.109375,1050.323203125,-1,0,3297973
1742405688,1742405701,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 277 n_samples 831 confidence 0.25 feature_proportion 0 n_clusters 3,277,831,0.25,0,3,None,None,1,None,i7119,3305993
1742405688,1742405701,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 178 n_samples 755 confidence 0.05 feature_proportion 0 n_clusters 3,178,755,0.05,0,3,None,None,1,None,i7090,3306034
1742405689,1742405702,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4000 n_samples 897 confidence 0.001 feature_proportion 0 n_clusters 1,4000,897,0.001,0,1,None,None,1,None,i7059,3306068
1742405689,1742405702,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3191 n_samples 738 confidence 0.005 feature_proportion 0 n_clusters 2,3191,738,0.005,0,2,None,None,1,None,i7082,3306045
1742405689,1742405702,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2271 n_samples 596 confidence 0.1 feature_proportion 0 n_clusters 4,2271,596,0.1,0,4,None,None,1,None,i7071,3306057
1742405689,1742405702,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1818 n_samples 860 confidence 0.025 feature_proportion 0 n_clusters 4,1818,860,0.025,0,4,None,None,1,None,i7087,3306038
1742405689,1742405702,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 874 confidence 0.005 feature_proportion 0 n_clusters 1,10,874,0.005,0,1,None,None,1,None,i7130,3305981
1742405689,1742405703,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3056 n_samples 871 confidence 0.05 feature_proportion 0 n_clusters 3,3056,871,0.05,0,3,None,None,1,None,i7140,3305968
1742405689,1742405703,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 317 confidence 0.05 feature_proportion 0 n_clusters 4,10,317,0.05,0,4,None,None,1,None,i7046,3306089
1742405690,1742405703,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1006 n_samples 891 confidence 0.25 feature_proportion 0 n_clusters 1,1006,891,0.25,0,1,None,None,1,None,i7078,3306051
1742405689,1742405721,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 308 confidence 0.001 feature_proportion 0.2 n_clusters 4,10,308,0.001,0.2,4,None,None,1,None,i7146,3305961
1742405689,1742405727,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 305 confidence 0.1 feature_proportion 0.0008520593597418887 n_clusters 4,10,305,0.1,0.0008520593597418887,4,0.4302429906542056,28.52893352508545,0,None,i7128,28.52893352508545,696.65625,692.23828125,-1,0,3305986
1742405689,1742405734,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 65 n_samples 567 confidence 0.25 feature_proportion 0.2 n_clusters 1,65,567,0.25,0.2,1,0.4577445482866044,31.43402338027954,0,None,i7111,31.43402338027954,710.69921875,696.8541666666666,-1,0,3306008
1742405690,1742405735,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 30 n_samples 307 confidence 0.1 feature_proportion 0.15996843214008613 n_clusters 4,30,307,0.1,0.15996843214008613,4,None,None,1,None,i7156,3305949
1742405690,1742405741,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 47 n_samples 586 confidence 0.05 feature_proportion 0.1891245215349471 n_clusters 3,47,586,0.05,0.1891245215349471,3,0.43981308411214953,34.79698085784912,0,None,i7133,34.79698085784912,704.1640625,694.9680989583334,-1,0,3305975
1742405690,1742405747,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 168 n_samples 462 confidence 0.001 feature_proportion 0.17177827076055077 n_clusters 1,168,462,0.001,0.17177827076055077,1,0.5403862928348909,46.270461320877075,0,None,i7096,46.270461320877075,767.10546875,722.1780133928571,-1,0,3306027
1742405691,1742405762,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2158 n_samples 451 confidence 0.01 feature_proportion 0.12478529161473705 n_clusters 1,2158,451,0.01,0.12478529161473705,1,0.545196261682243,54.00809407234192,0,None,i7040,54.00809407234192,763.48828125,721.45068359375,-1,0,3306093
1742405690,1742405767,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2486 n_samples 882 confidence 0.01 feature_proportion 0.027739278107548157 n_clusters 1,2486,882,0.01,0.027739278107548157,1,0.5510654205607477,61.05541777610779,0,None,i7176,61.05541777610779,795.00390625,737.9908854166666,-1,0,3305943
1742405690,1742405773,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 476 n_samples 569 confidence 0.01 feature_proportion 0.2 n_clusters 1,476,569,0.01,0.2,1,0.5857819314641745,71.92372250556946,0,None,i7153,71.92372250556946,858.51171875,772.267578125,-1,0,3305955
1742405689,1742405784,95,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2758 n_samples 736 confidence 0.01 feature_proportion 0.09214391014741712 n_clusters 1,2758,736,0.01,0.09214391014741712,1,0.6085607476635514,79.66668844223022,0,None,i7051,79.66668844223022,884.04296875,778.731640625,-1,0,3306078
1742405689,1742405784,95,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 927 n_samples 893 confidence 0.001 feature_proportion 0.10818764161783306 n_clusters 1,927,893,0.001,0.10818764161783306,1,0.5946915887850467,80.36852836608887,0,None,i7098,80.36852836608887,885.29296875,789.0955255681819,-1,0,3306022
1742405689,1742405791,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2713 n_samples 722 confidence 0.001 feature_proportion 0.031279995614651765 n_clusters 1,2713,722,0.001,0.031279995614651765,1,0.6036884735202492,85.72172570228577,0,None,i7030,85.72172570228577,876.84765625,779.4946732954545,-1,0,3306105
1742405689,1742405797,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2867 n_samples 598 confidence 0.1 feature_proportion 0.1686467393387566 n_clusters 4,2867,598,0.1,0.1686467393387566,4,0.6036635514018691,93.80832958221436,0,None,i7105,93.80832958221436,922.9453125,803.6585286458334,-1,0,3306014
1742405688,1742405816,128,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1610 n_samples 906 confidence 0.001 feature_proportion 0.2 n_clusters 1,1610,906,0.001,0.2,1,0.6059065420560747,115.4262375831604,0,None,i7039,115.4262375831604,945.8671875,816.8175223214286,-1,0,3306097
1742405689,1742405829,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3913 n_samples 889 confidence 0.01 feature_proportion 0.18109200192364786 n_clusters 2,3913,889,0.01,0.18109200192364786,2,0.609595015576324,128.37196397781372,0,None,i7053,128.37196397781372,970.74609375,828.7080729166667,-1,0,3306072
1742405690,1742405848,158,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3962 n_samples 856 confidence 0.025 feature_proportion 0.06288468382518166 n_clusters 4,3962,856,0.025,0.06288468382518166,4,0.6100934579439252,144.49067401885986,0,None,i7029,144.49067401885986,985.95703125,838.4590992647059,-1,0,3306108
1742405689,1742405859,170,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3620 n_samples 583 confidence 0.25 feature_proportion 0.2 n_clusters 1,3620,583,0.25,0.2,1,0.6118380062305296,155.5044298171997,0,None,i7068,155.5044298171997,1034.05078125,855.2903645833334,-1,0,3306061
1742405690,1742405861,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3841 n_samples 882 confidence 0.1 feature_proportion 0.10833802240334241 n_clusters 4,3841,882,0.1,0.10833802240334241,4,0.6084236760124611,156.29547691345215,0,None,i7034,156.29547691345215,975.41796875,833.494140625,-1,0,3306102
1742405691,1742405864,173,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3016 n_samples 579 confidence 0.001 feature_proportion 0.2 n_clusters 1,3016,579,0.001,0.2,1,0.6042616822429907,157.35604214668274,0,None,i7116,157.35604214668274,949.74609375,817.4524739583334,-1,0,3305999
1742405748,1742405874,126,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 757 confidence 0.05 feature_proportion 0 n_clusters 2,10,757,0.05,0,2,None,None,1,None,i7048,3306084
1742406899,1742406913,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 825 n_samples 571 confidence 0.25 feature_proportion 0 n_clusters 1,825,571,0.25,0,1,None,None,1,None,i7097,3311154
1742406900,1742406913,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 846 n_samples 574 confidence 0.25 feature_proportion 0 n_clusters 4,846,574,0.25,0,4,None,None,1,None,i7121,3311113
1742406900,1742406913,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 699 n_samples 851 confidence 0.025 feature_proportion 0 n_clusters 1,699,851,0.025,0,1,None,None,1,None,i7132,3311100
1742406900,1742406913,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1762 n_samples 295 confidence 0.25 feature_proportion 0 n_clusters 4,1762,295,0.25,0,4,None,None,1,None,i7086,3311183
1742406900,1742406913,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1690 n_samples 575 confidence 0.25 feature_proportion 0 n_clusters 4,1690,575,0.25,0,4,None,None,1,None,i7144,3311081
1742406904,1742406917,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1763 n_samples 749 confidence 0.25 feature_proportion 0 n_clusters 4,1763,749,0.25,0,4,None,None,1,None,i7094,3311168
1742406900,1742406919,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1232 n_samples 438 confidence 0.25 feature_proportion 0 n_clusters 1,1232,438,0.25,0,1,None,None,1,None,i7115,3311128
1742406909,1742406923,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 958 n_samples 843 confidence 0.25 feature_proportion 0 n_clusters 1,958,843,0.25,0,1,None,None,1,None,i7082,3311253
1742406901,1742406933,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 847 confidence 0.001 feature_proportion 0 n_clusters 1,10,847,0.001,0,1,None,None,1,None,i7074,3311208
1742406930,1742406943,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 955 confidence 0.001 feature_proportion 0 n_clusters 4,10,955,0.001,0,4,None,None,1,None,i7138,3311317
1742406930,1742406943,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 298 confidence 0.001 feature_proportion 0 n_clusters 1,10,298,0.001,0,1,None,None,1,None,i7088,3311383
1742406930,1742406943,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 385 n_samples 850 confidence 0.001 feature_proportion 0 n_clusters 4,385,850,0.001,0,4,None,None,1,None,i7121,3311333
1742406930,1742406944,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 492 n_samples 749 confidence 0.001 feature_proportion 0 n_clusters 4,492,749,0.001,0,4,None,None,1,None,i7074,3311416
1742406931,1742406944,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1325 n_samples 855 confidence 0.25 feature_proportion 0 n_clusters 1,1325,855,0.25,0,1,None,None,1,None,i7101,3311363
1742406900,1742406945,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 297 confidence 0.001 feature_proportion 0.2 n_clusters 4,10,297,0.001,0.2,4,0.4184922118380062,30.072325229644775,0,None,i7102,30.072325229644775,697.890625,692.78515625,-1,0,3311140
1742406961,1742406974,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 762 n_samples 434 confidence 0.05 feature_proportion 0 n_clusters 1,762,434,0.05,0,1,None,None,1,None,i7139,3311444
1742406961,1742406974,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4000 n_samples 428 confidence 0.001 feature_proportion 0 n_clusters 4,4000,428,0.001,0,4,None,None,1,None,i7095,3311500
1742406930,1742406975,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 435 confidence 0.001 feature_proportion 0.2 n_clusters 4,10,435,0.001,0.2,4,0.4044735202492212,30.19045925140381,0,None,i7161,30.19045925140381,696.796875,693.6276041666666,-1,0,3311300
1742406966,1742406979,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1419 n_samples 294 confidence 0.25 feature_proportion 0 n_clusters 1,1419,294,0.25,0,1,None,None,1,None,i7117,3311466
1742406966,1742406979,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 424 n_samples 574 confidence 0.25 feature_proportion 0 n_clusters 4,424,574,0.25,0,4,None,None,1,None,i7099,3311488
1742406930,1742407037,107,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1523 n_samples 954 confidence 0.25 feature_proportion 0.2 n_clusters 3,1523,954,0.25,0.2,3,0.602392523364486,97.81654644012451,0,None,i7082,97.81654644012451,931.046875,804.673828125,-1,0,3311400
1742406910,1742407077,167,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3053 n_samples 436 confidence 0.25 feature_proportion 0.2 n_clusters 1,3053,436,0.25,0.2,1,0.6142056074766356,147.26119208335876,0,None,i7127,147.26119208335876,1001.765625,841.9551930147059,-1,0,3311237
1742406930,1742407091,161,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1694 n_samples 749 confidence 0.25 feature_proportion 0.2 n_clusters 4,1694,749,0.25,0.2,4,0.6135077881619938,145.82007718086243,0,None,i7115,145.82007718086243,839.421875,753.7803308823529,-1,0,3311348
1742406962,1742407097,135,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3693 n_samples 952 confidence 0.001 feature_proportion 0.2 n_clusters 4,3693,952,0.001,0.2,4,0.6077507788161993,121.54477643966675,0,None,i7147,121.54477643966675,942.828125,817.5236979166667,-1,0,3311431
1742406899,1742407134,235,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3855 n_samples 434 confidence 0.001 feature_proportion 0.2 n_clusters 4,3855,434,0.001,0.2,4,0.619202492211838,215.66254687309265,0,None,i7067,215.66254687309265,1003.46484375,795.1829427083334,-1,0,3311223
1742406900,1742407143,243,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1756 n_samples 439 confidence 0.25 feature_proportion 0.2 n_clusters 4,1756,439,0.25,0.2,4,0.6129844236760125,230.38514041900635,0,None,i7139,230.38514041900635,1128.875,908.0767728365385,-1,0,3311090
1742406909,1742407197,288,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1945 n_samples 434 confidence 0.25 feature_proportion 0.2 n_clusters 4,1945,434,0.25,0.2,4,0.6137445482866044,272.6396541595459,0,None,i7030,272.6396541595459,1039.55859375,832.9872395833333,-1,0,3311287
1742406899,1742407197,298,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1612 n_samples 296 confidence 0.25 feature_proportion 0.2 n_clusters 3,1612,296,0.25,0.2,3,0.6099190031152648,284.57457518577576,0,None,i7083,284.57457518577576,1220.6953125,952.9736643145161,-1,0,3311194
1742406962,1742407265,303,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1763 n_samples 295 confidence 0.25 feature_proportion 0.2 n_clusters 4,1763,295,0.25,0.2,4,0.615588785046729,290.82162380218506,0,None,i7102,290.82162380218506,1186.07421875,935.4600830078125,-1,0,3311481
1742406911,1742407299,388,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 673 n_samples 296 confidence 0.25 feature_proportion 0.2 n_clusters 1,673,296,0.25,0.2,1,0.6148909657320872,141.33565211296082,0,None,i7053,141.33565211296082,848.41796875,709.9986213235294,-1,0,3311271
1742408449,1742408488,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1631 n_samples 750 confidence 0.001 feature_proportion 0 n_clusters 4,1631,750,0.001,0,4,None,None,1,None,i7143,3316370
1742408449,1742408489,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 661 n_samples 883 confidence 0.001 feature_proportion 0 n_clusters 4,661,883,0.001,0,4,None,None,1,None,i7131,3316414
1742408449,1742408489,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 741 n_samples 465 confidence 0.1 feature_proportion 0 n_clusters 1,741,465,0.1,0,1,None,None,1,None,i7122,3316424
1742408450,1742408491,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 678 n_samples 853 confidence 0.1 feature_proportion 0 n_clusters 1,678,853,0.1,0,1,None,None,1,None,i7145,3316362
1742408451,1742408495,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 606 n_samples 900 confidence 0.1 feature_proportion 0 n_clusters 1,606,900,0.1,0,1,None,None,1,None,i7136,3316396
1742408453,1742408520,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 774 n_samples 868 confidence 0.025 feature_proportion 0 n_clusters 2,774,868,0.025,0,2,None,None,1,None,i7186,3316441
1742408456,1742408520,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 676 n_samples 297 confidence 0.001 feature_proportion 0 n_clusters 4,676,297,0.001,0,4,None,None,1,None,i7137,3316392
1742408472,1742408539,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 575 n_samples 438 confidence 0.05 feature_proportion 0 n_clusters 1,575,438,0.05,0,1,None,None,1,None,i7183,3316451
1742408480,1742408543,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 873 n_samples 486 confidence 0.01 feature_proportion 0 n_clusters 1,873,486,0.01,0,1,None,None,1,None,i7167,3316459
1742408450,1742408554,104,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 519 n_samples 303 confidence 0.005 feature_proportion 0.002478598260425365 n_clusters 4,519,303,0.005,0.002478598260425365,4,None,None,1,None,i7151,3316342
1742408451,1742408572,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 696 n_samples 591 confidence 0.25 feature_proportion 0.01700880026603647 n_clusters 4,696,591,0.25,0.01700880026603647,4,0.5925233644859813,107.68565535545349,0,None,i7141,107.68565535545349,902.23046875,791.7391826923077,-1,0,3316379
1742408449,1742408578,129,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1459 n_samples 817 confidence 0.25 feature_proportion 0.03797160334902511 n_clusters 2,1459,817,0.25,0.03797160334902511,2,0.6009595015576324,118.94506311416626,0,None,i7132,118.94506311416626,968.33203125,828.3772321428571,-1,0,3316407
1742408572,1742408580,8,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 814 n_samples 284 confidence 0.001 feature_proportion 0 n_clusters 4,814,284,0.001,0,4,None,None,1,None,i7119,3316865
1742408451,1742408582,131,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 673 n_samples 449 confidence 0.025 feature_proportion 0.00875271780369717 n_clusters 2,673,449,0.025,0.00875271780369717,2,0.609993769470405,118.27320790290833,0,None,i7147,118.27320790290833,967.69140625,836.9846540178571,-1,0,3316351
1742408572,1742408589,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 684 n_samples 593 confidence 0.001 feature_proportion 0 n_clusters 4,684,593,0.001,0,4,None,None,1,None,i7105,3316880
1742408572,1742408590,18,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4000 n_samples 468 confidence 0.05 feature_proportion 0 n_clusters 1,4000,468,0.05,0,1,None,None,1,None,i7143,3316826
1742408572,1742408590,18,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 633 n_samples 442 confidence 0.25 feature_proportion 0 n_clusters 1,633,442,0.25,0,1,None,None,1,None,i7138,3316834
1742408572,1742408591,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 699 n_samples 949 confidence 0.025 feature_proportion 0 n_clusters 1,699,949,0.025,0,1,None,None,1,None,i7145,3316819
1742408573,1742408591,18,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 658 n_samples 774 confidence 0.05 feature_proportion 0 n_clusters 3,658,774,0.05,0,3,None,None,1,None,i7132,3316843
1742408573,1742408593,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 786 n_samples 833 confidence 0.025 feature_proportion 0 n_clusters 4,786,833,0.025,0,4,None,None,1,None,i7151,3316811
1742408573,1742408593,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 589 n_samples 904 confidence 0.025 feature_proportion 0 n_clusters 4,589,904,0.025,0,4,None,None,1,None,i7112,3316873
1742408572,1742408598,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1674 n_samples 301 confidence 0.001 feature_proportion 0 n_clusters 4,1674,301,0.001,0,4,None,None,1,None,i7153,3316805
1742408449,1742408603,154,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 734 n_samples 310 confidence 0.001 feature_proportion 0.039228975618062024 n_clusters 4,734,310,0.001,0.039228975618062024,4,0.6134330218068536,140.41379737854004,0,None,i7117,140.41379737854004,979.9921875,830.8099724264706,-1,0,3316433
1742408603,1742408616,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1402 n_samples 782 confidence 0.005 feature_proportion 0 n_clusters 4,1402,782,0.005,0,4,None,None,1,None,i7133,3316960
1742408603,1742408616,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 666 n_samples 293 confidence 0.001 feature_proportion 0 n_clusters 2,666,293,0.001,0,2,None,None,1,None,i7132,3316966
1742408573,1742408652,79,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 732 n_samples 981 confidence 0.001 feature_proportion 0.020766204835648634 n_clusters 1,732,981,0.001,0.020766204835648634,1,0.572386292834891,65.69087624549866,0,None,i7130,65.69087624549866,837.80859375,757.2374131944445,-1,0,3316851
1742408572,1742408658,86,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 588 n_samples 816 confidence 0.25 feature_proportion 0.026851543684910335 n_clusters 1,588,816,0.25,0.026851543684910335,1,0.5679127725856697,75.20727109909058,0,None,i7161,75.20727109909058,831.27734375,756.5828125,-1,0,3316801
1742408573,1742408685,112,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 626 n_samples 460 confidence 0.01 feature_proportion 0.2 n_clusters 2,626,460,0.01,0.2,2,0.6041370716510903,90.253977060318,0,None,i7102,90.253977060318,904.25,797.4762369791666,-1,0,3316890
1742408604,1742408775,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2658 n_samples 288 confidence 0.001 feature_proportion 0.08412929033363922 n_clusters 4,2658,288,0.001,0.08412929033363922,4,0.6141682242990654,154.26624035835266,0,None,i7184,154.26624035835266,991.1875,839.0978732638889,-1,0,3316898
1742408572,1742408861,289,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 532 n_samples 100 confidence 0.001 feature_proportion 0.12909404876410957 n_clusters 4,532,100,0.001,0.12909404876410957,4,0.6235140186915887,275.4829783439636,0,None,i7122,275.4829783439636,1156.88671875,909.940625,-1,0,3316858
1742409710,1742409724,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 243 n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,243,100,0.001,0,4,None,None,1,None,i7102,3320075
1742409712,1742409725,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2722 n_samples 479 confidence 0.1 feature_proportion 0 n_clusters 1,2722,479,0.1,0,1,None,None,1,None,i7094,3320082
1742409711,1742409748,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2426 n_samples 373 confidence 0.001 feature_proportion 0 n_clusters 1,2426,373,0.001,0,1,None,None,1,None,i7077,3320109
1742409736,1742409750,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2340 n_samples 533 confidence 0.001 feature_proportion 0 n_clusters 1,2340,533,0.001,0,1,None,None,1,None,i7183,3320141
1742409740,1742409754,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2413 n_samples 433 confidence 0.001 feature_proportion 0 n_clusters 4,2413,433,0.001,0,4,None,None,1,None,i7119,3320175
1742409741,1742409754,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2320 n_samples 582 confidence 0.001 feature_proportion 0 n_clusters 4,2320,582,0.001,0,4,None,None,1,None,i7093,3320197
1742409740,1742409754,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 247 n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,247,100,0.25,0,1,None,None,1,None,i7067,3320212
1742409740,1742409754,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2779 n_samples 439 confidence 0.001 feature_proportion 0 n_clusters 1,2779,439,0.001,0,1,None,None,1,None,i7115,3320179
1742409741,1742409754,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2398 n_samples 486 confidence 0.001 feature_proportion 0 n_clusters 4,2398,486,0.001,0,4,None,None,1,None,i7159,3320146
1742409743,1742409756,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3283 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,3283,1000,0.25,0,4,None,None,1,None,i7136,3320164
1742409759,1742409772,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3166 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,3166,1000,0.001,0,1,None,None,1,None,i7149,3320216
1742409771,1742409785,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2374 n_samples 422 confidence 0.001 feature_proportion 0 n_clusters 1,2374,422,0.001,0,1,None,None,1,None,i7146,3320232
1742409710,1742409787,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2358 n_samples 526 confidence 0.001 feature_proportion 0.2 n_clusters 4,2358,526,0.001,0.2,4,0.5708286604361371,63.10011672973633,0,None,i7088,63.10011672973633,775.65625,709.6909722222222,-1,0,3320090
1742409741,1742409792,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 174 n_samples 974 confidence 0.001 feature_proportion 0.2 n_clusters 4,174,974,0.001,0.2,4,0.4694454828660436,34.92033553123474,0,None,i7102,34.92033553123474,674.5546875,662.4153645833334,-1,0,3320191
1742409710,1742409800,90,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2434 n_samples 448 confidence 0.001 feature_proportion 0.2 n_clusters 1,2434,448,0.001,0.2,1,0.6021931464174455,78.90971541404724,0,None,i7117,78.90971541404724,881.54296875,777.85546875,-1,0,3320067
1742409710,1742409813,103,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 252 n_samples 193 confidence 0.001 feature_proportion 0.2 n_clusters 4,252,193,0.001,0.2,4,0.6238753894080997,91.88092374801636,0,None,i7082,91.88092374801636,898.05859375,785.1240234375,-1,0,3320100
1742409740,1742409818,78,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2384 n_samples 509 confidence 0.001 feature_proportion 0.2 n_clusters 1,2384,509,0.001,0.2,1,0.5896323987538941,65.19609928131104,0,None,i7150,65.19609928131104,837.6640625,758.0980902777778,-1,0,3320153
1742409710,1742409831,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2727 n_samples 465 confidence 0.01 feature_proportion 0.2 n_clusters 1,2727,465,0.01,0.2,1,0.6070155763239875,98.42846727371216,0,None,i7068,98.42846727371216,918.45703125,795.9407552083334,-1,0,3320116
1742409740,1742409837,97,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 240 n_samples 209 confidence 0.25 feature_proportion 0.2 n_clusters 4,240,209,0.25,0.2,4,0.6203489096573209,84.0319311618805,0,None,i7143,84.0319311618805,900.05859375,793.1125710227273,-1,0,3320158
1742409715,1742409857,142,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2397 n_samples 487 confidence 0.001 feature_proportion 0.003689519962727331 n_clusters 1,2397,487,0.001,0.003689519962727331,1,0.5943426791277259,124.48534488677979,0,None,i7137,124.48534488677979,815.7421875,744.3872395833333,-1,0,3320052
1742409741,1742409862,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2387 n_samples 348 confidence 0.001 feature_proportion 0.2 n_clusters 4,2387,348,0.001,0.2,4,0.6119501557632399,91.48940134048462,0,None,i7090,91.48940134048462,762.8515625,708.6640625,-1,0,3320203
1742409711,1742409865,154,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 236 n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 4,236,100,0.25,0.2,4,0.6317258566978193,140.7662868499756,0,None,i7132,140.7662868499756,974.08203125,841.5142463235294,-1,0,3320058
1742409757,1742409885,128,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1253 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 4,1253,1000,0.25,0.2,4,0.5940934579439252,103.20461821556091,0,None,i7140,103.20461821556091,779.7109375,707.8849158653846,-1,0,3320219
1742409772,1742409890,118,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2597 n_samples 439 confidence 0.1 feature_proportion 0.2 n_clusters 1,2597,439,0.1,0.2,1,0.606791277258567,101.04082250595093,0,None,i7163,101.04082250595093,911.58984375,801.6379206730769,-1,0,3320224
1742409711,1742409892,181,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2590 n_samples 456 confidence 0.01 feature_proportion 0.2 n_clusters 1,2590,456,0.01,0.2,1,0.6110903426791278,129.48246955871582,0,None,i7060,129.48246955871582,762.2109375,634.7828125,-1,0,3320123
1742409716,1742409903,187,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 262 n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 4,262,100,0.001,0.2,4,0.6412461059190031,173.089097738266,0,None,i7172,173.089097738266,1007.88671875,843.6421875,-1,0,3320135
1742409741,1742409935,194,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2731 n_samples 398 confidence 0.25 feature_proportion 0.2 n_clusters 1,2731,398,0.25,0.2,1,0.6078130841121495,154.68181109428406,0,None,i7076,154.68181109428406,809.4609375,667.0549045138889,-1,0,3320207
1742409741,1742409938,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2339 n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 4,2339,100,0.25,0.2,4,0.6338442367601246,182.32125115394592,0,None,i7131,182.32125115394592,1091.734375,884.2088913690476,-1,0,3320168
1742409741,1742409956,215,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4000 n_samples 514 confidence 0.25 feature_proportion 0.2 n_clusters 1,4000,514,0.25,0.2,1,0.6141806853582554,204.35234141349792,0,None,i7103,204.35234141349792,1127.3203125,900.0636888586956,-1,0,3320186
1742409711,1742409981,270,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2369 n_samples 191 confidence 0.001 feature_proportion 0.2 n_clusters 4,2369,191,0.001,0.2,4,0.6251090342679128,230.7335398197174,0,None,i7059,230.7335398197174,815.76171875,691.9421574519231,-1,0,3320130
1742410817,1742410830,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 266 n_samples 168 confidence 0.001 feature_proportion 0 n_clusters 4,266,168,0.001,0,4,None,None,1,None,i7153,3322455
1742410817,1742410830,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 222 n_samples 397 confidence 0.25 feature_proportion 0 n_clusters 4,222,397,0.25,0,4,None,None,1,None,i7150,3322460
1742410819,1742410832,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3281 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,3281,1000,0.25,0,4,None,None,1,None,i7137,3322473
1742410788,1742410833,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 188 n_samples 924 confidence 0.001 feature_proportion 0.08286898733530644 n_clusters 4,188,924,0.001,0.08286898733530644,4,0.4857819314641745,34.4283332824707,0,None,i7133,34.4283332824707,727.91015625,703.8294270833334,-1,0,3322426
1742410788,1742410851,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 161 n_samples 419 confidence 0.25 feature_proportion 0.1557233995678288 n_clusters 4,161,419,0.25,0.1557233995678288,4,0.5433894080996885,49.684279680252075,0,None,i7138,49.684279680252075,773.5234375,722.59375,-1,0,3322422
1742410797,1742410861,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 450 n_samples 1000 confidence 0.001 feature_proportion 0.08745904499094427 n_clusters 4,450,1000,0.001,0.08745904499094427,4,0.5259563862928349,54.1499297618866,0,None,i7165,54.1499297618866,779.67578125,729.0576171875,-1,0,3322437
1742410817,1742410862,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 158 n_samples 1000 confidence 0.25 feature_proportion 0.0033295662157790335 n_clusters 4,158,1000,0.25,0.0033295662157790335,4,0.45166355140186915,34.87004041671753,0,None,i7146,34.87004041671753,721.8203125,700.7532552083334,-1,0,3322463
1742410817,1742410862,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 111 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 1,111,1000,0.25,0.2,1,0.4333208722741433,31.110738277435303,0,None,i7151,31.110738277435303,713.03125,699.2122395833334,-1,0,3322457
1742410818,1742410862,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 123 n_samples 779 confidence 0.1 feature_proportion 0.2 n_clusters 1,123,779,0.1,0.2,1,0.46213084112149533,34.54665446281433,0,None,i7143,34.54665446281433,723.5625,702.3756510416666,-1,0,3322467
1742410849,1742410863,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2792 n_samples 822 confidence 0.25 feature_proportion 0 n_clusters 4,2792,822,0.25,0,4,None,None,1,None,i7159,3322494
1742410850,1742410863,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 262 n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,262,100,0.001,0,4,None,None,1,None,i7139,3322518
1742410818,1742410870,52,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 173 n_samples 784 confidence 0.001 feature_proportion 0.2 n_clusters 4,173,784,0.001,0.2,4,0.489208722741433,41.259029150009155,0,None,i7156,41.259029150009155,732.0859375,708.9397321428571,-1,0,3322452
1742410797,1742410874,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2667 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 1,2667,1000,0.25,0.2,1,0.5611713395638629,62.081666707992554,0,None,i7183,62.081666707992554,825.1875,753.2339409722222,-1,0,3322435
1742410817,1742410881,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 76 n_samples 443 confidence 0.25 feature_proportion 0.034730300015906414 n_clusters 1,76,443,0.25,0.034730300015906414,1,0.48862305295950154,50.17362833023071,0,None,i7165,50.17362833023071,726.984375,705.220703125,-1,0,3322447
1742410837,1742410882,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 79 n_samples 1000 confidence 0.001 feature_proportion 0.0754731574651209 n_clusters 1,79,1000,0.001,0.0754731574651209,1,0.4185669781931464,30.797226667404175,0,None,i7182,30.797226667404175,701.82421875,694.482421875,-1,0,3322483
1742410817,1742410887,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 563 n_samples 895 confidence 0.001 feature_proportion 0.2 n_clusters 4,563,895,0.001,0.2,4,0.5606105919003115,60.65245461463928,0,None,i7171,60.65245461463928,818.23046875,749.9986979166666,-1,0,3322443
1742410818,1742410888,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2490 n_samples 822 confidence 0.25 feature_proportion 0.06713860968752086 n_clusters 4,2490,822,0.25,0.06713860968752086,4,0.5564859813084112,56.018726110458374,0,None,i7186,56.018726110458374,806.125,742.8525390625,-1,0,3322440
1742410788,1742410896,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2568 n_samples 459 confidence 0.001 feature_proportion 0.11416081749318312 n_clusters 1,2568,459,0.001,0.11416081749318312,1,0.6070903426791278,92.50155711174011,0,None,i7131,92.50155711174011,892.3125,791.2600911458334,-1,0,3322431
1742410788,1742410897,109,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2504 n_samples 498 confidence 0.001 feature_proportion 0.11198339480735688 n_clusters 4,2504,498,0.001,0.11198339480735688,4,0.6036635514018691,97.4034492969513,0,None,i7140,97.4034492969513,887.6875,782.9954427083334,-1,0,3322418
1742410817,1742410899,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2408 n_samples 511 confidence 0.001 feature_proportion 0.11035713478662604 n_clusters 4,2408,511,0.001,0.11035713478662604,4,0.593595015576324,71.09805464744568,0,None,i7161,71.09805464744568,854.37890625,770.815234375,-1,0,3322449
1742410823,1742410907,84,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2404 n_samples 593 confidence 0.001 feature_proportion 0.10121623303146655 n_clusters 4,2404,593,0.001,0.10121623303146655,4,0.5741682242990654,73.52348327636719,0,None,i7135,73.52348327636719,826.73046875,755.879296875,-1,0,3322476
1742410849,1742410913,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 487 n_samples 1000 confidence 0.25 feature_proportion 0.09186565259229629 n_clusters 1,487,1000,0.25,0.09186565259229629,1,0.539202492211838,49.816471099853516,0,None,i7150,49.816471099853516,785.42578125,727.0111607142857,-1,0,3322504
1742410849,1742410920,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 578 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 1,578,1000,0.001,0.2,1,0.5492585669781932,61.75925135612488,0,None,i7182,61.75925135612488,807.25,744.91015625,-1,0,3322489
1742410855,1742410925,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 238 n_samples 386 confidence 0.25 feature_proportion 0.09586163469719003 n_clusters 4,238,386,0.25,0.09586163469719003,4,0.5793769470404985,59.173320293426514,0,None,i7117,59.173320293426514,792.08203125,729.27783203125,-1,0,3322534
1742410850,1742410925,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2672 n_samples 1000 confidence 0.25 feature_proportion 0.09096400512379894 n_clusters 4,2672,1000,0.25,0.09096400512379894,4,0.5639626168224299,64.89527177810669,0,None,i7131,64.89527177810669,819.8671875,747.8602430555555,-1,0,3322524
1742410849,1742410932,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 875 n_samples 1000 confidence 0.25 feature_proportion 0.06635439738002917 n_clusters 4,875,1000,0.25,0.06635439738002917,4,0.5814828660436137,70.89947056770325,0,None,i7146,70.89947056770325,860.78125,775.28125,-1,0,3322509
1742410858,1742410934,76,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 131 n_samples 377 confidence 0.25 feature_proportion 0.2 n_clusters 4,131,377,0.25,0.2,4,0.5439626168224299,44.73272657394409,0,None,i7112,44.73272657394409,613.8671875,572.6155133928571,-1,0,3322539
1742410850,1742410951,101,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2560 n_samples 997 confidence 0.001 feature_proportion 0.2 n_clusters 1,2560,997,0.001,0.2,1,0.5514018691588785,61.30353021621704,0,None,i7121,61.30353021621704,722.3828125,665.3463541666666,-1,0,3322529
1742410849,1742410963,114,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2749 n_samples 519 confidence 0.001 feature_proportion 0.09927640693091526 n_clusters 4,2749,519,0.001,0.09927640693091526,4,0.6042118380062306,102.79720282554626,0,None,i7152,102.79720282554626,926.71875,809.4762620192307,-1,0,3322499
1742410849,1742411001,152,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1817 n_samples 783 confidence 0.001 feature_proportion 0.08443301817178875 n_clusters 4,1817,783,0.001,0.08443301817178875,4,0.6069906542056075,138.7195794582367,0,None,i7142,138.7195794582367,990.5703125,839.865478515625,-1,0,3322513
1742412017,1742412030,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 233 n_samples 878 confidence 0.001 feature_proportion 0 n_clusters 1,233,878,0.001,0,1,None,None,1,None,i7151,3325070
1742412018,1742412031,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 157 n_samples 792 confidence 0.25 feature_proportion 0 n_clusters 4,157,792,0.25,0,4,None,None,1,None,i7140,3325078
1742412018,1742412032,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3156 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,3156,1000,0.25,0,1,None,None,1,None,i7162,3325065
1742412023,1742412036,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 202 n_samples 268 confidence 0.001 feature_proportion 0 n_clusters 1,202,268,0.001,0,1,None,None,1,None,i7139,3325085
1742412037,1742412050,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2444 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,2444,1000,0.25,0,1,None,None,1,None,i7158,3325092
1742412037,1742412050,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4000 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,4000,1000,0.25,0,1,None,None,1,None,i7171,3325089
1742412037,1742412051,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2743 n_samples 889 confidence 0.25 feature_proportion 0 n_clusters 1,2743,889,0.25,0,1,None,None,1,None,i7165,3325091
1742411993,1742412063,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2511 n_samples 836 confidence 0.25 feature_proportion 0.2 n_clusters 1,2511,836,0.25,0.2,1,0.5683613707165109,58.08796787261963,0,None,i7119,58.08796787261963,803.4765625,739.61962890625,-1,0,3325053
1742412053,1742412066,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 422 n_samples 822 confidence 0.25 feature_proportion 0 n_clusters 4,422,822,0.25,0,4,None,None,1,None,i7151,3325109
1742412053,1742412066,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 148 n_samples 682 confidence 0.001 feature_proportion 0 n_clusters 1,148,682,0.001,0,1,None,None,1,None,i7183,3325096
1742412017,1742412068,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 173 n_samples 656 confidence 0.001 feature_proportion 0.2 n_clusters 1,173,656,0.001,0.2,1,0.5045358255451713,39.21395230293274,0,None,i7143,39.21395230293274,743.5390625,709.9596354166666,-1,0,3325075
1742412054,1742412068,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 627 n_samples 889 confidence 0.001 feature_proportion 0 n_clusters 4,627,889,0.001,0,4,None,None,1,None,i7163,3325101
1742412054,1742412074,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3189 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,3189,1000,0.001,0,4,None,None,1,None,i7162,3325102
1742412017,1742412082,65,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 93 n_samples 309 confidence 0.25 feature_proportion 0.2 n_clusters 1,93,309,0.25,0.2,1,0.5484984423676013,48.7971978187561,0,None,i7165,48.7971978187561,755.1171875,714.8275669642857,-1,0,3325062
1742411997,1742412082,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 245 n_samples 318 confidence 0.001 feature_proportion 0.2 n_clusters 4,245,318,0.001,0.2,4,0.5980934579439252,68.88354992866516,0,None,i7153,68.88354992866516,848.37109375,760.9921875,-1,0,3325056
1742412017,1742412088,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 203 n_samples 313 confidence 0.25 feature_proportion 0.2 n_clusters 1,203,313,0.25,0.2,1,0.5897694704049844,58.80632543563843,0,None,i7159,58.80632543563843,816.0703125,744.27490234375,-1,0,3325067
1742412017,1742412094,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2642 n_samples 841 confidence 0.001 feature_proportion 0.2 n_clusters 1,2642,841,0.001,0.2,1,0.5775576323987539,64.91550469398499,0,None,i7146,64.91550469398499,834.29296875,757.1063368055555,-1,0,3325073
1742412017,1742412094,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2416 n_samples 887 confidence 0.25 feature_proportion 0.2 n_clusters 1,2416,887,0.25,0.2,1,0.5182679127725857,62.06952738761902,0,None,i7138,62.06952738761902,784.5859375,733.8563368055555,-1,0,3325082
1742412053,1742412110,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 366 n_samples 889 confidence 0.001 feature_proportion 0.2 n_clusters 1,366,889,0.001,0.2,1,0.5206604361370717,45.44699263572693,0,None,i7146,45.44699263572693,777.1015625,725.5708705357143,-1,0,3325112
1742411997,1742412112,115,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 267 n_samples 161 confidence 0.001 feature_proportion 0.2 n_clusters 4,267,161,0.001,0.2,4,0.6269034267912773,106.22525238990784,0,None,i7142,106.22525238990784,941.67578125,807.2725360576923,-1,0,3325058
1742412023,1742412113,90,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 261 n_samples 266 confidence 0.001 feature_proportion 0.2 n_clusters 4,261,266,0.001,0.2,4,0.6167601246105919,78.36879277229309,0,None,i7154,78.36879277229309,880.75390625,777.162890625,-1,0,3325083
1742412053,1742412117,64,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 153 n_samples 322 confidence 0.25 feature_proportion 0.2 n_clusters 1,153,322,0.25,0.2,1,0.5674641744548287,50.38141989707947,0,None,i7172,50.38141989707947,789.9140625,737.91552734375,-1,0,3325099
1742412053,1742412123,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2462 n_samples 732 confidence 0.25 feature_proportion 0.2 n_clusters 1,2462,732,0.25,0.2,1,0.5674890965732087,56.15884709358215,0,None,i7143,56.15884709358215,815.4609375,746.919921875,-1,0,3325116
1742412054,1742412125,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2421 n_samples 817 confidence 0.001 feature_proportion 0.2 n_clusters 1,2421,817,0.001,0.2,1,0.5523364485981308,60.65438199043274,0,None,i7186,60.65438199043274,797.94140625,739.8177083333334,-1,0,3325094
1742412057,1742412127,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2488 n_samples 877 confidence 0.25 feature_proportion 0.2 n_clusters 4,2488,877,0.25,0.2,4,0.5500311526479751,54.58196449279785,0,None,i7158,54.58196449279785,802.6171875,739.86279296875,-1,0,3325125
1742412053,1742412135,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 171 n_samples 308 confidence 0.25 feature_proportion 0.2 n_clusters 4,171,308,0.25,0.2,4,0.5766230529595016,69.87320113182068,0,None,i7152,69.87320113182068,818.8203125,746.0169270833334,-1,0,3325105
1742412038,1742412140,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3250 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 4,3250,1000,0.25,0.2,4,0.5944548286604361,90.183513879776,0,None,i7183,90.183513879776,911.4765625,800.9091796875,-1,0,3325087
1742412017,1742412151,134,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1787 n_samples 912 confidence 0.25 feature_proportion 0.2 n_clusters 1,1787,912,0.25,0.2,1,0.6021183800623053,119.76500082015991,0,None,i7139,119.76500082015991,958.3828125,817.1275111607143,-1,0,3325080
1742412053,1742412161,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3314 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 1,3314,1000,0.001,0.2,1,0.5981682242990655,95.44271731376648,0,None,i7182,95.44271731376648,911.51171875,799.2568359375,-1,0,3325097
1742412057,1742412196,139,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4000 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 1,4000,1000,0.001,0.2,1,0.6072772585669782,129.61645531654358,0,None,i7182,129.61645531654358,961.03515625,824.33671875,-1,0,3325120
1742413517,1742413530,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3231 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,3231,1000,0.25,0,1,None,None,1,None,i7132,3327405
1742413517,1742413530,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 217 n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,217,100,0.25,0,1,None,None,1,None,i7133,3327401
1742413518,1742413531,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 176 n_samples 293 confidence 0.001 feature_proportion 0 n_clusters 1,176,293,0.001,0,1,None,None,1,None,i7144,3327390
1742413518,1742413531,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 247 n_samples 232 confidence 0.001 feature_proportion 0 n_clusters 1,247,232,0.001,0,1,None,None,1,None,i7130,3327409
1742413526,1742413539,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 224 n_samples 271 confidence 0.001 feature_proportion 0 n_clusters 4,224,271,0.001,0,4,None,None,1,None,i7162,3327417
1742413537,1742413550,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 87 n_samples 232 confidence 0.001 feature_proportion 0 n_clusters 1,87,232,0.001,0,1,None,None,1,None,i7182,3327429
1742413554,1742413567,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2438 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,2438,1000,0.25,0,4,None,None,1,None,i7146,3327463
1742413554,1742413567,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2845 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,2845,1000,0.25,0,1,None,None,1,None,i7152,3327458
1742413554,1742413567,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 448 n_samples 901 confidence 0.25 feature_proportion 0 n_clusters 4,448,901,0.25,0,4,None,None,1,None,i7158,3327450
1742413554,1742413567,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 405 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,405,1000,0.001,0,1,None,None,1,None,i7183,3327438
1742413555,1742413568,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3066 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,3066,1000,0.001,0,4,None,None,1,None,i7161,3327448
1742413517,1742413568,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 53 n_samples 275 confidence 0.25 feature_proportion 0.0822750507867642 n_clusters 1,53,275,0.25,0.0822750507867642,1,0.513171339563863,37.061887979507446,0,None,i7153,37.061887979507446,733.86328125,706.2591145833334,-1,0,3327381
1742413555,1742413569,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 47 n_samples 145 confidence 0.25 feature_proportion 0 n_clusters 4,47,145,0.25,0,4,None,None,1,None,i7162,3327445
1742413557,1742413570,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2918 n_samples 910 confidence 0.25 feature_proportion 0 n_clusters 1,2918,910,0.25,0,1,None,None,1,None,i7137,3327474
1742413557,1742413570,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 966 n_samples 916 confidence 0.25 feature_proportion 0 n_clusters 4,966,916,0.25,0,4,None,None,1,None,i7171,3327480
1742413557,1742413571,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4000 n_samples 616 confidence 0.001 feature_proportion 0 n_clusters 4,4000,616,0.001,0,4,None,None,1,None,i7182,3327477
1742413519,1742413571,52,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 126 n_samples 796 confidence 0.25 feature_proportion 0.048103921523861434 n_clusters 1,126,796,0.25,0.048103921523861434,1,0.46277881619937694,40.39024114608765,0,None,i7135,40.39024114608765,721.84375,703.3119419642857,-1,0,3327396
1742413524,1742413574,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 258 n_samples 959 confidence 0.25 feature_proportion 0.11741278370889174 n_clusters 1,258,959,0.25,0.11741278370889174,1,0.48670404984423676,38.81190586090088,0,None,i7128,38.81190586090088,747.3125,709.8430989583334,-1,0,3327427
1742413517,1742413575,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 241 n_samples 628 confidence 0.25 feature_proportion 0.2 n_clusters 4,241,628,0.25,0.2,4,0.5326604361370717,46.82987689971924,0,None,i7150,46.82987689971924,767.27734375,722.6696428571429,-1,0,3327385
1742413522,1742413580,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 192 n_samples 779 confidence 0.001 feature_proportion 0.002256141414090022 n_clusters 4,192,779,0.001,0.002256141414090022,4,0.4893956386292835,43.53434753417969,0,None,i7158,43.53434753417969,741.6171875,711.4603794642857,-1,0,3327375
1742413538,1742413595,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 239 n_samples 724 confidence 0.25 feature_proportion 0.2 n_clusters 4,239,724,0.25,0.2,4,0.5050841121495327,44.22631502151489,0,None,i7171,44.22631502151489,757.984375,718.7745535714286,-1,0,3327432
1742413585,1742413598,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 182 n_samples 885 confidence 0.25 feature_proportion 0 n_clusters 1,182,885,0.25,0,1,None,None,1,None,i7185,3327489
1742413525,1742413601,76,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2527 n_samples 795 confidence 0.001 feature_proportion 0.13113334043832062 n_clusters 4,2527,795,0.001,0.13113334043832062,4,0.5691339563862928,64.2805848121643,0,None,i7186,64.2805848121643,817.796875,749.4253472222222,-1,0,3327413
1742413554,1742413605,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 149 n_samples 700 confidence 0.25 feature_proportion 0.12404781643412839 n_clusters 4,149,700,0.25,0.12404781643412839,4,0.48679127725856697,37.04089975357056,0,None,i7139,37.04089975357056,736.26953125,706.3053385416666,-1,0,3327470
1742413554,1742413612,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 39 n_samples 352 confidence 0.25 feature_proportion 0.10498178532278885 n_clusters 1,39,352,0.25,0.10498178532278885,1,0.47280996884735205,42.49387550354004,0,None,i7167,42.49387550354004,716.84765625,700.796875,-1,0,3327441
1742413554,1742413624,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 526 n_samples 742 confidence 0.25 feature_proportion 0.2 n_clusters 4,526,742,0.25,0.2,4,0.5723613707165109,60.87425374984741,0,None,i7153,60.87425374984741,832.890625,755.8268229166666,-1,0,3327455
1742413578,1742413629,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 147 n_samples 992 confidence 0.25 feature_proportion 0.13121590132807093 n_clusters 4,147,992,0.25,0.13121590132807093,4,0.45486604361370714,36.21155524253845,0,None,i7183,36.21155524253845,717.33203125,699.1236979166666,-1,0,3327485
1742413524,1742413631,107,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 232 n_samples 157 confidence 0.25 feature_proportion 0.2 n_clusters 1,232,157,0.25,0.2,1,0.6231900311526479,98.5530366897583,0,None,i7132,98.5530366897583,926.19140625,801.0026041666666,-1,0,3327423
1742413554,1742413656,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 877 n_samples 823 confidence 0.25 feature_proportion 0.2 n_clusters 1,877,823,0.25,0.2,1,0.5935700934579439,87.9837372303009,0,None,i7186,87.9837372303009,891.0625,783.6750710227273,-1,0,3327435
1742413554,1742413701,147,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 252 n_samples 125 confidence 0.001 feature_proportion 0.2 n_clusters 1,252,125,0.001,0.2,1,0.6342429906542056,136.33309745788574,0,None,i7144,136.33309745788574,967.94921875,823.75390625,-1,0,3327466
1742414839,1742414853,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3154 n_samples 856 confidence 0.25 feature_proportion 0 n_clusters 1,3154,856,0.25,0,1,None,None,1,None,i7184,3329837
1742414845,1742414858,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2055 n_samples 967 confidence 0.25 feature_proportion 0 n_clusters 4,2055,967,0.25,0,4,None,None,1,None,i7158,3329855
1742414846,1742414859,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1122 n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,1122,100,0.001,0,1,None,None,1,None,i7142,3329872
1742414846,1742414859,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 30 n_samples 156 confidence 0.25 feature_proportion 0 n_clusters 1,30,156,0.25,0,1,None,None,1,None,i7162,3329851
1742414846,1742414859,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 186 n_samples 216 confidence 0.25 feature_proportion 0 n_clusters 1,186,216,0.25,0,1,None,None,1,None,i7149,3329859
1742414847,1742414860,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3234 n_samples 824 confidence 0.25 feature_proportion 0 n_clusters 4,3234,824,0.25,0,4,None,None,1,None,i7141,3329874
1742414858,1742414871,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 984 n_samples 639 confidence 0.001 feature_proportion 0 n_clusters 1,984,639,0.001,0,1,None,None,1,None,i7145,3329882
1742414859,1742414872,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3006 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,3006,1000,0.25,0,1,None,None,1,None,i7183,3329878
1742414875,1742414888,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2361 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,2361,1000,0.001,0,4,None,None,1,None,i7143,3329914
1742414875,1742414888,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2969 n_samples 921 confidence 0.25 feature_proportion 0 n_clusters 1,2969,921,0.25,0,1,None,None,1,None,i7146,3329909
1742414875,1742414888,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 111 n_samples 203 confidence 0.25 feature_proportion 0 n_clusters 1,111,203,0.25,0,1,None,None,1,None,i7158,3329898
1742414875,1742414888,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2207 n_samples 763 confidence 0.25 feature_proportion 0 n_clusters 4,2207,763,0.25,0,4,None,None,1,None,i7167,3329891
1742414845,1742414903,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2232 n_samples 693 confidence 0.001 feature_proportion 0.2 n_clusters 4,2232,693,0.001,0.2,4,0.5202367601246106,44.86746954917908,0,None,i7164,44.86746954917908,763.234375,720.5262276785714,-1,0,3329848
1742414875,1742414914,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2019 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,2019,1000,0.001,0.2,4,0.36183177570093455,26.830154180526733,0,None,i7182,26.830154180526733,694.53515625,691.76171875,-1,0,3329885
1742414858,1742414916,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2224 n_samples 539 confidence 0.001 feature_proportion 0.2 n_clusters 4,2224,539,0.001,0.2,4,0.536,46.51066565513611,0,None,i7159,46.51066565513611,776.54296875,724.5161830357143,-1,0,3329880
1742414840,1742414923,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2253 n_samples 482 confidence 0.001 feature_proportion 0.2 n_clusters 4,2253,482,0.001,0.2,4,0.5581931464174454,69.62704515457153,0,None,i7176,69.62704515457153,797.89453125,736.2960069444445,-1,0,3329841
1742414845,1742414928,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2290 n_samples 450 confidence 0.001 feature_proportion 0.2 n_clusters 4,2290,450,0.001,0.2,4,0.5710404984423676,72.7749240398407,0,None,i7144,72.7749240398407,823.03515625,753.2234375,-1,0,3329865
1742414845,1742414934,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3055 n_samples 903 confidence 0.25 feature_proportion 0.2 n_clusters 1,3055,903,0.25,0.2,1,0.5929844236760125,81.6974229812622,0,None,i7143,81.6974229812622,899.703125,795.8430397727273,-1,0,3329868
1742414876,1742414935,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2200 n_samples 732 confidence 0.25 feature_proportion 0.2 n_clusters 4,2200,732,0.25,0.2,4,0.49466666666666664,43.27050805091858,0,None,i7163,43.27050805091858,745.7890625,711.9542410714286,-1,0,3329894
1742414898,1742414937,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2033 n_samples 908 confidence 0.25 feature_proportion 0.2 n_clusters 4,2033,908,0.25,0.2,4,0.3790280373831776,28.275927543640137,0,None,i7183,28.275927543640137,695.2890625,692.490625,-1,0,3329933
1742414845,1742414947,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3138 n_samples 853 confidence 0.25 feature_proportion 0.2 n_clusters 4,3138,853,0.25,0.2,4,0.5954641744548287,88.89556622505188,0,None,i7146,88.89556622505188,916.9921875,797.5081676136364,-1,0,3329861
1742414875,1742414965,90,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2657 n_samples 642 confidence 0.001 feature_proportion 0.2 n_clusters 1,2657,642,0.001,0.2,1,0.5946168224299065,77.05146980285645,0,None,i7142,77.05146980285645,888.3515625,781.16484375,-1,0,3329917
1742414899,1742414970,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2330 n_samples 704 confidence 0.25 feature_proportion 0.2 n_clusters 4,2330,704,0.25,0.2,4,0.5450841121495327,61.03848147392273,0,None,i7185,61.03848147392273,772.0546875,724.1141493055555,-1,0,3329930
1742414906,1742414971,65,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 28 n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,28,100,0.25,0.2,1,0.591563862928349,47.86318397521973,0,None,i7184,47.86318397521973,752.52734375,712.1294642857143,-1,0,3329936
1742414875,1742414985,110,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 965 n_samples 627 confidence 0.25 feature_proportion 0.2 n_clusters 1,965,627,0.25,0.2,1,0.5968348909657321,100.59809947013855,0,None,i7150,100.59809947013855,929.04296875,809.0024038461538,-1,0,3329905
1742414877,1742414986,109,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 465 n_samples 353 confidence 0.001 feature_proportion 0.2 n_clusters 4,465,353,0.001,0.2,4,None,None,1,None,i7141,3329921
1742414878,1742414999,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1958 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,1958,1000,0.001,0.2,4,0.6069657320872274,112.61151099205017,0,None,i7145,112.61151099205017,958.92578125,825.75390625,-1,0,3329928
1742414867,1742415058,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2957 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 1,2957,1000,0.001,0.2,1,0.59197507788162,162.0165627002716,0,None,i7173,162.0165627002716,873.8203125,781.9751233552631,-1,0,3329845
1742414875,1742415117,242,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1027 n_samples 242 confidence 0.001 feature_proportion 0.2 n_clusters 4,1027,242,0.001,0.2,4,0.6173956386292835,230.70898461341858,0,None,i7154,230.70898461341858,1145.15625,912.4900841346154,-1,0,3329902
1742414877,1742415437,560,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1053 n_samples 182 confidence 0.25 feature_proportion 0.2 n_clusters 1,1053,182,0.25,0.2,1,0.6292585669781932,539.9989767074585,0,None,i7169,539.9989767074585,1203.4375,949.0694056919643,-1,0,3329888
1742416946,1742416959,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1919 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,1919,1000,0.25,0,4,None,None,1,None,i7154,3332585
1742416946,1742416959,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1339 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,1339,1000,0.001,0,4,None,None,1,None,i7153,3332591
1742416946,1742416959,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 78 n_samples 149 confidence 0.01 feature_proportion 0 n_clusters 1,78,149,0.01,0,1,None,None,1,None,i7158,3332580
1742416946,1742416959,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2241 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,2241,1000,0.001,0,4,None,None,1,None,i7152,3332596
1742416976,1742416989,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1888 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,1888,1000,0.25,0,4,None,None,1,None,i7151,3332635
1742416976,1742416989,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1943 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,1943,1000,0.25,0,1,None,None,1,None,i7153,3332629
1742416976,1742416990,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 84 n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,84,100,0.25,0,1,None,None,1,None,i7158,3332623
1742416976,1742416990,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2742 n_samples 824 confidence 0.25 feature_proportion 0 n_clusters 1,2742,824,0.25,0,1,None,None,1,None,i7150,3332643
1742416976,1742416990,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 92 n_samples 235 confidence 0.01 feature_proportion 0 n_clusters 1,92,235,0.01,0,1,None,None,1,None,i7146,3332647
1742416976,1742416990,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2268 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,2268,1000,0.001,0,1,None,None,1,None,i7150,3332641
1742416979,1742416992,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1196 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,1196,1000,0.001,0,1,None,None,1,None,i7145,3332653
1742416939,1742416997,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2233 n_samples 1000 confidence 0.005 feature_proportion 0.2 n_clusters 1,2233,1000,0.005,0.2,1,0.4861682242990654,43.3129198551178,0,None,i7183,43.3129198551178,733.62890625,708.76171875,-1,0,3332563
1742416946,1742416997,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 39 n_samples 186 confidence 0.01 feature_proportion 0.2 n_clusters 1,39,186,0.01,0.2,1,0.5516510903426791,39.075759410858154,0,None,i7151,39.075759410858154,740.52734375,707.515625,-1,0,3332600
1742416946,1742417004,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2201 n_samples 1000 confidence 0.1 feature_proportion 0.2 n_clusters 1,2201,1000,0.1,0.2,1,0.4838753894080997,45.047884702682495,0,None,i7164,45.047884702682495,727.80859375,704.5943080357143,-1,0,3332576
1742416999,1742417012,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1886 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,1886,1000,0.001,0,4,None,None,1,None,i7183,3332656
1742416959,1742417017,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2292 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,2292,1000,0.001,0.2,4,0.5073395638629283,43.501038551330566,0,None,i7183,43.501038551330566,747.05859375,713.94140625,-1,0,3332603
1742416976,1742417027,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2212 n_samples 1000 confidence 0.01 feature_proportion 0.2 n_clusters 4,2212,1000,0.01,0.2,4,0.4788909657320872,38.441001892089844,0,None,i7165,38.441001892089844,727.23828125,702.84765625,-1,0,3332615
1742416977,1742417034,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2208 n_samples 937 confidence 0.001 feature_proportion 0.2 n_clusters 1,2208,937,0.001,0.2,1,0.4768598130841121,45.65560984611511,0,None,i7154,45.65560984611511,735.23046875,707.6897321428571,-1,0,3332624
1742416946,1742417048,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2758 n_samples 813 confidence 0.25 feature_proportion 0.2 n_clusters 4,2758,813,0.25,0.2,4,0.587214953271028,85.65534996986389,0,None,i7164,85.65534996986389,868.6640625,774.9591619318181,-1,0,3332574
1742416939,1742417066,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1898 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 4,1898,1000,0.25,0.2,4,0.6121370716510903,117.34260845184326,0,None,i7186,117.34260845184326,954.5234375,820.2583705357143,-1,0,3332559
1742416976,1742417071,95,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1057 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,1057,1000,0.001,0.2,4,0.5941308411214953,84.19932699203491,0,None,i7152,84.19932699203491,891.5,787.3781960227273,-1,0,3332632
1742416976,1742417072,96,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2370 n_samples 336 confidence 0.25 feature_proportion 0.2 n_clusters 1,2370,336,0.25,0.2,1,0.619601246105919,83.02502775192261,0,None,i7172,83.02502775192261,890.171875,788.6566051136364,-1,0,3332612
1742416946,1742417073,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1887 n_samples 1000 confidence 0.01 feature_proportion 0.2 n_clusters 4,1887,1000,0.01,0.2,4,0.6087601246105919,116.21261239051819,0,None,i7152,116.21261239051819,952.01171875,820.7553013392857,-1,0,3332597
1742416946,1742417081,135,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 920 n_samples 519 confidence 0.001 feature_proportion 0.2 n_clusters 1,920,519,0.001,0.2,1,0.6074641744548287,122.47852063179016,0,None,i7171,122.47852063179016,947.54296875,818.6276041666666,-1,0,3332570
1742416946,1742417086,140,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1919 n_samples 944 confidence 0.25 feature_proportion 0.2 n_clusters 4,1919,944,0.25,0.2,4,0.6039003115264797,124.91519856452942,0,None,i7153,124.91519856452942,978.80859375,834.8997395833334,-1,0,3332587
1742416959,1742417092,133,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 936 n_samples 539 confidence 0.001 feature_proportion 0.2 n_clusters 4,936,539,0.001,0.2,4,0.6023177570093458,122.92730808258057,0,None,i7161,122.92730808258057,942.23828125,817.4674479166666,-1,0,3332607
1742416976,1742417098,122,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1894 n_samples 1000 confidence 0.05 feature_proportion 0.2 n_clusters 1,1894,1000,0.05,0.2,1,0.6127352024922118,110.62496638298035,0,None,i7150,110.62496638298035,951.53125,823.6632254464286,-1,0,3332637
1742416979,1742417106,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1914 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 4,1914,1000,0.25,0.2,4,0.6040623052959502,116.31282925605774,0,None,i7175,116.31282925605774,958.45703125,824.5212053571429,-1,0,3332650
1742416947,1742417112,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1932 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 4,1932,1000,0.25,0.2,4,0.6063302180685358,154.3372097015381,0,None,i7183,154.3372097015381,956.078125,826.2204861111111,-1,0,3332566
1742417020,1742417444,424,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 838 n_samples 490 confidence 0.001 feature_proportion 0.2 n_clusters 1,838,490,0.001,0.2,1,0.6020809968847352,375.3563451766968,0,None,i7160,375.3563451766968,947.328125,821.06298828125,-1,0,3332619
1742419099,1742419113,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2832 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,2832,1000,0.001,0,1,None,None,1,None,i7183,3335073
1742419100,1742419113,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 424 n_samples 305 confidence 0.25 feature_proportion 0 n_clusters 4,424,305,0.25,0,4,None,None,1,None,i7186,3335070
1742419100,1742419113,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3411 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,3411,1000,0.25,0,4,None,None,1,None,i7186,3335071
1742419100,1742419113,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1907 n_samples 785 confidence 0.001 feature_proportion 0 n_clusters 1,1907,785,0.001,0,1,None,None,1,None,i7186,3335069
1742419107,1742419120,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3567 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,3567,1000,0.25,0,4,None,None,1,None,i7183,3335075
1742419107,1742419120,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1163 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,1163,1000,0.25,0,1,None,None,1,None,i7183,3335076
1742419107,1742419121,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1903 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,1903,1000,0.001,0,1,None,None,1,None,i7182,3335080
1742419120,1742419133,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2426 n_samples 922 confidence 0.001 feature_proportion 0 n_clusters 4,2426,922,0.001,0,4,None,None,1,None,i7183,3335085
1742419120,1742419133,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1334 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,1334,1000,0.001,0,1,None,None,1,None,i7186,3335082
1742419138,1742419151,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3572 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,3572,1000,0.001,0,1,None,None,1,None,i7175,3335097
1742419138,1742419151,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 157 n_samples 357 confidence 0.25 feature_proportion 0 n_clusters 2,157,357,0.25,0,2,None,None,1,None,i7175,3335095
1742419140,1742419153,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 305 n_samples 267 confidence 0.25 feature_proportion 0 n_clusters 2,305,267,0.25,0,2,None,None,1,None,i7183,3335109
1742419139,1742419153,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3412 n_samples 840 confidence 0.001 feature_proportion 0 n_clusters 4,3412,840,0.001,0,4,None,None,1,None,i7168,3335106
1742419137,1742419157,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 989 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,989,1000,0.25,0,1,None,None,1,None,i7171,3335102
1742419160,1742419173,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2576 n_samples 546 confidence 0.25 feature_proportion 0 n_clusters 4,2576,546,0.25,0,4,None,None,1,None,i7171,3335114
1742419167,1742419180,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1705 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,1705,1000,0.001,0,1,None,None,1,None,i7171,3335118
1742419107,1742419190,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2506 n_samples 597 confidence 0.25 feature_proportion 0.2 n_clusters 2,2506,597,0.25,0.2,2,0.5948785046728972,71.38886618614197,0,None,i7182,71.38886618614197,857.73828125,771.522265625,-1,0,3335079
1742419120,1742419209,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2822 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,2822,1000,0.001,0.2,4,0.5788909657320872,78.04843950271606,0,None,i7186,78.04843950271606,852.53125,764.51953125,-1,0,3335081
1742419166,1742419219,53,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 783 n_samples 904 confidence 0.25 feature_proportion 0 n_clusters 4,783,904,0.25,0,4,None,None,1,None,i7173,3335099
1742419099,1742419220,121,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2573 n_samples 540 confidence 0.25 feature_proportion 0.2 n_clusters 4,2573,540,0.25,0.2,4,0.5901806853582554,107.20571613311768,0,None,i7183,107.20571613311768,896.30859375,789.1096754807693,-1,0,3335072
1742419159,1742419224,65,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2356 n_samples 669 confidence 0.001 feature_proportion 0.2 n_clusters 1,2356,669,0.001,0.2,1,0.5458940809968847,51.36734485626221,0,None,i7175,51.36734485626221,798.64453125,742.126953125,-1,0,3335112
1742419140,1742419224,84,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 99 n_samples 401 confidence 0.25 feature_proportion 0.2 n_clusters 2,99,401,0.25,0.2,2,0.5150155763239875,66.38941621780396,0,None,i7176,66.38941621780396,738.80859375,709.7170138888889,-1,0,3335093
1742419137,1742419252,115,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 316 n_samples 376 confidence 0.25 feature_proportion 0.2 n_clusters 3,316,376,0.25,0.2,3,0.6048348909657321,100.23506045341492,0,None,i7169,100.23506045341492,858.51171875,772.0375600961538,-1,0,3335104
1742419107,1742419266,159,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 300 n_samples 165 confidence 0.25 feature_proportion 0.2 n_clusters 1,300,165,0.25,0.2,1,0.6206105919003115,148.32322072982788,0,None,i7183,148.32322072982788,958.5625,817.1050091911765,-1,0,3335074
1742419139,1742419267,128,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 121 n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,121,100,0.25,0.2,1,0.6449968847352024,111.87959718704224,0,None,i7185,111.87959718704224,892.07421875,779.828125,-1,0,3335089
1742419107,1742419272,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1910 n_samples 699 confidence 0.001 feature_proportion 0.2 n_clusters 1,1910,699,0.001,0.2,1,0.609993769470405,151.47026205062866,0,None,i7182,151.47026205062866,1023.18359375,855.6792534722222,-1,0,3335078
1742419138,1742419277,139,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3549 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,3549,1000,0.001,0.2,4,0.6066915887850467,126.14393472671509,0,None,i7183,126.14393472671509,924.58984375,802.8528645833334,-1,0,3335091
1742419122,1742419282,160,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3729 n_samples 744 confidence 0.25 feature_proportion 0.2 n_clusters 1,3729,744,0.25,0.2,1,0.6136448598130841,145.60398840904236,0,None,i7180,145.60398840904236,987.1640625,838.1330422794117,-1,0,3335087
1742419125,1742419290,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1713 n_samples 747 confidence 0.001 feature_proportion 0.2 n_clusters 1,1713,747,0.001,0.2,1,0.6141682242990654,149.68773341178894,0,None,i7186,149.68773341178894,982.08984375,830.3933823529412,-1,0,3335083
1742419160,1742419337,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3424 n_samples 653 confidence 0.25 feature_proportion 0.2 n_clusters 1,3424,653,0.25,0.2,1,0.6137445482866044,162.46566557884216,0,None,i7165,162.46566557884216,972.73828125,835.9886924342105,-1,0,3335116
1742421156,1742421169,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 153 n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,153,100,0.001,0,1,None,None,1,None,i7163,3341044
1742421156,1742421169,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1339 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,1339,1000,0.001,0,1,None,None,1,None,i7159,3341046
1742421156,1742421169,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2468 n_samples 939 confidence 0.001 feature_proportion 0 n_clusters 4,2468,939,0.001,0,4,None,None,1,None,i7164,3341039
1742421156,1742421169,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3421 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,3421,1000,0.001,0,4,None,None,1,None,i7171,3341036
1742421160,1742421173,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 123 n_samples 161 confidence 0.001 feature_proportion 0 n_clusters 1,123,161,0.001,0,1,None,None,1,None,i7151,3341056
1742421160,1742421173,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 947 n_samples 880 confidence 0.25 feature_proportion 0 n_clusters 4,947,880,0.25,0,4,None,None,1,None,i7146,3341059
1742421181,1742421195,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 631 n_samples 914 confidence 0.25 feature_proportion 0 n_clusters 4,631,914,0.25,0,4,None,None,1,None,i7184,3341063
1742421186,1742421199,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 46 n_samples 269 confidence 0.001 feature_proportion 0 n_clusters 1,46,269,0.001,0,1,None,None,1,None,i7146,3341083
1742421186,1742421199,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 125 n_samples 248 confidence 0.001 feature_proportion 0 n_clusters 1,125,248,0.001,0,1,None,None,1,None,i7171,3341070
1742421186,1742421199,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 633 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,633,1000,0.001,0,4,None,None,1,None,i7143,3341088
1742421186,1742421200,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1905 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,1905,1000,0.001,0,1,None,None,1,None,i7152,3341081
1742421187,1742421200,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 991 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 1,991,1000,0.001,0,1,None,None,1,None,i7165,3341072
1742421187,1742421200,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 781 n_samples 916 confidence 0.25 feature_proportion 0 n_clusters 4,781,916,0.25,0,4,None,None,1,None,i7159,3341077
1742421188,1742421201,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1170 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,1170,1000,0.25,0,1,None,None,1,None,i7154,3341079
1742421184,1742421204,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 322 n_samples 862 confidence 0.25 feature_proportion 0 n_clusters 2,322,862,0.25,0,2,None,None,1,None,i7180,3341066
1742421156,1742421207,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 222 n_samples 853 confidence 0.25 feature_proportion 0.2 n_clusters 1,222,853,0.25,0.2,1,0.4938566978193146,37.862650632858276,0,None,i7158,37.862650632858276,742.70703125,709.7552083333334,-1,0,3341049
1742421200,1742421213,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 382 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,382,1000,0.25,0,4,None,None,1,None,i7183,3341092
1742421217,1742421230,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1036 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,1036,1000,0.25,0,1,None,None,1,None,i7165,3341103
1742421218,1742421231,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2361 n_samples 848 confidence 0.001 feature_proportion 0 n_clusters 4,2361,848,0.001,0,4,None,None,1,None,i7162,3341105
1742421219,1742421232,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 431 n_samples 422 confidence 0.001 feature_proportion 0 n_clusters 4,431,422,0.001,0,4,None,None,1,None,i7184,3341098
1742421220,1742421233,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3393 n_samples 877 confidence 0.001 feature_proportion 0 n_clusters 4,3393,877,0.001,0,4,None,None,1,None,i7180,3341101
1742421221,1742421241,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 410 n_samples 211 confidence 0.25 feature_proportion 0 n_clusters 4,410,211,0.25,0,4,None,None,1,None,i7180,3341100
1742421186,1742421244,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2412 n_samples 940 confidence 0.001 feature_proportion 0.2 n_clusters 4,2412,940,0.001,0.2,4,0.5181682242990654,46.735487937927246,0,None,i7145,46.735487937927246,776.09765625,725.9547991071429,-1,0,3341085
1742421157,1742421245,88,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 117 n_samples 140 confidence 0.25 feature_proportion 0.2 n_clusters 1,117,140,0.25,0.2,1,0.6273644859813085,79.79699945449829,0,None,i7163,79.79699945449829,853.05078125,760.624609375,-1,0,3341042
1742421156,1742421270,114,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 401 n_samples 263 confidence 0.25 feature_proportion 0.2 n_clusters 4,401,263,0.25,0.2,4,0.6214454828660436,100.63986778259277,0,None,i7153,100.63986778259277,930.91796875,810.2145432692307,-1,0,3341052
1742421187,1742421284,97,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 972 n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,972,1000,0.001,0.2,4,0.5958006230529596,83.49857807159424,0,None,i7144,83.49857807159424,880.15234375,781.3149857954545,-1,0,3341086
1742421186,1742421288,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 396 n_samples 364 confidence 0.25 feature_proportion 0.2 n_clusters 4,396,364,0.25,0.2,4,0.608,87.65925598144531,0,None,i7163,87.65925598144531,895.1953125,785.8291903409091,-1,0,3341075
1742421248,1742421296,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 588 n_samples 394 confidence 0.001 feature_proportion 0 n_clusters 4,588,394,0.001,0,4,None,None,1,None,i7160,3341096
1742421186,1742421301,115,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1373 n_samples 871 confidence 0.25 feature_proportion 0.2 n_clusters 4,1373,871,0.25,0.2,4,0.6053457943925233,103.13276481628418,0,None,i7143,103.13276481628418,921.78515625,807.5213341346154,-1,0,3341090
1742421200,1742421303,103,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3411 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 4,3411,1000,0.25,0.2,4,0.5927601246105919,93.79290008544922,0,None,i7175,93.79290008544922,919.703125,802.5950520833334,-1,0,3341094
1742423121,1742423140,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 214 confidence 0.001 feature_proportion 0 n_clusters 1,10,214,0.001,0,1,None,None,1,None,i7128,3343092
1742423120,1742423140,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2384 n_samples 977 confidence 0.25 feature_proportion 0 n_clusters 1,2384,977,0.25,0,1,None,None,1,None,i7128,3343095
1742423120,1742423152,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 117 n_samples 233 confidence 0.001 feature_proportion 0 n_clusters 1,117,233,0.001,0,1,None,None,1,None,i7132,3343089
1742423141,1742423154,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 118 n_samples 619 confidence 0.001 feature_proportion 0 n_clusters 1,118,619,0.001,0,1,None,None,1,None,i7158,3343113
1742423141,1742423154,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 133 n_samples 184 confidence 0.25 feature_proportion 0 n_clusters 1,133,184,0.25,0,1,None,None,1,None,i7175,3343108
1742423128,1742423166,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 804 n_samples 742 confidence 0.25 feature_proportion 0 n_clusters 4,804,742,0.25,0,4,None,None,1,None,i7122,3343102
1742423154,1742423167,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 52 n_samples 216 confidence 0.25 feature_proportion 0 n_clusters 4,52,216,0.25,0,4,None,None,1,None,i7165,3343117
1742423153,1742423172,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2469 n_samples 690 confidence 0.001 feature_proportion 0 n_clusters 4,2469,690,0.001,0,4,None,None,1,None,i7150,3343119
1742423153,1742423186,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 931 n_samples 901 confidence 0.001 feature_proportion 0 n_clusters 4,931,901,0.001,0,4,None,None,1,None,i7110,3343149
1742423154,1742423192,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2319 n_samples 901 confidence 0.001 feature_proportion 0 n_clusters 4,2319,901,0.001,0,4,None,None,1,None,i7118,3343132
1742423183,1742423196,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 87 n_samples 266 confidence 0.001 feature_proportion 0 n_clusters 1,87,266,0.001,0,1,None,None,1,None,i7117,3343204
1742423184,1742423197,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 472 n_samples 312 confidence 0.25 feature_proportion 0 n_clusters 4,472,312,0.25,0,4,None,None,1,None,i7105,3343213
1742423184,1742423197,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 346 n_samples 691 confidence 0.25 feature_proportion 0 n_clusters 1,346,691,0.25,0,1,None,None,1,None,i7119,3343199
1742423121,1742423197,76,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 356 n_samples 695 confidence 0.25 feature_proportion 0.2 n_clusters 1,356,695,0.25,0.2,1,0.5541931464174454,56.767404079437256,0,None,i7150,56.767404079437256,763.5234375,721.35888671875,-1,0,3343106
1742423187,1742423207,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 346 n_samples 923 confidence 0.25 feature_proportion 0 n_clusters 4,346,923,0.25,0,4,None,None,1,None,i7162,3343179
1742423170,1742423208,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1901 n_samples 901 confidence 0.001 feature_proportion 0 n_clusters 4,1901,901,0.001,0,4,None,None,1,None,i7112,3343137
1742423170,1742423208,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 300 n_samples 290 confidence 0.001 feature_proportion 0 n_clusters 4,300,290,0.001,0,4,None,None,1,None,i7112,3343142
1742423174,1742423212,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3621 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,3621,1000,0.25,0,1,None,None,1,None,i7096,3343164
1742423175,1742423213,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 194 n_samples 626 confidence 0.001 feature_proportion 0 n_clusters 4,194,626,0.001,0,4,None,None,1,None,i7099,3343158
1742423153,1742423216,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 181 n_samples 543 confidence 0.001 feature_proportion 0.2 n_clusters 4,181,543,0.001,0.2,4,0.5210093457943925,44.554792642593384,0,None,i7127,44.554792642593384,682.9765625,661.52734375,-1,0,3343128
1742423188,1742423219,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 534 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,534,1000,0.25,0,4,None,None,1,None,i7093,3343220
1742423120,1742423228,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 912 n_samples 725 confidence 0.25 feature_proportion 0.2 n_clusters 4,912,725,0.25,0.2,4,0.6022928348909657,97.33438205718994,0,None,i7133,97.33438205718994,876.359375,773.51171875,-1,0,3343086
1742423153,1742423229,76,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 130 n_samples 900 confidence 0.25 feature_proportion 0.2 n_clusters 1,130,900,0.25,0.2,1,0.45622429906542056,32.43326163291931,0,None,i7138,32.43326163291931,694.49609375,678.5247395833334,-1,0,3343122
1742423197,1742423235,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 71 n_samples 208 confidence 0.25 feature_proportion 0 n_clusters 1,71,208,0.25,0,1,None,None,1,None,i7090,3343226
1742423197,1742423235,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 155 n_samples 898 confidence 0.001 feature_proportion 0 n_clusters 1,155,898,0.001,0,1,None,None,1,None,i7086,3343237
1742423168,1742423238,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 105 n_samples 209 confidence 0.25 feature_proportion 0.2 n_clusters 1,105,209,0.25,0.2,1,0.5873644859813084,54.15411949157715,0,None,i7175,54.15411949157715,793.8984375,735.8984375,-1,0,3343171
1742423196,1742423265,69,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 114 n_samples 614 confidence 0.001 feature_proportion 0.2 n_clusters 4,114,614,0.001,0.2,4,0.47814330218068535,34.885934352874756,0,None,i7088,34.885934352874756,710.50390625,686.990234375,-1,0,3343230
1742423184,1742423267,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2519 n_samples 695 confidence 0.001 feature_proportion 0.2 n_clusters 4,2519,695,0.001,0.2,4,0.5853457943925233,64.69295930862427,0,None,i7150,64.69295930862427,816.55078125,744.5933159722222,-1,0,3343185
1742423128,1742423280,152,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 150 n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 1,150,100,0.001,0.2,1,0.6352897196261682,112.99910306930542,0,None,i7127,112.99910306930542,838.43359375,746.9547991071429,-1,0,3343099
1742423185,1742423305,120,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3760 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 1,3760,1000,0.25,0.2,1,0.6058068535825545,107.33335971832275,0,None,i7131,107.33335971832275,898.33203125,789.765625,-1,0,3343194
1742425419,1742425432,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 97 n_samples 236 confidence 0.001 feature_proportion 0 n_clusters 1,97,236,0.001,0,1,None,None,1,None,i7159,3345375
1742425419,1742425432,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 21 n_samples 221 confidence 0.001 feature_proportion 0 n_clusters 1,21,221,0.001,0,1,None,None,1,None,i7155,3345387
1742425419,1742425432,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 142 n_samples 196 confidence 0.001 feature_proportion 0 n_clusters 1,142,196,0.001,0,1,None,None,1,None,i7158,3345382
1742425419,1742425432,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 195 n_samples 627 confidence 0.25 feature_proportion 0 n_clusters 4,195,627,0.25,0,4,None,None,1,None,i7153,3345393
1742425423,1742425436,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 311 n_samples 637 confidence 0.001 feature_proportion 0 n_clusters 1,311,637,0.001,0,1,None,None,1,None,i7183,3345398
1742425443,1742425456,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2322 n_samples 905 confidence 0.25 feature_proportion 0 n_clusters 4,2322,905,0.25,0,4,None,None,1,None,i7164,3345411
1742425444,1742425458,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1902 n_samples 890 confidence 0.001 feature_proportion 0 n_clusters 4,1902,890,0.001,0,4,None,None,1,None,i7185,3345405
1742425448,1742425461,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1146 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,1146,1000,0.25,0,1,None,None,1,None,i7158,3345428
1742425448,1742425461,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2508 n_samples 706 confidence 0.001 feature_proportion 0 n_clusters 4,2508,706,0.001,0,4,None,None,1,None,i7163,3345416
1742425449,1742425462,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 337 n_samples 278 confidence 0.001 feature_proportion 0 n_clusters 4,337,278,0.001,0,4,None,None,1,None,i7155,3345434
1742425449,1742425462,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 386 n_samples 905 confidence 0.001 feature_proportion 0 n_clusters 4,386,905,0.001,0,4,None,None,1,None,i7153,3345443
1742425449,1742425462,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 101 n_samples 209 confidence 0.25 feature_proportion 0 n_clusters 1,101,209,0.25,0,1,None,None,1,None,i7152,3345449
1742425450,1742425463,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 55 n_samples 189 confidence 0.25 feature_proportion 0 n_clusters 4,55,189,0.25,0,4,None,None,1,None,i7154,3345437
1742425450,1742425463,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 347 n_samples 614 confidence 0.25 feature_proportion 0 n_clusters 4,347,614,0.25,0,4,None,None,1,None,i7162,3345421
1742425449,1742425469,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 508 n_samples 950 confidence 0.25 feature_proportion 0 n_clusters 4,508,950,0.25,0,4,None,None,1,None,i7150,3345473
1742425463,1742425476,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 376 n_samples 724 confidence 0.001 feature_proportion 0 n_clusters 1,376,724,0.001,0,1,None,None,1,None,i7183,3345478
1742425453,1742425491,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 277 n_samples 298 confidence 0.001 feature_proportion 0 n_clusters 4,277,298,0.001,0,4,None,None,1,None,i7151,3345462
1742425453,1742425491,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2486 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,2486,1000,0.001,0,4,None,None,1,None,i7151,3345454
1742425479,1742425492,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2396 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,2396,1000,0.25,0,4,None,None,1,None,i7158,3345505
1742425479,1742425492,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 986 n_samples 911 confidence 0.25 feature_proportion 0 n_clusters 4,986,911,0.25,0,4,None,None,1,None,i7153,3345519
1742425479,1742425492,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 436 n_samples 261 confidence 0.25 feature_proportion 0 n_clusters 1,436,261,0.25,0,1,None,None,1,None,i7153,3345516
1742425479,1742425492,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2369 n_samples 926 confidence 0.001 feature_proportion 0 n_clusters 1,2369,926,0.001,0,1,None,None,1,None,i7152,3345522
1742425479,1742425492,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 271 n_samples 869 confidence 0.25 feature_proportion 0 n_clusters 4,271,869,0.25,0,4,None,None,1,None,i7171,3345489
1742425485,1742425498,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 122 n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,122,100,0.001,0,1,None,None,1,None,i7185,3345484
1742425453,1742425523,70,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 10 n_samples 227 confidence 0.001 feature_proportion 0.2 n_clusters 1,10,227,0.001,0.2,1,0.45852959501557633,29.603178024291992,0,None,i7151,29.603178024291992,701.15234375,693.11796875,-1,0,3345466
1742425453,1742425535,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2308 n_samples 852 confidence 0.001 feature_proportion 0.2 n_clusters 4,2308,852,0.001,0.2,4,0.5142679127725857,43.485713958740234,0,None,i7151,43.485713958740234,767.0546875,722.875,-1,0,3345459
1742425479,1742425580,101,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1350 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 1,1350,1000,0.25,0.2,1,0.5885981308411214,92.24845695495605,0,None,i7150,92.24845695495605,903.28515625,791.6555989583334,-1,0,3345528
1742425479,1742425581,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 987 n_samples 970 confidence 0.25 feature_proportion 0.2 n_clusters 1,987,970,0.25,0.2,1,0.5948161993769471,89.32465291023254,0,None,i7154,89.32465291023254,880.9921875,776.6590909090909,-1,0,3345511
1742425479,1742425581,102,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 118 n_samples 170 confidence 0.25 feature_proportion 0.2 n_clusters 1,118,170,0.25,0.2,1,0.610791277258567,84.36427211761475,0,None,i7164,84.36427211761475,825.41015625,752.5458096590909,-1,0,3345495
1742425480,1742425606,126,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1318 n_samples 948 confidence 0.001 feature_proportion 0.2 n_clusters 1,1318,948,0.001,0.2,1,0.5996760124610592,110.19505453109741,0,None,i7161,110.19505453109741,915.984375,802.439453125,-1,0,3345500
1742427759,1742427772,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 77 n_samples 231 confidence 0.25 feature_proportion 0 n_clusters 4,77,231,0.25,0,4,None,None,1,None,i7155,3347259
1742427759,1742427772,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 100 n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,100,0.25,0,1,None,None,1,None,i7158,3347256
1742427759,1742427772,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 244 n_samples 269 confidence 0.001 feature_proportion 0 n_clusters 2,244,269,0.001,0,2,None,None,1,None,i7153,3347269
1742427759,1742427772,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 146 n_samples 576 confidence 0.25 feature_proportion 0 n_clusters 4,146,576,0.25,0,4,None,None,1,None,i7153,3347267
1742427759,1742427772,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 106 n_samples 234 confidence 0.001 feature_proportion 0 n_clusters 1,106,234,0.001,0,1,None,None,1,None,i7161,3347253
1742427760,1742427773,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2373 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,2373,1000,0.001,0,4,None,None,1,None,i7154,3347263
1742427762,1742427775,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 298 n_samples 617 confidence 0.001 feature_proportion 0 n_clusters 4,298,617,0.001,0,4,None,None,1,None,i7169,3347272
1742427782,1742427795,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2315 n_samples 907 confidence 0.25 feature_proportion 0 n_clusters 4,2315,907,0.25,0,4,None,None,1,None,i7169,3347279
1742427783,1742427796,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 102 n_samples 193 confidence 0.25 feature_proportion 0 n_clusters 4,102,193,0.25,0,4,None,None,1,None,i7183,3347277
1742427783,1742427797,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 57 n_samples 255 confidence 0.001 feature_proportion 0 n_clusters 4,57,255,0.001,0,4,None,None,1,None,i7184,3347275
1742427789,1742427802,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 216 n_samples 917 confidence 0.25 feature_proportion 0 n_clusters 1,216,917,0.25,0,1,None,None,1,None,i7163,3347289
1742427789,1742427802,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 304 n_samples 909 confidence 0.25 feature_proportion 0 n_clusters 4,304,909,0.25,0,4,None,None,1,None,i7164,3347283
1742427789,1742427802,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 375 n_samples 297 confidence 0.001 feature_proportion 0 n_clusters 1,375,297,0.001,0,1,None,None,1,None,i7158,3347302
1742427789,1742427802,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1074 n_samples 678 confidence 0.001 feature_proportion 0 n_clusters 4,1074,678,0.001,0,4,None,None,1,None,i7163,3347287
1742427789,1742427802,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1903 n_samples 898 confidence 0.001 feature_proportion 0 n_clusters 1,1903,898,0.001,0,1,None,None,1,None,i7183,3347281
1742427789,1742427802,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 376 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,376,1000,0.25,0,1,None,None,1,None,i7163,3347285
1742427790,1742427803,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2506 n_samples 676 confidence 0.001 feature_proportion 0 n_clusters 4,2506,676,0.001,0,4,None,None,1,None,i7161,3347296
1742427789,1742427803,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 26 n_samples 203 confidence 0.25 feature_proportion 0 n_clusters 4,26,203,0.25,0,4,None,None,1,None,i7161,3347292
1742427802,1742427815,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 111 n_samples 310 confidence 0.001 feature_proportion 0 n_clusters 4,111,310,0.001,0,4,None,None,1,None,i7165,3347308
1742427803,1742427816,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 100 n_samples 122 confidence 0.25 feature_proportion 0 n_clusters 4,100,122,0.25,0,4,None,None,1,None,i7183,3347305
1742427819,1742427832,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 650 n_samples 938 confidence 0.25 feature_proportion 0 n_clusters 4,650,938,0.25,0,4,None,None,1,None,i7159,3347325
1742427819,1742427832,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2337 n_samples 960 confidence 0.25 feature_proportion 0 n_clusters 1,2337,960,0.25,0,1,None,None,1,None,i7158,3347329
1742427819,1742427832,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 209 n_samples 666 confidence 0.001 feature_proportion 0 n_clusters 1,209,666,0.001,0,1,None,None,1,None,i7164,3347314
1742427819,1742427832,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 104 n_samples 171 confidence 0.25 feature_proportion 0 n_clusters 1,104,171,0.25,0,1,None,None,1,None,i7163,3347317
1742427820,1742427833,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 396 n_samples 856 confidence 0.25 feature_proportion 0 n_clusters 4,396,856,0.25,0,4,None,None,1,None,i7161,3347321
1742427820,1742427833,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 400 n_samples 629 confidence 0.25 feature_proportion 0 n_clusters 4,400,629,0.25,0,4,None,None,1,None,i7183,3347311
1742427789,1742427847,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2492 n_samples 997 confidence 0.25 feature_proportion 0.2 n_clusters 4,2492,997,0.25,0.2,4,0.5449719626168225,48.596277475357056,0,None,i7159,48.596277475357056,788.5625,730.4363839285714,-1,0,3347299
1742427820,1742427879,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 370 n_samples 733 confidence 0.001 feature_proportion 0.2 n_clusters 1,370,733,0.001,0.2,1,0.5431526479750779,51.19123816490173,0,None,i7158,51.19123816490173,797.09765625,738.82568359375,-1,0,3347327
1742427820,1742427938,118,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 924 n_samples 929 confidence 0.25 feature_proportion 0.2 n_clusters 4,924,929,0.25,0.2,4,0.5913520249221184,102.4255735874176,0,None,i7161,102.4255735874176,880.62109375,784.1820913461538,-1,0,3347323
1742427821,1742427992,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1165 n_samples 918 confidence 0.25 feature_proportion 0.2 n_clusters 4,1165,918,0.25,0.2,4,0.5929844236760125,151.95300030708313,0,None,i7156,151.95300030708313,907.109375,800.0928819444445,-1,0,3347331
1742429663,1742429676,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1011 n_samples 403 confidence 0.001 feature_proportion 0 n_clusters 1,1011,403,0.001,0,1,None,None,1,None,i7172,3348745
1742429680,1742429693,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 953 n_samples 311 confidence 0.001 feature_proportion 0 n_clusters 4,953,311,0.001,0,4,None,None,1,None,i7158,3348765
1742429680,1742429693,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1088 n_samples 396 confidence 0.25 feature_proportion 0 n_clusters 4,1088,396,0.25,0,4,None,None,1,None,i7165,3348754
1742429680,1742429693,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1030 n_samples 417 confidence 0.001 feature_proportion 0 n_clusters 4,1030,417,0.001,0,4,None,None,1,None,i7186,3348751
1742429680,1742429693,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1000 n_samples 389 confidence 0.25 feature_proportion 0 n_clusters 4,1000,389,0.25,0,4,None,None,1,None,i7155,3348770
1742429680,1742429694,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2177 n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,2177,100,0.25,0,1,None,None,1,None,i7155,3348772
1742429680,1742429694,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 987 n_samples 374 confidence 0.001 feature_proportion 0 n_clusters 1,987,374,0.001,0,1,None,None,1,None,i7162,3348762
1742429683,1742429696,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1057 n_samples 391 confidence 0.001 feature_proportion 0 n_clusters 1,1057,391,0.001,0,1,None,None,1,None,i7175,3348779
1742429709,1742429722,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 713 n_samples 768 confidence 0.25 feature_proportion 0 n_clusters 4,713,768,0.25,0,4,None,None,1,None,i7158,3348793
1742429710,1742429723,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 890 n_samples 225 confidence 0.001 feature_proportion 0 n_clusters 4,890,225,0.001,0,4,None,None,1,None,i7145,3348804
1742429710,1742429723,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3326 n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,3326,100,0.25,0,1,None,None,1,None,i7145,3348807
1742429710,1742429723,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 536 n_samples 375 confidence 0.25 feature_proportion 0 n_clusters 4,536,375,0.25,0,4,None,None,1,None,i7146,3348800
1742429723,1742429736,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3455 n_samples 377 confidence 0.001 feature_proportion 0 n_clusters 4,3455,377,0.001,0,4,None,None,1,None,i7144,3348815
1742429723,1742429736,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 599 n_samples 590 confidence 0.25 feature_proportion 0 n_clusters 4,599,590,0.25,0,4,None,None,1,None,i7142,3348827
1742429723,1742429736,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1016 n_samples 439 confidence 0.25 feature_proportion 0 n_clusters 4,1016,439,0.25,0,4,None,None,1,None,i7143,3348822
1742429724,1742429737,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2120 n_samples 598 confidence 0.001 feature_proportion 0 n_clusters 1,2120,598,0.001,0,1,None,None,1,None,i7183,3348810
1742429740,1742429753,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3446 n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,3446,100,0.25,0,1,None,None,1,None,i7158,3348832
1742429740,1742429753,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 836 n_samples 105 confidence 0.001 feature_proportion 0 n_clusters 4,836,105,0.001,0,4,None,None,1,None,i7145,3348842
1742429651,1742429778,127,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1013 n_samples 404 confidence 0.001 feature_proportion 0.19482007168817614 n_clusters 4,1013,404,0.001,0.19482007168817614,4,None,None,1,None,i7175,3348743
1742429723,1742429817,94,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 95 n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,95,100,0.25,0.2,1,0.6473021806853583,79.56483101844788,0,None,i7143,79.56483101844788,871.06640625,768.10546875,-1,0,3348818
1742429680,1742429838,158,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1033 n_samples 419 confidence 0.001 feature_proportion 0.2 n_clusters 1,1033,419,0.001,0.2,1,0.6100934579439252,146.88075375556946,0,None,i7152,146.88075375556946,1007.79296875,849.0944393382352,-1,0,3348776
1742429663,1742429840,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1072 n_samples 398 confidence 0.001 feature_proportion 0.2 n_clusters 4,1072,398,0.001,0.2,4,0.6078753894080997,162.28846049308777,0,None,i7171,162.28846049308777,1032.8359375,855.9689555921053,-1,0,3348746
1742429703,1742429849,146,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1021 n_samples 443 confidence 0.001 feature_proportion 0.2 n_clusters 4,1021,443,0.001,0.2,4,0.6166105919003115,136.81450748443604,0,None,i7175,136.81450748443604,988.97265625,834.50341796875,-1,0,3348783
1742429680,1742429870,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1106 n_samples 388 confidence 0.001 feature_proportion 0.2 n_clusters 1,1106,388,0.001,0.2,1,0.615214953271028,178.09540581703186,0,None,i7163,178.09540581703186,1035.71875,855.2265625,-1,0,3348760
1742429703,1742429886,183,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1050 n_samples 402 confidence 0.001 feature_proportion 0.2 n_clusters 4,1050,402,0.001,0.2,4,0.6177320872274144,172.4730830192566,0,None,i7164,172.4730830192566,1018.00390625,850.2669921875,-1,0,3348788
1742429739,1742429898,159,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 998 n_samples 388 confidence 0.25 feature_proportion 0.2 n_clusters 1,998,388,0.25,0.2,1,0.6077383177570094,149.79200959205627,0,None,i7146,149.79200959205627,1017.22265625,844.1475183823529,-1,0,3348837
1742429680,1742429902,222,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1025 n_samples 414 confidence 0.25 feature_proportion 0.2 n_clusters 1,1025,414,0.25,0.2,1,0.6124361370716511,206.8427128791809,0,None,i7164,206.8427128791809,1004.84375,845.9867527173913,-1,0,3348757
1742429663,1742429911,248,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 976 n_samples 348 confidence 0.001 feature_proportion 0.2 n_clusters 4,976,348,0.001,0.2,4,0.621183800623053,235.38698744773865,0,None,i7170,235.38698744773865,1030.953125,862.4253305288462,-1,0,3348748
1742429710,1742430012,302,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3405 n_samples 286 confidence 0.001 feature_proportion 0.2 n_clusters 1,3405,286,0.001,0.2,1,0.6214454828660436,292.18354868888855,0,None,i7155,292.18354868888855,1161.171875,916.882568359375,-1,0,3348794
1742429680,1742430425,745,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3400 n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 1,3400,100,0.001,0.2,1,0.6083489096573209,729.4754655361176,0,None,i7155,729.4754655361176,1451.3984375,1113.330625,-1,0,3348768
1742432621,1742432634,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 83 n_samples 226 confidence 0.25 feature_proportion 0 n_clusters 4,83,226,0.25,0,4,None,None,1,None,i7172,3351082
1742432621,1742432634,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2330 n_samples 923 confidence 0.25 feature_proportion 0 n_clusters 1,2330,923,0.25,0,1,None,None,1,None,i7159,3351105
1742432621,1742432634,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1904 n_samples 872 confidence 0.001 feature_proportion 0 n_clusters 4,1904,872,0.001,0,4,None,None,1,None,i7158,3351108
1742432621,1742432634,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 429 n_samples 669 confidence 0.25 feature_proportion 0 n_clusters 4,429,669,0.25,0,4,None,None,1,None,i7153,3351126
1742432621,1742432634,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2418 n_samples 1000 confidence 0.001 feature_proportion 0 n_clusters 4,2418,1000,0.001,0,4,None,None,1,None,i7165,3351085
1742432621,1742432634,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 335 n_samples 917 confidence 0.001 feature_proportion 0 n_clusters 1,335,917,0.001,0,1,None,None,1,None,i7154,3351121
1742432644,1742432657,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4000 n_samples 696 confidence 0.001 feature_proportion 0 n_clusters 4,4000,696,0.001,0,4,None,None,1,None,i7186,3351136
1742432651,1742432663,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 352 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 1,352,1000,0.25,0,1,None,None,1,None,i7153,3351168
1742432651,1742432664,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2359 n_samples 1000 confidence 0.25 feature_proportion 0 n_clusters 4,2359,1000,0.25,0,4,None,None,1,None,i7161,3351153
1742432651,1742432664,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 19 n_samples 199 confidence 0.25 feature_proportion 0 n_clusters 1,19,199,0.25,0,1,None,None,1,None,i7152,3351172
1742432651,1742432664,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2486 n_samples 678 confidence 0.25 feature_proportion 0 n_clusters 1,2486,678,0.25,0,1,None,None,1,None,i7164,3351147
1742432651,1742432664,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 346 n_samples 656 confidence 0.001 feature_proportion 0 n_clusters 4,346,656,0.001,0,4,None,None,1,None,i7151,3351176
1742432623,1742432674,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 37 n_samples 225 confidence 0.001 feature_proportion 0.2 n_clusters 4,37,225,0.001,0.2,4,0.5152398753894081,37.491087436676025,0,None,i7175,37.491087436676025,723.78125,701.0052083333334,-1,0,3351131
1742432663,1742432676,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 81 n_samples 272 confidence 0.001 feature_proportion 0 n_clusters 4,81,272,0.001,0,4,None,None,1,None,i7163,3351199
1742432663,1742432677,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 416 n_samples 815 confidence 0.25 feature_proportion 0 n_clusters 1,416,815,0.25,0,1,None,None,1,None,i7165,3351196
1742432626,1742432683,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 92 n_samples 252 confidence 0.001 feature_proportion 0.2 n_clusters 1,92,252,0.001,0.2,1,0.5590654205607477,48.399779319763184,0,None,i7155,48.399779319763184,770.7109375,722.2260044642857,-1,0,3351116
1742432621,1742432684,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 292 n_samples 597 confidence 0.25 feature_proportion 0.2 n_clusters 4,292,597,0.25,0.2,4,0.5556261682242991,53.344884157180786,0,None,i7158,53.344884157180786,789.125,736.4658203125,-1,0,3351112
1742432681,1742432694,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 431 n_samples 934 confidence 0.25 feature_proportion 0 n_clusters 4,431,934,0.25,0,4,None,None,1,None,i7171,3351205
1742432681,1742432694,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2437 n_samples 398 confidence 0.25 feature_proportion 0 n_clusters 4,2437,398,0.25,0,4,None,None,1,None,i7183,3351202
1742432682,1742432695,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 85 n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,85,100,0.001,0,1,None,None,1,None,i7167,3351207
1742432621,1742432697,76,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 82 n_samples 210 confidence 0.25 feature_proportion 0.2 n_clusters 1,82,210,0.25,0.2,1,0.5762990654205608,60.44705033302307,0,None,i7164,60.44705033302307,774.6015625,729.52734375,-1,0,3351090
1742432643,1742432700,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 343 n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 4,343,1000,0.25,0.2,4,0.5136822429906542,45.64118480682373,0,None,i7164,45.64118480682373,755.859375,715.3465401785714,-1,0,3351142
1742432651,1742432702,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 125 n_samples 561 confidence 0.25 feature_proportion 0.2 n_clusters 1,125,561,0.25,0.2,1,0.4990155763239875,37.91976571083069,0,None,i7158,37.91976571083069,732.2109375,704.623046875,-1,0,3351158
1742432658,1742432703,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3128 n_samples 788 confidence 0.25 feature_proportion 0 n_clusters 4,3128,788,0.25,0,4,None,None,1,None,i7150,3351180
1742432652,1742432709,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 65 n_samples 221 confidence 0.001 feature_proportion 0.2 n_clusters 4,65,221,0.001,0.2,4,0.5602866043613707,45.2733588218689,0,None,i7155,45.2733588218689,733.54296875,707.4056919642857,-1,0,3351164
1742432621,1742432710,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 93 n_samples 115 confidence 0.25 feature_proportion 0.2 n_clusters 1,93,115,0.25,0.2,1,0.634816199376947,76.21061754226685,0,None,i7163,76.21061754226685,849.21875,762.50859375,-1,0,3351097
1742432607,1742432717,110,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 100 n_samples 178 confidence 0.25 feature_proportion 0.2 n_clusters 1,100,178,0.25,0.2,1,0.5997507788161994,95.64318561553955,0,None,i7168,95.64318561553955,805.8203125,740.322265625,-1,0,3351077
1742432622,1742432730,108,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 99 n_samples 143 confidence 0.25 feature_proportion 0.2 n_clusters 1,99,143,0.25,0.2,1,0.6222429906542056,89.99675178527832,0,None,i7162,89.99675178527832,833.42578125,754.5809659090909,-1,0,3351099
1742432658,1742432747,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 95 n_samples 202 confidence 0.001 feature_proportion 0.2 n_clusters 4,95,202,0.001,0.2,4,None,None,1,None,i7150,3351185
1742432658,1742432784,126,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 293 n_samples 293 confidence 0.001 feature_proportion 0.2 n_clusters 1,293,293,0.001,0.2,1,0.6147414330218068,81.97791123390198,0,None,i7150,81.97791123390198,884.96875,783.9808238636364,-1,0,3351191

Key	Value
config_yaml	None
config_toml	None
config_json	None
num_random_steps	20
max_eval	500
run_program	[['bW9kdWxlIGxvYWQgR0NDY29yZS8xMC4zLjAgUHl0aG9uICYmIHNvdXJjZSAvZGF0YS9ob3JzZS93cy9zNDEyMjQ4NS1jb21wUGVyZkREL2JlbmNobWFyay92ZW52L2Jpbi9hY3RpdmF0ZSAmJiBw…
experiment_name	CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACC-RUNTIME
mem_gb	32
parameter	[['recent_samples_size', 'range', '10', '4000', 'int'], ['n_samples', 'range', '100', '1000', 'int'], ['confidence', 'choice',
	'0.25,0.1,0.05,0.025,0.01,0.005,0.001'], ['feature_proportion', 'range', '0.0', '0.2', 'float'], ['n_clusters', 'range', '1', '4', 'int']]
continue_previous_job	None
maximize	False
experiment_constraints	None
stderr_to_stdout	False
run_dir	runs
seed	None
decimalrounding	12
enforce_sequential_optimization	False
verbose_tqdm	False
model	BOTORCH_MODULAR
gridsearch	False
occ	False
show_sixel_scatter	False
show_sixel_general	False
show_sixel_trial_index_result	False
follow	False
send_anonymized_usage_stats	True
ui_url	None
root_venv_dir	/home/s4122485
exclude	None
main_process_gb	8
pareto_front_confidence	1
max_nr_of_zero_results	50
disable_search_space_exhaustion_detection	False
abbreviate_job_names	False
orchestrator_file	None
checkout_to_latest_tested_version	False
live_share	False
disable_tqdm	False
workdir	False
max_parallelism	max_eval_times_thousand_plus_thousand
occ_type	euclid
result_names	['ACCURACY=max', 'RUNTIME=min']
minkowski_p	2
signed_weighted_euclidean_weights
generation_strategy	None
generate_all_jobs_at_once	True
revert_to_random_when_seemingly_exhausted	True
num_parallel_jobs	30
worker_timeout	30
slurm_use_srun	False
time	1260
partition	romeo
reservation	None
force_local_execution	False
slurm_signal_delay_s	0
nodes_per_job	1
cpus_per_task	1
account	None
gpus	0
run_mode	local
verbose	False
verbose_break_run_search_table	False
debug	False
no_sleep	False
tests	False
show_worker_percentage_table_at_end	False
auto_exclude_defective_hosts	False
run_tests_that_fail_on_taurus	False
raise_in_eval	False
show_ram_every_n_seconds	False

Skipped tabs:

Job-Infos
Single Logs

<!DOCTYPE html>
<html lang='en'>
	<head>
		<meta charset='UTF-8'>
		<meta name='viewport' content='width=device-width, initial-scale=1.0'>
		<title>Exported &raquo;s4122485/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACC-RUNTIME/0&laquo; from OmniOpt2-Share</title>
		<script src='https://code.jquery.com/jquery-3.7.1.js'></script>
		<script src='https://cdnjs.cloudflare.com/ajax/libs/gridjs/6.2.0/gridjs.production.min.js'></script>
		<script src='https://cdn.jsdelivr.net/npm/plotly.js-dist@3.0.1/plotly.min.js'></script>
		<link rel='stylesheet' href='https://cdnjs.cloudflare.com/ajax/libs/gridjs/6.2.0/theme/mermaid.css'>
		<style>
			#share_path {
				color: black;
			}
			
			.debug_log_pre {
				min-width: 300px;
			}
			
			body.dark-mode {
				background-color: #1e1e1e; color: #fff;
			}
			
			.plot-container {
				margin-bottom: 2rem;
			}
			
			.spinner {
				border: 4px solid #f3f3f3;
				border-top: 4px solid #3498db;
				border-radius: 50%;
				width: 40px;
				height: 40px;
				animation: spin 2s linear infinite;
				margin: auto;
			}
			
			@keyframes spin {
				0% { transform: rotate(0deg); }
				100% { transform: rotate(360deg); }
			}
			
			.tabs {
				margin-bottom: 20px;
			}
			
			.tab-content {
				display: none;
			}
			
			.tab-content.active {
				display: block;
			}
			
			pre {
				color: #00CC00 !important;
				background-color: black !important;
				font-family: monospace !important;
				line-break: anywhere;
			}
			
			menu[role="tablist"] {
				display: flex;
				flex-wrap: wrap;
				gap: 4px;
				max-width: 100%;
				max-height: 100px;
				overflow: scroll;
			}
			
			menu[role="tablist"] button {
				white-space: nowrap;
				min-width: 100px;
			}
			
			.container {
				max-width: 100% !important;
			}
			
			.gridjs-sort {
				min-width: 1px !important;
			}
			
			td.gridjs-td {
				overflow: clip;
			}
			
			.title-bar-text {
				font-size: 22px;
				display: block ruby;
			}
			
			.title-bar {
				height: fit-content;
			}
			
			.window {
				width: fit-content;
				min-width: 100%;
			}
			
			.top_link {
				display: inline-block;
				padding: 5px 5px;
				background-color: #007bff; /* Blau, kannst du anpassen */
				color: white;
				text-decoration: none;
				font-size: 16px;
				font-weight: bold;
				border-radius: 6px;
				border: 2px solid #0056b3;
				text-align: center;
				transition: all 0.3s ease-in-out;
			}
			
			.top_link:hover {
				background-color: #0056b3;
				border-color: #004494;
			}
			
			.top_link:active {
				background-color: #003366;
				border-color: #002244;
			}
			
			button {
				color: black;
			}
			
			.share_folder_buttons {
				width: fit-content;
			}
			
			button {
				background: #fcfcfe;
				border-color: #919b9c;
				border-top-color: rgb(145, 155, 156);
				border-bottom-color: rgb(145, 155, 156);
				margin-right: -1px;
				border-bottom: 1px solid transparent;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c;
			}
			
			button {
				padding-bottom: 2px;
				margin-top: -2px;
				background-color: #ece9d8;
				position: relative;
				z-index: 8;
				margin-left: -3px;
				margin-bottom: 1px;
			}
			
			.window {
				min-width: 1100px;
			}
			
			[role="tab"] {
				padding: 10px !important;
			}
			
			[role="tabpanel"] {
				min-width: fit-content;
			}
			
			select {
				border: 1px solid #7f9db9;
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				background-size: 15px;
				font-size: 11px;
				border: none;
				background-color: #fff;
				box-sizing: border-box;
				height: 21px;
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				position: relative;
				padding: 5px 32px 32px 5px;
				background-position: top 50% right 2px;
				background-repeat: no-repeat;
				border-radius: 0;
				border: 1px solid black;
			}
			

			body {
				font-variant: oldstyle-nums;
				font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
				background-color: #fafafa;
				text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
				scroll-behavior: smooth;
				text-wrap: balance;
				text-rendering: optimizeLegibility;
				-webkit-font-smoothing: antialiased;
				-moz-osx-font-smoothing: grayscale;
				font-feature-settings: "ss02", "liga", "onum";
			}
			
			.marked_text {
				background-color: yellow;
			}
			
			.time_picker_container {
				font-variant: small-caps;
				width: 100%;
			}
			
			.time_picker_container > input {
				width: 50px;
			}
			
			#loader {
				display: grid;
				justify-content: center;
				align-items: center;
				height: 100%;
			}
			
			.no_linebreak {
				line-break: auto;
			}
			
			.dark_code_bg {
				background-color: #363636;
				color: white;
			}
			
			.code_bg {
				background-color: #C0C0C0;
			}
			
			#commands {
				line-break: anywhere;
			}
			
			.color_red {
				color: red;
			}
			
			.color_orange {
				color: orange;
			}
			
			table > tbody > tr:nth-child(odd) {
				background-color: #fafafa;
			}
			
			table > tbody > tr:nth-child(even) {
				background-color: #ddd;
			}
			
			table {
				border-collapse: collapse;
				margin: 0 0;
				min-width: 200px;
			}
			
			th {
				background-color: #4eae46;
				color: #ffffff;
				text-align: left;
				border: 0px;
			}
			
			.error_element {
				background-color: #e57373;
				border-radius: 10px;
				padding: 4px;
				display: none;
			}
			
			button {
				background-color: #4eae46;
				border: 1px solid #2A8387;
				border-radius: 4px;
				box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
				cursor: pointer;
				display: block;
				line-height: 100%;
				outline: 0;
				padding: 11px 15px 12px;
				text-align: center;
				transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
				user-select: none;
				-webkit-user-select: none;
				touch-action: manipulation;
				font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
			}
			
			button:hover {
				box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
				text-decoration: none;
				transition-duration: .15s, .15s;
			}
			
			button:active {
				box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
			}
			
			button:disabled {
				cursor: not-allowed;
				opacity: .6;
			}
			
			button:disabled:active {
				pointer-events: none;
			}
			
			button:disabled:hover {
				box-shadow: none;
			}
			
			.half_width_td {
				vertical-align: baseline;
				width: 50%;
			}
			
			#scads_bar {
				width: 100%;
				margin: 0;
				padding: 0;
				user-select: none;
				user-drag: none;
				-webkit-user-drag: none;
				user-select: none;
				-moz-user-select: none;
				-webkit-user-select: none;
				-ms-user-select: none;
				display: -webkit-box;
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			.tab {
				display: inline-block;
				padding: 0px;
				margin: 0px;
				font-size: 16px;
				font-weight: bold;
				text-align: center;
				border-radius: 25px;
				text-decoration: none !important;
				transition: background-color 0.3s, color 0.3s;
				color: unset !important;
			}
			
			.tooltipster-base {
				border: 1px solid black;
				position: absolute;
				border-radius: 8px;
				padding: 2px;
				color: white;
				background-color: #61686f;
				width: 70%;
				min-width: 200px;
				pointer-events: none;
			}
			
			td {
				padding-top: 3px;
				padding-bottom: 3px;
			}
			
			.left_side {
				text-align: right;
			}
			
			.right_side {
				text-align: left;
			}
			
			.spinner {
				border: 8px solid rgba(0, 0, 0, 0.1);
				border-left: 8px solid #3498db;
				border-radius: 50%;
				width: 50px;
				height: 50px;
				animation: spin 1s linear infinite;
			}
			
			@keyframes spin {
				0% {
					transform: rotate(0deg);
				}
			
				100% {
					transform: rotate(360deg);
				}
			}
			
			#spinner-overlay {
				-webkit-text-stroke: 1px black;
				white !important;
				position: fixed;
				top: 0;
				left: 0;
				width: 100%;
				height: 100%;
				display: flex;
				justify-content: center;
				align-items: center;
				z-index: 9999;
			}
			
			#spinner-container {
				text-align: center;
				color: #fff;
				display: contents;
			}
			
			#spinner-text {
				font-size: 3vw;
				margin-left: 10px;
			}
			
			a, a:visited, a:active, a:hover, a:link {
				color: #007bff;
				text-decoration: none;
			}
			
			.copy-container {
				display: inline-block;
				position: relative;
				cursor: pointer;
				margin-left: 10px;
				color: blue;
			}
			
			.copy-container:hover {
				text-decoration: underline;
			}
			
			.clipboard-icon {
				position: absolute;
				top: 5px;
				right: 5px;
				font-size: 1.5em;
			}
			
			#main_tab {
				overflow: scroll;
				width: max-content;
			}
			
			.ui-tabs .ui-tabs-nav li {
				user-select: none;
			}
			
			.stacktrace_table {
				background-color: black !important;
				color: white !important;
			}
			
			#breadcrumb {
				user-select: none;
			}
			
			#statusBar {
				user-select: none;
			}
			
			.error_line {
				background-color: red !important;
				color: white !important;
			}
			
			.header_table {
				border: 0px !important;
				padding: 0px !important;
				width: revert !important;
				min-width: revert !important;
			}
			
			.img_auto_width {
				max-width: revert !important;
			}
			
			#main_dir_or_plot_view {
				display: inline-grid;
			}
			
			#refresh_button {
				width: 300px;
			}
			
			._share_link {
				color: black !important;
			}
			
			#footer_element {
				height: 30px;
				background-color: #f8f9fa;
				padding: 0px;
				text-align: center;
				border-top: 1px solid #dee2e6;
				width: 100%;
				box-sizing: border-box;
				position: fixed;
				bottom: 0;
				z-index: 2;
				margin-left: -9px;
				z-index: 99;
			}
			
			.switch {
				position: relative;
				display: inline-block;
				width: 50px;
				height: 26px;
			}
			
			.switch input {
				opacity: 0;
				width: 0;
				height: 0;
			}
			
			.slider {
				position: absolute;
				cursor: pointer;
				top: 0;
				left: 0;
				right: 0;
				bottom: 0;
				background-color: #ccc;
				transition: .4s;
				border-radius: 26px;
			}
			
			.slider:before {
				position: absolute;
				content: "";
				height: 20px;
				width: 20px;
				left: 3px;
				bottom: 3px;
				background-color: white;
				transition: .4s;
				border-radius: 50%;
			}
			
			input:checked + .slider {
				background-color: #444;
			}
			
			input:checked + .slider:before {
				transform: translateX(24px);
			}
			
			.mode-text {
				position: absolute;
				top: 5px;
				left: 65px;
				font-size: 14px;
				color: black;
				transition: .4s;
				width: 65px;
				display: block;
				font-size: 0.7rem;
				text-align: center;
			}
			
			input:checked + .slider .mode-text {
				content: "Dark Mode";
				color: white;
			}
			
			#mainContent {
				height: fit-content;
				min-height: 100%;
			}
			
			li {
				text-align: left;
			}
			
			#share_path {
				margin-bottom: 20px;
				margin-top: 20px;
			}
			
			#sortForm {
				margin-bottom: 20px;
			}
			
			.share_folder_buttons {
				margin-top: 10px;
				margin-bottom: 10px;
			}
			
			.nav_tab_button {
				margin: 10px;
			}
			
			.header_table {
				margin: 10px;
			}
			
			.no_border {
				border: unset !important;
			}
			
			.gui_table {
				padding: 5px !important;
			}
			
			.gui_parameter_row {
			}
			
			.gui_parameter_row_cell {
				border: unset !important;
			}
			
			.gui_param_table {
				width: 95%;
				margin: unset !important;
			}
			
			table td, table tr,
			.parameterRow table {
				padding: 2px !important;
			}
			
			.parameterRow table {
				margin: 0px;
				border: unset;
			}
			
			.parameterRow > td {
				border: 0px !important;
			}
			
			.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
				border: 0px !important;
			}
			
			.green_text {
				color: green;
			}
			
			.remove_parameter {
				white-space: pre;
			}
			
			select {
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				background-color: #fff;
				color: #222;
				padding: 5px 30px 5px 5px;
				border: 1px solid #555;
				border-radius: 5px;
				cursor: pointer;
				outline: none;
				transition: all 0.3s ease;
				background:
					url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
					no-repeat right 10px center,
				linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
				background-size: 12px, auto;
			}
			
			select:hover {
				border-color: #888;
			}
			
			select:focus {
				border-color: #4caf50;
				box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
			}
			
			select::-ms-expand {
				display: none;
			}
			
			input, textarea {
				border-radius: 5px;
			}
			
			#search {
				width: 200px;
				max-width: 70%;
				background-image: url(images/search.svg);
				background-repeat: no-repeat;
				background-size: auto 40px;
				height: 40px;
				line-height: 40px;
				padding-left: 40px;
				box-sizing: border-box;
			}
			
			input[type="checkbox"] {
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				width: 25px;
				height: 25px;
				border: 2px solid #3498db;
				border-radius: 5px;
				background-color: #fff;
				position: relative;
				cursor: pointer;
				transition: all 0.3s ease;
				width: 25px !important;
			}
			
			input[type="checkbox"]:checked {
				background-color: #3498db;
				border-color: #2980b9;
			}
			
			input[type="checkbox"]:checked::before {
				content: '✔';
				position: absolute;
				left: 4px;
				top: 2px;
				color: #fff;
			}
			
			input[type="checkbox"]:hover {
				border-color: #2980b9;
				background-color: #3caffc;
			}
			
			.toc {
				margin-bottom: 20px;
			}
			
			.toc li {
				margin-bottom: 5px;
			}
			
			.toc a {
				text-decoration: none;
				color: #007bff;
			}
			
			.toc a:hover {
				text-decoration: underline;
			}
			
			.table-container {
				width: 100%;
				overflow-x: auto;
			}
			
			.section-header {
				background-color: #1d6f9a !important;
				color: white;
			}
			
			.warning {
				color: red;
			}
			
			.li_list a {
				text-decoration: none;
			}
			
			.gridjs-td {
				white-space: nowrap;
			}
			
			th, td {
				border: 1px solid gray !important;
			}
			
			.no_border {
				border: 0px !important;
			}
			
			.no_break {
			
			}
			
			img {
				user-select: none;
				pointer-events: none;
			}
			
			#config_table, #hidden_config_table {
				user-select: none;
			}
			
			.copy_clipboard_button {
				margin-bottom: 10px;
			}
			
			.badge_table {
				background-color: unset !important;
			}
			
			.make_markable {
				user-select: text;
			}
			
			.header-container {
				display: flex;
				flex-wrap: wrap;
				align-items: center;
				justify-content: space-between;
				gap: 1rem;
				padding: 10px;
				background: var(--header-bg, #fff);
				border-bottom: 1px solid #ccc;
			}
			
			.header-logo-group {
				display: flex;
				gap: 1rem;
				align-items: center;
				flex: 1 1 auto;
				min-width: 200px;
			}
			
			.logo-img {
				max-height: 45px;
				height: auto;
				width: auto;
				object-fit: contain;
				pointer-events: unset;
			}
			
			.header-badges {
				flex-direction: column;
				gap: 5px;
				align-items: flex-start;
				flex: 0 1 auto;
				margin-top: auto;
				margin-bottom: auto;
			}
			
			.badge-img {
				height: auto;
				max-width: 130px;
				margin-top: 3px;
			}
			
			.header-tabs {
				margin-top: 10px;
				display: flex;
				flex-wrap: wrap;
				gap: 10px;
				flex: 2 1 100%;
				justify-content: center;
			}
			
			.nav-tab {
				display: inline-block;
				text-decoration: none;
				padding: 8px 16px;
				border-radius: 20px;
				background: linear-gradient(to right, #4a90e2, #357ABD);
				color: white;
				font-weight: bold;
				white-space: nowrap;
				transition: background 0.2s ease-in-out, transform 0.2s;
				box-shadow: 0 2px 4px rgba(0,0,0,0.2);
			}
			
			.nav-tab:hover {
				background: linear-gradient(to right, #5aa0f2, #4a90e2);
				transform: translateY(-2px);
			}
			
			.current-tag {
				padding-left: 10px;
				font-size: 0.9rem;
				color: #666;
			}
			
			.header-theme-toggle {
				flex: 1 1 auto;
				align-items: center;
				margin-top: 20px;
				min-width: 120px;
			}
			
			.switch {
				position: relative;
				display: inline-block;
				width: 60px;
				height: 30px;
			}
			
			.switch input {
				display: none;
			}
			
			.slider {
				position: absolute;
				top: 0; left: 0; right: 0; bottom: 0;
				background-color: #ccc;
				border-radius: 34px;
				cursor: pointer;
			}
			
			.slider::before {
				content: "";
				position: absolute;
				height: 24px;
				width: 24px;
				left: 3px;
				bottom: 3px;
				background-color: white;
				transition: .4s;
				border-radius: 50%;
			}
			
			input:checked + .slider {
				background-color: #2196F3;
			}
			
			input:checked + .slider::before {
				transform: translateX(30px);
			}
			
			@media (max-width: 768px) {
				.header-logo-group,
				.header-badges,
				.header-theme-toggle {
					justify-content: center;
					flex: 1 1 100%;
					text-align: center;
					width: inherit;
				}
			
				.logo-img {
					max-height: 50px;
					pointer-events: unset;
				}
			
				.badge-img {
					max-width: 100px;
				}
			
				.hide_on_mobile {
					display: none;
				}
			
				.nav-tab {
					font-size: 0.9rem;
					padding: 6px 12px;
				}
			
				.header_button {
					white-space: pre;
					font-size: 2em;
				}
			}
			
			.header_button {
				white-space: pre;
				margin-top: 20px;
				margin: 5px;
			}
			
			.line_break_anywhere {
				line-break: anywhere;
			}
			
			.responsive-container {
				display: flex;
				flex-wrap: wrap;
				justify-content: space-between;
				gap: 20px;
			}
			
			.responsive-container .half {
				flex: 1 1 48%;
				box-sizing: border-box;
				min-width: 500px;
			}
			
			.config-section table {
				width: 100%;
				border-collapse: collapse;
			}
			
			@media (max-width: 768px) {
				.responsive-container .half {
					flex: 1 1 100%;
				}
			}
			
			@keyframes spin {
				0% {
					transform: rotate(0deg);
				}
			
				100% {
					transform: rotate(360deg);
				}
			}
			
			.rotate {
				animation: spin 2s linear infinite;
				display: inline-block;
			}
			
			input::placeholder {
				font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
			}
			
			.gridjs-th-content {
				overflow: visible !important;
			}
			
			.error_text {
				color: red;
			}
			
			h1, h2, h3, h4, h5, h6 {
				margin-top: 1em;
				font-weight: bold;
				color: #333;
				border-left: 5px solid #ccc;
				padding-left: 0.5em;
			}
			
			.no_cursive {
				font-style: normal;	
			}
			
			.caveat {
				background-color: #fff8b3;
				border: 1px solid #f2d600;
				padding: 1em 1em 1em 70px;
				position: relative;
				font-family: sans-serif;
				color: #665500;
				margin: 1em 0;
				border-radius: 4px;
			}
			
			.caveat h1, .caveat h2, .caveat h3, .caveat h4 {
				margin-top: 0;
				margin-bottom: 0.5em;
				font-weight: bold;
			}
			
			.caveat::before {
				content: "⚠️";
				font-size: 50px;
				line-height: 1;
				position: absolute;
				left: 10px;
				top: 50%;
				transform: translateY(-50%);
				pointer-events: none;
				user-select: none;
			}
			
			.caveat.warning::before { content: "⚠️"; }
			.caveat.stop::before { content: "🛑"; }
			.caveat.exclamation::before { content: "❗"; }
			.caveat.alarm::before { content: "🚨"; }
			.caveat.tip::before { content: "💡"; }
			
			.tutorial_icon {
				display: inline-block;
				font-size: 1.3em;
				line-height: 1;
				vertical-align: middle;
				transform: translateY(-10%);
				padding: 0.2em 0;
			}
			
			.highlight {
				background-color: yellow;
				font-weight: bold;
			}
			
			#searchResults li {
				opacity: 0;
				transform: translateY(8px);
				animation: fadeInUp 0.3s ease-out forwards;
				animation-delay: 0.05s;
				list-style: none;
				margin-bottom: 5px;
			}
			
			@keyframes fadeInUp {
				to {
					opacity: 1;
					transform: translateY(0);
				}
			}
			
			.search_headline {
				font-weight: bold;
				margin-top: 1em;
				margin-bottom: 0.3em;
				color: #444;
			}
			
			.search_share_path {
				color: black;
				display: block ruby;
				margin-top: 20px;
			}
			
			@media print {
				#scads_bar {
					display: none !important;
				}
			}
			

			/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
			body{
				color: #222
			}
			
			.surface{
				background: #ece9d8
			}
			
			u{
				text-decoration: none;
				border-bottom: .5px solid #222
			}
			
			a{
				color: #00f
			}
			
			a: focus{
				outline: 1px dotted #00f
			}
			
			code,code *{
				font-family: monospace
			}
			
			pre{
				display: block;
				padding: 12px 8px;
				background-color: #000;
				color: silver;
				font-size: 1rem;
				margin: 0;
				overflow: scroll;
			}
			
			summary: focus{
				outline: 1px dotted #000
			}
			
			: :-webkit-scrollbar{
				width: 16px
			}
			
			: :-webkit-scrollbar: horizontal{
				height: 17px
			}
			
			: :-webkit-scrollbar-track{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-color: #dfdfdf;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
				display: block
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: end{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			button{
				border: none;
				background: #ece9d8;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
				border-radius: 0;
				min-width: 75px;
				min-height: 23px;
				padding: 0 12px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
			}
			
			button.focused,button: focus{
				outline: 1px dotted #000;
				outline-offset: -4px
			}
			
			label{
				display: inline-flex;
				align-items: center
			}
			
			textarea{
				padding: 3px 4px;
				border: none;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0
			}
			
			textarea: focus{
				outline: none
			}
			
			select: focus option{
				color: #000;
				background-color: #fff
			}
			
			.vertical-bar{
				width: 4px;
				height: 20px;
				background: silver;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			&: disabled,&: disabled+label{
				color: grey;
				text-shadow: 1px 1px 0 #fff
			}
			
			input[type=radio]+label{
				line-height: 13px;
				position: relative;
				margin-left: 19px
			}
			
			input[type=radio]+label: before{
				content: "";
				position: absolute;
				top: 0;
				left: -19px;
				display: inline-block;
				width: 13px;
				height: 13px;
				margin-right: 6px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: active+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: checked+label: after{
				content: "";
				display: block;
				width: 5px;
				height: 5px;
				top: 5px;
				left: -14px;
				position: absolute;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password]{
				padding: 3px 4px;
				border: 1px solid #7f9db9;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0;
				height: 21px;
				line-height: 2
			}
			
			input[type=email]: focus,input[type=password]: focus{
				outline: none
			}
			
			input[type=range]{
				-webkit-appearance: none;
				width: 100%;
				background: transparent
			}
			
			input[type=range]: focus{
				outline: none
			}
			
			input[type=range]: :-webkit-slider-thumb{
				-webkit-appearance: none;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range]: :-moz-range-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			.is-vertical{
				display: inline-block;
				width: 4px;
				height: 150px;
				transform: translateY(50%)
			}
			
			.is-vertical>input[type=range]{
				width: 150px;
				height: 4px;
				margin: 0 16px 0 10px;
				transform-origin: left;
				transform: rotate(270deg) translateX(calc(-50% + 8px))
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-thumb{
				transform: translateY(-8px) scaleX(-1)
			}
			
			.is-vertical>input[type=range]: :-moz-range-thumb{
				transform: translateY(2px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
				transform: translateY(-10px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
				transform: translateY(0) scaleX(-1)
			}
			
			.window{
				font-size: 11px;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
				background: #ece9d8;
				padding: 3px
			}
			
			.window fieldset{
				margin-bottom: 9px
			}
			
			.title-bar{
				background: #000;
				padding: 3px 2px 3px 3px;
				display: flex;
				justify-content: space-between;
				align-items: center
			}
			
			.title-bar-text{
				font-weight: 700;
				color: #fff;
				letter-spacing: 0;
				margin-right: 24px
			}
			
			.title-bar-controls button{
				padding: 0;
				display: block;
				min-width: 16px;
				min-height: 14px
			}
			
			.title-bar-controls button: focus{
				outline: none
			}
			
			.window-body{
				margin: 8px
			}
			
			.window-body pre{
				margin: -8px
			}
			
			.status-bar{
				margin: 0 1px;
				display: flex;
				gap: 1px
			}
			
			.status-bar-field{
				box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
				flex-grow: 1;
				padding: 2px 3px;
				margin: 0
			}
			
			ul.tree-view{
				display: block;
				background: #fff;
				padding: 6px;
				margin: 0
			}
			
			ul.tree-view li{
				list-style-type: none;
				margin-top: 3px
			}
			
			ul.tree-view a{
				text-decoration: none;
				color: #000
			}
			
			ul.tree-view a: focus{
				background-color: #2267cb;
				color: #fff
			}
			
			ul.tree-view ul{
				margin-top: 3px;
				margin-left: 16px;
				padding-left: 16px;
				border-left: 1px dotted grey
			}
			
			ul.tree-view ul>li{
				position: relative
			}
			
			ul.tree-view ul>li: before{
				content: "";
				display: block;
				position: absolute;
				left: -16px;
				top: 6px;
				width: 12px;
				border-bottom: 1px dotted grey
			}
			
			ul.tree-view ul>li: last-child: after{
				content: "";
				display: block;
				position: absolute;
				left: -20px;
				top: 7px;
				bottom: 0;
				width: 8px;
				background: #fff
			}
			
			ul.tree-view ul details>summary: before{
				margin-left: -22px;
				position: relative;
				z-index: 1
			}
			
			ul.tree-view details{
				margin-top: 0
			}
			
			ul.tree-view details>summary: before{
				text-align: center;
				display: block;
				float: left;
				content: "+";
				border: 1px solid grey;
				width: 8px;
				height: 9px;
				line-height: 9px;
				margin-right: 5px;
				padding-left: 1px;
				background-color: #fff
			}
			
			ul.tree-view details[open] summary{
				margin-bottom: 0
			}
			
			ul.tree-view details[open]>summary: before{
				content: "-"
			}
			
			fieldset{
				border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
				padding: 10px;
				padding-block-start: 8px;
				margin: 0
			}
			
			legend{
				background: #ece9d8
			}
			
			menu[role=tablist]{
				position: relative;
				margin: 0 0 -2px;
				text-indent: 0;
				list-style-type: none;
				display: flex;
				padding-left: 3px
			}
			
			menu[role=tablist] button{
				z-index: 1;
				display: block;
				color: #222;
				text-decoration: none;
				min-width: unset
			}
			
			menu[role=tablist] button[aria-selected=true]{
				padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
			}
			
			menu[role=tablist] button: focus{
				outline: 1px dotted #222;outline-offset: -4px
			}
			
			menu[role=tablist].justified button{
				flex-grow: 1;text-align: center
			}
			
			[role=tabpanel]{
				padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
			}
			
			: :-webkit-scrollbar{
				width: 17px
			}
			
			: :-webkit-scrollbar-corner{
				background: #dfdfdf
			}
			
			: :-webkit-scrollbar-track: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-track: horizontal{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-position: 50%;
				background-repeat: no-repeat;
				background-color: #c8d6fb;
				background-size: 7px;
				border: 1px solid #fff;
				border-radius: 2px;
				box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
			}
			
			: :-webkit-scrollbar-thumb: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb: horizontal{
				background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				height: 17px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 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			}
			
			: :-webkit-scrollbar-button: vertical: end{
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			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 17px;
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			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 17px;
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			}
			
			.window{
				box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
				border-top-left-radius: 8px;
				border-top-right-radius: 8px;
				padding: 0 0 3px;
				-webkit-font-smoothing: antialiased
			}
			
			.title-bar{
				background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
				padding: 3px 5px 3px 3px;
				border-top: 1px solid #0831d9;
				border-left: 1px solid #0831d9;
				border-right: 1px solid #001ea0;
				border-top-left-radius: 8px;
				border-top-right-radius: 7px;
				font-size: 13px;
				text-shadow: 1px 1px #0f1089;
				height: 21px
			}
			
			.title-bar-text{
				padding-left: 3px
			}
			
			.title-bar-controls{
				display: flex
			}
			
			.title-bar-controls button{
				min-width: 21px;
				min-height: 21px;
				margin-left: 2px;
				background-repeat: no-repeat;
				background-position: 50%;
				box-shadow: none;
				background-color: #0050ee;
				transition: background .1s;
				border: none
			}
			
			.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
				box-shadow: none!important
			}
			
			.title-bar-controls button[aria-label=Minimize]{
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stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
			}
			
			.status-bar{
				margin: 0 3px;
				box-shadow: inset 0 1px 2px grey;
				padding: 2px 1px;
				gap: 0
			}
			
			.status-bar-field{
				-webkit-font-smoothing: antialiased;
				box-shadow: none;
				padding: 1px 2px;
				border-right: 1px solid rgba(208,206,191,.75);
				border-left: 1px solid hsla(0,0%,100%,.75)
			}
			
			.status-bar-field: first-of-type{
				border-left: none
			}
			
			.status-bar-field: last-of-type{
				border-right: none
			}
			
			button{
				-webkit-font-smoothing: antialiased;
				box-sizing: border-box;
				border: 1px solid #003c74;
				background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
				box-shadow: none;
				border-radius: 3px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: none;
				background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
			}
			
			button: not(: disabled): hover{
				box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
			}
			
			button.focused,button: focus{
				box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
			}
			
			button: :-moz-focus-inner{
				border: 0
			}
			
			input,label,option,select,textarea{
				-webkit-font-smoothing: antialiased
			}
			
			input[type=radio]{
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				margin: 0;
				background: 0;
				position: fixed;
				opacity: 0;
				border: none
			}
			
			input[type=radio]+label{
				line-height: 16px
			}
			
			input[type=radio]+label: before{
				background: linear-gradient(135deg,#dcdcd7,#fff);
				border-radius: 50%;
				border: 1px solid #1d5281
			}
			
			input[type=radio]: not([disabled]): not(: active)+label: hover: before{
				box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
			}
			
			input[type=radio]: active+label: before{
				background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
			}
			
			input[type=radio]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: focus+label{
				outline: 1px dotted #000
			}
			
			input[type=radio][disabled]+label: before{
				border: 1px solid #cac8bb;
				background: #fff
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password],textarea: :selection{
				background: #2267cb;
				color: #fff
			}
			
			input[type=range]: :-webkit-slider-thumb{
				height: 21px;
				width: 11px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
				transform: translateY(-8px)
			}
			
			input[type=range]: :-moz-range-thumb{
				height: 21px;
				width: 11px;
				border: 0;
				border-radius: 0;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
				transform: translateY(2px)
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				width: 100%;
				height: 2px;
				box-sizing: border-box;
				background: #ecebe4;
				border-right: 1px solid #f3f2ea;
				border-bottom: 1px solid #f3f2ea;
				border-radius: 2px;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
			}
			
			input[type=range]: :-moz-range-track{
				width: 100%;
				height: 2px;
				box-sizing: border-box;
				background: #ecebe4;
				border-right: 1px solid #f3f2ea;
				border-bottom: 1px solid #f3f2ea;
				border-radius: 2px;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid #f3f2ea;
				border-right: 0;
				border-bottom: 1px solid #f3f2ea;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid #f3f2ea;
				border-right: 0;
				border-bottom: 1px solid #f3f2ea;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
			}
			
			fieldset{
				box-shadow: none;
				background: #fff;
				border: 1px solid #d0d0bf;
				border-radius: 4px;
				padding-top: 10px
			}
			
			legend{
				background: transparent;
				color: #0046d5
			}
			
			.field-row{
				display: flex;
				align-items: center
			}
			
			.field-row>*+*{
				margin-left: 6px
			}
			
			[class^=field-row]+[class^=field-row]{
				margin-top: 6px
			}
			
			.field-row-stacked{
				display: flex;
				flex-direction: column
			}
			
			.field-row-stacked *+*{
				margin-top: 6px
			}
			
			menu[role=tablist] button{
				background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
				margin-left: -1px;
				margin-right: 2px;
				border-radius: 0;
				border-color: #91a7b4;
				border-top-right-radius: 3px;
				border-top-left-radius: 3px;
				padding: 0 12px 3px
			}
			
			menu[role=tablist] button: hover{
				box-shadow: unset;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c
			}
			
			menu[role=tablist] button[aria-selected=true]{
				border-color: #919b9c;
				margin-right: -1px;
				border-bottom: 1px solid transparent;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c
			}
			
			menu[role=tablist] button[aria-selected=true]: first-of-type: before{
				content: "";
				display: block;
				position: absolute;
				z-index: -1;
				top: 100%;
				left: -1px;
				height: 2px;
				width: 0;
				border-left: 1px solid #919b9c
			}
			
			[role=tabpanel]{
				box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
			}
			
			ul.tree-view{
				-webkit-font-smoothing: auto;
				border: 1px solid #7f9db9;
				padding: 2px 5px
			}
			
			@keyframes sliding{
				0%{
					transform: translateX(-30px)
				}
			
				to{
					transform: translateX(100%)
				}
			}
			
			progress{
				box-sizing: border-box;
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				height: 14px;
				border: 1px solid #686868;
				border-radius: 4px;
				padding: 1px 2px 1px 0;
				overflow: hidden;
				background-color: #fff;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				-moz-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress,progress: not([value]){
				box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]){
				-moz-box-shadow: inset 0 0 1px 0 #686868;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				height: 14px
			}
			
			progress[value]: :-webkit-progress-bar{
				background-color: transparent
			}
			
			progress[value]: :-webkit-progress-value{
				border-radius: 2px;
				background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress[value]: :-moz-progress-bar{
				border-radius: 2px;
				background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress: not([value]): :-webkit-progress-bar{
				width: 100%;
				background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
				animation: sliding 2s linear 0s infinite
			}
			
			progress: not([value]): :-webkit-progress-bar: not([value]){
				animation: sliding 2s linear 0s infinite;
				background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress: not([value]){
				position: relative
			}
			
			progress: not([value]): before{
				box-sizing: border-box;
				content: "";
				position: absolute;
				top: 0;
				left: 0;
				width: 100%;
				height: 100%;
				background-color: #fff;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				-moz-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): before,progress: not([value]): before: not([value]){
				box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): before: not([value]){
				-moz-box-shadow: inset 0 0 1px 0 #686868;
				-webkit-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): after{
				box-sizing: border-box;
				content: "";
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	<body>
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			var special_col_names = ["trial_index","arm_name","trial_status","generation_method","generation_node","hostname","run_time","start_time","exit_code","signal","end_time","program_string"]

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			    [
			        1742429752,
			        804.95703125,
			        5.9
			    ],
			    [
			        1742429752,
			        804.95703125,
			        2.8
			    ],
			    [
			        1742429752,
			        804.95703125,
			        4.9
			    ],
			    [
			        1742429752,
			        804.95703125,
			        8.6
			    ],
			    [
			        1742432695,
			        757.5234375,
			        6.5
			    ],
			    [
			        1742432695,
			        757.5234375,
			        6.2
			    ],
			    [
			        1742432695,
			        757.5234375,
			        6.3
			    ],
			    [
			        1742432695,
			        757.5234375,
			        5.6
			    ],
			    [
			        1742432799,
			        757.41796875,
			        5.5
			    ],
			    [
			        1742432799,
			        757.41796875,
			        2.8
			    ]
			];
			var tab_main_worker_cpu_ram_headers_json = [
			    "timestamp",
			    "ram_usage_mb",
			    "cpu_usage_percent"
			];
			
			"use strict";
			
			function add_default_layout_data (layout, no_height = 0) {
				layout["width"] = get_graph_width();
				if (!no_height) {
					layout["height"] = get_graph_height();
				}
				layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
				layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
			
				return layout;
			}
			
			function get_marker_size() {
				return 12;
			}
			
			function get_text_color() {
				return theme == "dark" ? "white" : "black";
			}
			
			function get_font_size() {
				return 14;
			}
			
			function get_graph_height() {
				return 800;
			}
			
			function get_font_data() {
				return {
					size: get_font_size(),
					color: get_text_color()
				}
			}
			
			function get_axis_title_data(name, axis_type = "") {
				if(axis_type) {
					return {
						text: name,
						type: axis_type,
						font: get_font_data()
					};
				}
			
				return {
					text: name,
					font: get_font_data()
				};
			}
			
			function get_graph_width() {
				var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
				return Math.max(800, Math.floor(width * 0.9));
			}
			
			function createTable(data, headers, table_name) {
				if (!$("#" + table_name).length) {
					console.error("#" + table_name + " not found");
					return;
				}
			
				new gridjs.Grid({
					columns: headers,
					data: data,
					search: true,
					sort: true,
					ellipsis: false
				}).render(document.getElementById(table_name));
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			
				colorize_table_entries();
			
				add_colorize_to_gridjs_table();
			}
			
			function download_as_file(id, filename) {
				var text = $("#" + id).text();
				var blob = new Blob([text], {
					type: "text/plain"
				});
				var link = document.createElement("a");
				link.href = URL.createObjectURL(blob);
				link.download = filename;
				document.body.appendChild(link);
				link.click();
				document.body.removeChild(link);
			}
			
			function copy_to_clipboard_from_id (id) {
				var text = $("#" + id).text();
			
				copy_to_clipboard(text);
			}
			
			function copy_to_clipboard(text) {
				if (!navigator.clipboard) {
					let textarea = document.createElement("textarea");
					textarea.value = text;
					document.body.appendChild(textarea);
					textarea.select();
					try {
						document.execCommand("copy");
					} catch (err) {
						console.error("Copy failed:", err);
					}
					document.body.removeChild(textarea);
					return;
				}
			
				navigator.clipboard.writeText(text).then(() => {
					console.log("Text copied to clipboard");
				}).catch(err => {
					console.error("Failed to copy text:", err);
				});
			}
			
			function filterNonEmptyRows(data) {
				var new_data = [];
				for (var row_idx = 0; row_idx <  data.length; row_idx++) {
					var line = data[row_idx];
			
					var line_has_empty_data = false;
			
					for (var col_idx = 0; col_idx < line.length; col_idx++) {
						var col_header_name = tab_results_headers_json[col_idx];
						var single_data_point = line[col_idx];
			
						if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
							line_has_empty_data = true;
							continue;
						}
					}
			
					if(!line_has_empty_data) {
						new_data.push(line);
					}
				}
			
				return new_data;
			}
			
			function make_text_in_parallel_plot_nicer() {
				$(".parcoords g > g > text").each(function() {
					if (theme == "dark") {
						$(this)
							.css("text-shadow", "unset")
							.css("font-size", "0.9em")
							.css("fill", "white")
							.css("stroke", "black")
							.css("stroke-width", "2px")
							.css("paint-order", "stroke fill");
					} else {
						$(this)
							.css("text-shadow", "unset")
							.css("font-size", "0.9em")
							.css("fill", "black")
							.css("stroke", "unset")
							.css("stroke-width", "unset")
							.css("paint-order", "stroke fill");
					}
				});
			}
			
			function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
				if ($("#parallel-plot").data("loaded") == "true") {
					return;
				}
			
				dataArray = filterNonEmptyRows(dataArray);
				const ignoreSet = new Set(ignoreColumns);
				const numericalCols = [];
				const categoricalCols = [];
				const categoryMappings = {};
			
				headers.forEach((header, colIndex) => {
					if (ignoreSet.has(header)) return;
			
					const values = dataArray.map(row => row[colIndex]);
					if (values.every(val => !isNaN(parseFloat(val)))) {
						numericalCols.push({ name: header, index: colIndex });
					} else {
						categoricalCols.push({ name: header, index: colIndex });
						const uniqueValues = [...new Set(values)];
						categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
					}
				});
			
				const dimensions = [];
			
				numericalCols.forEach(col => {
					dimensions.push({
						label: col.name,
						values: dataArray.map(row => parseFloat(row[col.index])),
						range: [
							Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
							Math.max(...dataArray.map(row => parseFloat(row[col.index])))
						]
					});
				});
			
				categoricalCols.forEach(col => {
					dimensions.push({
						label: col.name,
						values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
						tickvals: Object.values(categoryMappings[col.name]),
						ticktext: Object.keys(categoryMappings[col.name])
					});
				});
			
				let colorScale = null;
				let colorValues = null;
			
				if (resultNames.length > 1) {
					let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
					selectBox += '<option value="none">No color</option>';
					var k = 0;
					resultNames.forEach(resultName => {
						var minMax = result_min_max[k];
						if(minMax === undefined) {
							minMax = "min [automatically chosen]"
						}
						selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
						k = k + 1;
					});
					selectBox += '</select>';
					$("#parallel-plot").before(selectBox);
			
					$("#result-select").change(function() {
						const selectedResult = $(this).val();
						if (selectedResult === "none") {
							colorValues = null;
							colorScale = null;
						} else {
							const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
							colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
			
							let minResult = Math.min(...colorValues);
							let maxResult = Math.max(...colorValues);
			
							var _result_min_max_idx = result_names.indexOf(selectedResult);
			
							let invertColor = false;
							if (result_min_max.length > _result_min_max_idx) {
								invertColor = result_min_max[_result_min_max_idx] === "max";
							}
			
							colorScale = invertColor
								? [[0, 'red'], [1, 'green']]
								: [[0, 'green'], [1, 'red']];
						}
						updatePlot();
					});
				} else {
					let invertColor = false;
					if (Object.keys(result_min_max).length == 1) {
						invertColor = result_min_max[0] === "max";
					}
			
					colorScale = invertColor
						? [[0, 'red'], [1, 'green']]
						: [[0, 'green'], [1, 'red']];
			
					const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
					colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
				}
			
				function updatePlot() {
					const trace = {
						type: 'parcoords',
						dimensions: dimensions,
						line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
						unselected: {
							line: {
								color: get_text_color(),
								opacity: 0
							}
						},
					};
			
					dimensions.forEach(dim => {
						if (!dim.line) {
							dim.line = {};
						}
						if (!dim.line.color) {
							dim.line.color = 'rgba(169,169,169, 0.01)';
						}
					});
			
					Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
			
					make_text_in_parallel_plot_nicer();
				}
			
				updatePlot();
			
				$("#parallel-plot").data("loaded", "true");
			
				make_text_in_parallel_plot_nicer();
			}
			
			function plotWorkerUsage() {
				if($("#workerUsagePlot").data("loaded") == "true") {
					return;
				}
				var data = tab_worker_usage_csv_json;
				if (!Array.isArray(data) || data.length === 0) {
					console.error("Invalid or empty data provided.");
					return;
				}
			
				let timestamps = [];
				let desiredWorkers = [];
				let realWorkers = [];
			
				for (let i = 0; i < data.length; i++) {
					let entry = data[i];
			
					if (!Array.isArray(entry) || entry.length < 3) {
						console.warn("Skipping invalid entry:", entry);
						continue;
					}
			
					let unixTime = parseFloat(entry[0]);
					let desired = parseInt(entry[1], 10);
					let real = parseInt(entry[2], 10);
			
					if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
						console.warn("Skipping invalid numerical values:", entry);
						continue;
					}
			
					timestamps.push(new Date(unixTime * 1000).toISOString());
					desiredWorkers.push(desired);
					realWorkers.push(real);
				}
			
				let trace1 = {
					x: timestamps,
					y: desiredWorkers,
					mode: 'lines+markers',
					name: 'Desired Workers',
					line: {
						color: 'blue'
					}
				};
			
				let trace2 = {
					x: timestamps,
					y: realWorkers,
					mode: 'lines+markers',
					name: 'Real Workers',
					line: {
						color: 'red'
					}
				};
			
				let layout = {
					title: "Worker Usage Over Time",
					xaxis: {
						title: get_axis_title_data("Time", "date")
					},
					yaxis: {
						title: get_axis_title_data("Number of Workers")
					},
					legend: {
						x: 0,
						y: 1
					}
				};
			
				Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
				$("#workerUsagePlot").data("loaded", "true");
			}
			
			function plotCPUAndRAMUsage() {
				if($("#mainWorkerCPURAM").data("loaded") == "true") {
					return;
				}
				var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
				var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
				var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
			
				var trace1 = {
					x: timestamps,
					y: cpuUsage,
					mode: 'lines+markers',
					marker: {
						size: get_marker_size(),
					},
					name: 'CPU Usage (%)',
					type: 'scatter',
					yaxis: 'y1'
				};
			
				var trace2 = {
					x: timestamps,
					y: ramUsage,
					mode: 'lines+markers',
					marker: {
						size: get_marker_size(),
					},
					name: 'RAM Usage (MB)',
					type: 'scatter',
					yaxis: 'y2'
				};
			
				var layout = {
					title: 'CPU and RAM Usage Over Time',
					xaxis: {
						title: get_axis_title_data("Timestamp", "date"),
						tickmode: 'array',
						tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
						ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
						tickangle: -45
					},
					yaxis: {
						title: get_axis_title_data("CPU Usage (%)"),
						rangemode: 'tozero'
					},
					yaxis2: {
						title: get_axis_title_data("RAM Usage (MB)"),
						overlaying: 'y',
						side: 'right',
						rangemode: 'tozero'
					},
					legend: {
						x: 0.1,
						y: 0.9
					}
				};
			
				var data = [trace1, trace2];
				Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
				$("#mainWorkerCPURAM").data("loaded", "true");
			}
			
			function plotScatter2d() {
				if ($("#plotScatter2d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter2d");
			
				var minInput = document.getElementById("minValue");
				var maxInput = document.getElementById("maxValue");
			
				if (!minInput || !maxInput) {
					minInput = document.createElement("input");
					minInput.id = "minValue";
					minInput.type = "number";
					minInput.placeholder = "Min Value";
					minInput.step = "any";
			
					maxInput = document.createElement("input");
					maxInput.id = "maxValue";
					maxInput.type = "number";
					maxInput.placeholder = "Max Value";
					maxInput.step = "any";
			
					var inputContainer = document.createElement("div");
					inputContainer.style.marginBottom = "10px";
					inputContainer.appendChild(minInput);
					inputContainer.appendChild(maxInput);
					plotDiv.appendChild(inputContainer);
				}
			
				var resultSelect = document.getElementById("resultSelect");
				if (result_names.length > 1 && !resultSelect) {
					resultSelect = document.createElement("select");
					resultSelect.id = "resultSelect";
					resultSelect.style.marginBottom = "10px";
			
					var sortedResults = [...result_names].sort();
					sortedResults.forEach(result => {
						var option = document.createElement("option");
						option.value = result;
						option.textContent = result;
						resultSelect.appendChild(option);
					});
			
					var selectContainer = document.createElement("div");
					selectContainer.style.marginBottom = "10px";
					selectContainer.appendChild(resultSelect);
					plotDiv.appendChild(selectContainer);
				}
			
				minInput.addEventListener("input", updatePlots);
				maxInput.addEventListener("input", updatePlots);
				if (resultSelect) {
					resultSelect.addEventListener("change", updatePlots);
				}
			
				updatePlots();
			
				async function updatePlots() {
					var minValue = parseFloat(minInput.value);
					var maxValue = parseFloat(maxInput.value);
					if (isNaN(minValue)) minValue = -Infinity;
					if (isNaN(maxValue)) maxValue = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var selectedResult = resultSelect ? resultSelect.value : result_names[0];
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
					if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						!col.startsWith("OO_Info") &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 2) {
						console.error("Not enough columns for Scatter-Plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							let xCol = numericColumns[i];
							let yCol = numericColumns[j];
			
							let xIndex = tab_results_headers_json.indexOf(xCol);
							let yIndex = tab_results_headers_json.indexOf(yCol);
			
							let data = tab_results_csv_json.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
							}));
			
							data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
							let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
							let layout = {
								title: layoutTitle,
								xaxis: {
									title: get_axis_title_data(xCol)
								},
								yaxis: {
									title: get_axis_title_data(yCol)
								},
								showlegend: false
							};
							let subDiv = document.createElement("div");
			
							let spinnerContainer = document.createElement("div");
							spinnerContainer.style.display = "flex";
							spinnerContainer.style.alignItems = "center";
							spinnerContainer.style.justifyContent = "center";
							spinnerContainer.style.width = layout.width + "px";
							spinnerContainer.style.height = layout.height + "px";
							spinnerContainer.style.position = "relative";
			
							let spinner = document.createElement("div");
							spinner.className = "spinner";
							spinner.style.width = "40px";
							spinner.style.height = "40px";
			
							let loadingText = document.createElement("span");
							loadingText.innerText = `Loading ${layoutTitle}`;
							loadingText.style.marginLeft = "10px";
			
							spinnerContainer.appendChild(spinner);
							spinnerContainer.appendChild(loadingText);
			
							plotDiv.appendChild(spinnerContainer);
			
							await new Promise(resolve => setTimeout(resolve, 50));
			
							let colors = data.map(d => {
								if (d.result === null) {
									return 'rgb(0, 0, 0)';
								} else {
									let norm = (d.result - minResult) / (maxResult - minResult);
									if (invertColor) {
										norm = 1 - norm;
									}
									return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
								}
							});
			
							let trace = {
								x: data.map(d => d.x),
								y: data.map(d => d.y),
								mode: 'markers',
								marker: {
									size: get_marker_size(),
									color: data.map(d => d.result !== null ? d.result : null),
									colorscale: invertColor ? [
										[0, 'red'],
										[1, 'green']
									] : [
										[0, 'green'],
										[1, 'red']
									],
									colorbar: {
										title: 'Result',
										tickvals: [minResult, maxResult],
										ticktext: [`${minResult}`, `${maxResult}`]
									},
									symbol: data.map(d => d.result === null ? 'x' : 'circle'),
								},
								text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
								type: 'scatter',
								showlegend: false
							};
			
							try {
								plotDiv.replaceChild(subDiv, spinnerContainer);
							} catch (err) {
								//
							}
							Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
						}
					}
				}
			
				$("#plotScatter2d").data("loaded", "true");
			}
			
			function plotScatter3d() {
				if ($("#plotScatter3d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter3d");
				if (!plotDiv) {
					console.error("Div element with id 'plotScatter3d' not found");
					return;
				}
				plotDiv.innerHTML = "";
			
				var minInput3d = document.getElementById("minValue3d");
				var maxInput3d = document.getElementById("maxValue3d");
			
				if (!minInput3d || !maxInput3d) {
					minInput3d = document.createElement("input");
					minInput3d.id = "minValue3d";
					minInput3d.type = "number";
					minInput3d.placeholder = "Min Value";
					minInput3d.step = "any";
			
					maxInput3d = document.createElement("input");
					maxInput3d.id = "maxValue3d";
					maxInput3d.type = "number";
					maxInput3d.placeholder = "Max Value";
					maxInput3d.step = "any";
			
					var inputContainer3d = document.createElement("div");
					inputContainer3d.style.marginBottom = "10px";
					inputContainer3d.appendChild(minInput3d);
					inputContainer3d.appendChild(maxInput3d);
					plotDiv.appendChild(inputContainer3d);
				}
			
				var select3d = document.getElementById("select3dScatter");
				if (result_names.length > 1 && !select3d) {
					if (!select3d) {
						select3d = document.createElement("select");
						select3d.id = "select3dScatter";
						select3d.style.marginBottom = "10px";
						select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
			
						select3d.addEventListener("change", updatePlots3d);
			
						plotDiv.appendChild(select3d);
					}
				}
			
				minInput3d.addEventListener("input", updatePlots3d);
				maxInput3d.addEventListener("input", updatePlots3d);
			
				updatePlots3d();
			
				async function updatePlots3d() {
					var selectedResult = select3d ? select3d.value : result_names[0];
					var minValue3d = parseFloat(minInput3d.value);
					var maxValue3d = parseFloat(maxInput3d.value);
			
					if (isNaN(minValue3d)) minValue3d = -Infinity;
					if (isNaN(maxValue3d)) maxValue3d = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
					if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						!col.startsWith("OO_Info") &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 3) {
						console.error("Not enough columns for 3D scatter plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							for (let k = j + 1; k < numericColumns.length; k++) {
								let xCol = numericColumns[i];
								let yCol = numericColumns[j];
								let zCol = numericColumns[k];
			
								let xIndex = tab_results_headers_json.indexOf(xCol);
								let yIndex = tab_results_headers_json.indexOf(yCol);
								let zIndex = tab_results_headers_json.indexOf(zCol);
			
								let data = tab_results_csv_json.map(row => ({
									x: parseFloat(row[xIndex]),
									y: parseFloat(row[yIndex]),
									z: parseFloat(row[zIndex]),
									result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
								}));
			
								data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
								let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
								let layout = {
									title: layoutTitle,
									scene: {
										xaxis: {
											title: get_axis_title_data(xCol)
										},
										yaxis: {
											title: get_axis_title_data(yCol)
										},
										zaxis: {
											title: get_axis_title_data(zCol)
										}
									},
									showlegend: false
								};
			
								let spinnerContainer = document.createElement("div");
								spinnerContainer.style.display = "flex";
								spinnerContainer.style.alignItems = "center";
								spinnerContainer.style.justifyContent = "center";
								spinnerContainer.style.width = layout.width + "px";
								spinnerContainer.style.height = layout.height + "px";
								spinnerContainer.style.position = "relative";
			
								let spinner = document.createElement("div");
								spinner.className = "spinner";
								spinner.style.width = "40px";
								spinner.style.height = "40px";
			
								let loadingText = document.createElement("span");
								loadingText.innerText = `Loading ${layoutTitle}`;
								loadingText.style.marginLeft = "10px";
			
								spinnerContainer.appendChild(spinner);
								spinnerContainer.appendChild(loadingText);
			
								plotDiv.appendChild(spinnerContainer);
			
								await new Promise(resolve => setTimeout(resolve, 50));
			
								let colors = data.map(d => {
									if (d.result === null) {
										return 'rgb(0, 0, 0)';
									} else {
										let norm = (d.result - minResult) / (maxResult - minResult);
										if (invertColor) {
											norm = 1 - norm;
										}
										return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
									}
								});
			
								let trace = {
									x: data.map(d => d.x),
									y: data.map(d => d.y),
									z: data.map(d => d.z),
									mode: 'markers',
									marker: {
										size: get_marker_size(),
										color: data.map(d => d.result !== null ? d.result : null),
										colorscale: invertColor ? [
											[0, 'red'],
											[1, 'green']
										] : [
											[0, 'green'],
											[1, 'red']
										],
										colorbar: {
											title: 'Result',
											tickvals: [minResult, maxResult],
											ticktext: [`${minResult}`, `${maxResult}`]
										},
									},
									text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
									type: 'scatter3d',
									showlegend: false
								};
			
								let subDiv = document.createElement("div");
								try {
									plotDiv.replaceChild(subDiv, spinnerContainer);
								} catch (err) {
									//
								}
								Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
							}
						}
					}
				}
			
				$("#plotScatter3d").data("loaded", "true");
			}
			
			async function plot_worker_cpu_ram() {
				if($("#worker_cpu_ram_pre").data("loaded") == "true") {
					return;
				}
			
				const logData = $("#worker_cpu_ram_pre").text();
				const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
			
				const hostData = {};
			
				logData.split("\n").forEach(line => {
					line = line.trim();
					const match = line.match(regex);
					if (match) {
						const timestamp = new Date(parseInt(match[1]) * 1000);
						const hostname = match[2];
						const cpu = parseFloat(match[3]);
						const ram = parseFloat(match[4]);
			
						if (!hostData[hostname]) {
							hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
						}
			
						hostData[hostname].timestamps.push(timestamp);
						hostData[hostname].cpuUsage.push(cpu);
						hostData[hostname].ramUsage.push(ram);
					}
				});
			
				if (!Object.keys(hostData).length) {
					console.log("No valid data found");
					return;
				}
			
				const container = document.getElementById("cpuRamWorkerChartContainer");
				container.innerHTML = "";
			
				var i = 1;
			
				Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
					const chartId = `workerChart_${index}`;
					const chartDiv = document.createElement("div");
					chartDiv.id = chartId;
					chartDiv.style.marginBottom = "40px";
					container.appendChild(chartDiv);
			
					const cpuTrace = {
						x: timestamps,
						y: cpuUsage,
						mode: "lines+markers",
						name: "CPU Usage (%)",
						yaxis: "y1",
						line: {
							color: "red"
						}
					};
			
					const ramTrace = {
						x: timestamps,
						y: ramUsage,
						mode: "lines+markers",
						name: "RAM Usage (MB)",
						yaxis: "y2",
						line: {
							color: "blue"
						}
					};
			
					const layout = {
						title: `Worker CPU and RAM Usage - ${hostname}`,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date")
						},
						yaxis: {
							title: get_axis_title_data("CPU Usage (%)"),
							side: "left",
							color: "red"
						},
						yaxis2: {
							title: get_axis_title_data("RAM Usage (MB)"),
							side: "right",
							overlaying: "y",
							color: "blue"
						},
						showlegend: true
					};
			
					Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
					i++;
				});
			
				$("#plot_worker_cpu_ram_button").remove();
				$("#worker_cpu_ram_pre").data("loaded", "true");
			}
			
			function load_log_file(log_nr, filename) {
				var pre_id = `single_run_${log_nr}_pre`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							if (data.data) {
								$("#" + pre_id).html(data.data);
								$("#" + pre_id).data("loaded", true);
							} else {
								log(`No 'data' key found in response.`);
							}
			
							$("#spinner_log_" + log_nr).remove();
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#spinner_log_" + log_nr).remove();
						});
				}
			}
			
			function load_debug_log () {
				var pre_id = `here_debuglogs_go`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							$("#debug_log_spinner").remove();
			
							if (data.data) {
								try {
									$("#" + pre_id).html(data.data);
								} catch (err) {
									$("#" + pre_id).text(`Error loading data: ${err}`);
								}
			
								$("#" + pre_id).data("loaded", true);
			
								if (typeof apply_theme_based_on_system_preferences === 'function') {
									apply_theme_based_on_system_preferences();
								}
							} else {
								log(`No 'data' key found in response.`);
							}
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#debug_log_spinner").remove();
						});
				}
			}
			
			function plotBoxplot() {
				if ($("#plotBoxplot").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					!col.startsWith("OO_Info") &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough numeric columns for Boxplot");
					return;
				}
			
				var resultIndex = tab_results_headers_json.findIndex(function(header) {
					return result_names.includes(header.toLowerCase());
				});
				var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
				var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
				var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
				var plotDiv = document.getElementById("plotBoxplot");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'box',
						name: col,
						boxmean: 'sd',
						marker: {
							color: 'rgb(0, 255, 0)'
						},
					};
				});
			
				let layout = {
					title: 'Boxplot of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Value")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotBoxplot").data("loaded", "true");
			}
			
			function plotHeatmap() {
				if ($("#plotHeatmap").data("loaded") === "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col => {
					if (special_col_names.includes(col) || result_names.includes(col)) {
						return false;
					}
					if (!col.startsWith("OO_Info")) {
						return;
					}
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.every(row => {
						let value = parseFloat(row[index]);
						return !isNaN(value) && isFinite(value);
					});
				});
			
				if (numericColumns.length < 2) {
					console.error("Not enough valid numeric columns for Heatmap");
					return;
				}
			
				var columnData = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.map(row => parseFloat(row[index]));
				});
			
				var dataMatrix = numericColumns.map((_, i) =>
					numericColumns.map((_, j) => {
						let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
						return values.reduce((a, b) => a + b, 0) / values.length;
					})
				);
			
				var trace = {
					z: dataMatrix,
					x: numericColumns,
					y: numericColumns,
					colorscale: 'Viridis',
					type: 'heatmap'
				};
			
				var layout = {
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				var plotDiv = document.getElementById("plotHeatmap");
				plotDiv.innerHTML = "";
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotHeatmap").data("loaded", "true");
			}
			
			function plotHistogram() {
				if ($("#plotHistogram").data("loaded") == "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					!col.startsWith("OO_Info") &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Histogram");
					return;
				}
			
				var plotDiv = document.getElementById("plotHistogram");
				plotDiv.innerHTML = "";
			
				const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
			
				let traces = numericColumns.map((col, index) => {
					let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
			
					return {
						x: data,
						type: 'histogram',
						name: col,
						opacity: 0.7,
						marker: {
							color: colorPalette[index % colorPalette.length]
						},
						autobinx: true
					};
				});
			
				let layout = {
					title: 'Histogram of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Value")
					},
					yaxis: {
						title: get_axis_title_data("Frequency")
					},
					showlegend: true,
					barmode: 'overlay'
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotHistogram").data("loaded", "true");
			}
			
			function plotViolin() {
				if ($("#plotViolin").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					!col.startsWith("OO_Info") &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Violin Plot");
					return;
				}
			
				var plotDiv = document.getElementById("plotViolin");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'violin',
						name: col,
						box: {
							visible: true
						},
						line: {
							color: 'rgb(0, 255, 0)'
						},
						marker: {
							color: 'rgb(0, 255, 0)'
						},
						meanline: {
							visible: true
						},
					};
				});
			
				let layout = {
					title: 'Violin Plot of Numerical Columns',
					yaxis: {
						title: get_axis_title_data("Value")
					},
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotViolin").data("loaded", "true");
			}
			
			function plotExitCodesPieChart() {
				if ($("#plotExitCodesPieChart").data("loaded") == "true") {
					return;
				}
			
				var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
			
				var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
					counts[exitCode] = (counts[exitCode] || 0) + 1;
					return counts;
				}, {});
			
				var labels = Object.keys(exitCodeCounts);
				var values = Object.values(exitCodeCounts);
			
				var plotDiv = document.getElementById("plotExitCodesPieChart");
				plotDiv.innerHTML = "";
			
				var trace = {
					labels: labels,
					values: values,
					type: 'pie',
					hoverinfo: 'label+percent',
					textinfo: 'label+value',
					marker: {
						colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
					}
				};
			
				var layout = {
					title: 'Exit Code Distribution',
					showlegend: true
				};
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotExitCodesPieChart").data("loaded", "true");
			}
			
			function plotResultEvolution() {
				if ($("#plotResultEvolution").data("loaded") == "true") {
					return;
				}
			
				result_names.forEach(resultName => {
					var relevantColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
					);
			
					var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
					var resultIndex = tab_results_headers_json.indexOf(resultName);
			
					let data = tab_results_csv_json.map(row => ({
						x: row[xColumnIndex],
						y: parseFloat(row[resultIndex])
					}));
			
					data.sort((a, b) => a.x - b.x);
			
					let xData = data.map(item => item.x);
					let yData = data.map(item => item.y);
			
					let trace = {
						x: xData,
						y: yData,
						mode: 'lines+markers',
						name: resultName,
						line: {
							shape: 'linear'
						},
						marker: {
							size: get_marker_size()
						}
					};
			
					let layout = {
						title: `Evolution of ${resultName} over time`,
						xaxis: {
							title: get_axis_title_data("Trial-Index")
						},
						yaxis: {
							title: get_axis_title_data(resultName)
						},
						showlegend: true
					};
			
					let subDiv = document.createElement("div");
					document.getElementById("plotResultEvolution").appendChild(subDiv);
			
					Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
				});
			
				$("#plotResultEvolution").data("loaded", "true");
			}
			
			function plotResultPairs() {
				if ($("#plotResultPairs").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotResultPairs");
				plotDiv.innerHTML = "";
			
				for (let i = 0; i < result_names.length; i++) {
					for (let j = i + 1; j < result_names.length; j++) {
						let xName = result_names[i];
						let yName = result_names[j];
			
						let xIndex = tab_results_headers_json.indexOf(xName);
						let yIndex = tab_results_headers_json.indexOf(yName);
			
						let data = tab_results_csv_json
							.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
							.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								status: row[tab_results_headers_json.indexOf("trial_status")]
							}));
			
						let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
			
						let trace = {
							x: data.map(d => d.x),
							y: data.map(d => d.y),
							mode: 'markers',
							marker: {
								size: get_marker_size(),
								color: colors
							},
							text: data.map(d => `Status: ${d.status}`),
							type: 'scatter',
							showlegend: false
						};
			
						let layout = {
							xaxis: {
								title: get_axis_title_data(xName)
							},
							yaxis: {
								title: get_axis_title_data(yName)
							},
							showlegend: false
						};
			
						let subDiv = document.createElement("div");
						plotDiv.appendChild(subDiv);
			
						Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
					}
				}
			
				$("#plotResultPairs").data("loaded", "true");
			}
			
			function add_up_down_arrows_for_scrolling () {
				const upArrow = document.createElement('div');
				const downArrow = document.createElement('div');
			
				const style = document.createElement('style');
				style.innerHTML = `
					.scroll-arrow {
						position: fixed;
						right: 10px;
						z-index: 100;
						cursor: pointer;
						font-size: 25px;
						display: none;
						background-color: green;
						color: white;
						padding: 5px;
						outline: 2px solid white;
						box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
						transition: background-color 0.3s, transform 0.3s;
					}
					.scroll-arrow:hover {
						background-color: darkgreen;
						transform: scale(1.1);
					}
					#up-arrow {
						top: 10px;
					}
					#down-arrow {
						bottom: 10px;
					}
				`;
				document.head.appendChild(style);
			
				upArrow.id = "up-arrow";
				upArrow.classList.add("scroll-arrow");
				upArrow.classList.add("invert_in_dark_mode");
				upArrow.innerHTML = "&#8593;";
			
				downArrow.id = "down-arrow";
				downArrow.classList.add("scroll-arrow");
				downArrow.classList.add("invert_in_dark_mode");
				downArrow.innerHTML = "&#8595;";
			
				document.body.appendChild(upArrow);
				document.body.appendChild(downArrow);
			
				function checkScrollPosition() {
					const scrollPosition = window.scrollY;
					const pageHeight = document.documentElement.scrollHeight;
					const windowHeight = window.innerHeight;
			
					if (scrollPosition > 0) {
						upArrow.style.display = "block";
					} else {
						upArrow.style.display = "none";
					}
			
					if (scrollPosition + windowHeight < pageHeight) {
						downArrow.style.display = "block";
					} else {
						downArrow.style.display = "none";
					}
				}
			
				window.addEventListener("scroll", checkScrollPosition);
			
				upArrow.addEventListener("click", function () {
					window.scrollTo({ top: 0, behavior: 'smooth' });
				});
			
				downArrow.addEventListener("click", function () {
					window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
				});
			
				checkScrollPosition();
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			}
			
			function plotGPUUsage() {
				if ($("#tab_gpu_usage").data("loaded") === "true") {
					return;
				}
			
				Object.keys(gpu_usage).forEach(node => {
					const nodeData = gpu_usage[node];
			
					var timestamps = [];
					var gpuUtilizations = [];
					var temperatures = [];
			
					nodeData.forEach(entry => {
						try {
							var timestamp = new Date(entry[0]* 1000);
							var utilization = parseFloat(entry[1]);
							var temperature = parseFloat(entry[2]);
			
							if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
								timestamps.push(timestamp);
								gpuUtilizations.push(utilization);
								temperatures.push(temperature);
							} else {
								console.warn("Invalid data point:", entry);
							}
						} catch (error) {
							console.error("Error processing GPU data entry:", error, entry);
						}
					});
			
					var trace1 = {
						x: timestamps,
						y: gpuUtilizations,
						mode: 'lines+markers',
						marker: {
							size: get_marker_size(),
						},
						name: 'GPU Utilization (%)',
						type: 'scatter',
						yaxis: 'y1'
					};
			
					var trace2 = {
						x: timestamps,
						y: temperatures,
						mode: 'lines+markers',
						marker: {
							size: get_marker_size(),
						},
						name: 'GPU Temperature (°C)',
						type: 'scatter',
						yaxis: 'y2'
					};
			
					var layout = {
						title: 'GPU Usage Over Time - ' + node,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date"),
							tickmode: 'array',
							tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
							ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
							tickangle: -45
						},
						yaxis: {
							title: get_axis_title_data("GPU Utilization (%)"),
							overlaying: 'y',
							rangemode: 'tozero'
						},
						yaxis2: {
							title: get_axis_title_data("GPU Temperature (°C)"),
							overlaying: 'y',
							side: 'right',
							position: 0.85,
							rangemode: 'tozero'
						},
						legend: {
							x: 0.1,
							y: 0.9
						}
					};
			
					var divId = 'gpu_usage_plot_' + node;
			
					if (!document.getElementById(divId)) {
						var div = document.createElement('div');
						div.id = divId;
						div.className = 'gpu-usage-plot';
						document.getElementById('tab_gpu_usage').appendChild(div);
					}
			
					var plotData = [trace1, trace2];
			
					Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
				});
			
				$("#tab_gpu_usage").data("loaded", "true");
			}
			
			function plotResultsDistributionByGenerationMethod() {
				if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
					return;
				}
			
				var res_col = result_names[0];
				var gen_method_col = "generation_node";
			
				var data = {};
			
				tab_results_csv_json.forEach(row => {
					var gen_method = row[tab_results_headers_json.indexOf(gen_method_col)];
					var result = row[tab_results_headers_json.indexOf(res_col)];
			
					if (!data[gen_method]) {
						data[gen_method] = [];
					}
					data[gen_method].push(result);
				});
			
				var traces = Object.keys(data).map(method => {
					return {
						y: data[method],
						type: 'box',
						name: method,
						boxpoints: 'outliers',
						jitter: 0.5,
						pointpos: 0
					};
				});
			
				var layout = {
					title: 'Distribution of Results by Generation Method',
					yaxis: {
						title: get_axis_title_data(res_col)
					},
					xaxis: {
						title: get_axis_title_data("Generation Method")
					},
					boxmode: 'group'
				};
			
				Plotly.newPlot("plotResultsDistributionByGenerationMethod", traces, add_default_layout_data(layout));
				$("#plotResultsDistributionByGenerationMethod").data("loaded", "true");
			}
			
			function plotJobStatusDistribution() {
				if ($("#plotJobStatusDistribution").data("loaded") === "true") {
					return;
				}
			
				var status_col = "trial_status";
				var status_counts = {};
			
				tab_results_csv_json.forEach(row => {
					var status = row[tab_results_headers_json.indexOf(status_col)];
					if (status) {
						status_counts[status] = (status_counts[status] || 0) + 1;
					}
				});
			
				var statuses = Object.keys(status_counts);
				var counts = Object.values(status_counts);
			
				var colors = statuses.map((status, i) =>
					status === "FAILED" ? "#FF0000" : `hsl(${30 + ((i * 137) % 330)}, 70%, 50%)`
				);
			
				var trace = {
					x: statuses,
					y: counts,
					type: 'bar',
					marker: { color: colors }
				};
			
				var layout = {
					title: 'Distribution of Job Status',
					xaxis: { title: 'Trial Status' },
					yaxis: { title: 'Nr. of jobs' }
				};
			
				Plotly.newPlot("plotJobStatusDistribution", [trace], add_default_layout_data(layout));
				$("#plotJobStatusDistribution").data("loaded", "true");
			}
			
			function _colorize_table_entries_by_generation_method () {
				document.querySelectorAll('[data-column-id="generation_node"]').forEach(el => {
					let text = el.textContent.toLowerCase();
					let color = text.includes("manual") ? "green" :
						text.includes("sobol") ? "orange" :
						text.includes("saasbo") ? "pink" :
						text.includes("uniform") ? "lightblue" :
						text.includes("legacy_gpei") ? "sienna" :
						text.includes("bo_mixed") ? "aqua" :
						text.includes("randomforest") ? "darkseagreen" :
						text.includes("external_generator") ? "purple" :
						text.includes("botorch") ? "yellow" : "";
					if (color !== "") {
						el.style.backgroundColor = color;
					}
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_trial_status () {
				document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
					let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
						el.textContent.includes("RUNNING") ? "orange" :
						el.textContent.includes("FAILED") ? "red" :
						el.textContent.includes("ABANDONED") ? "yellow" : "";
					if (color) el.style.backgroundColor = color;
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_run_time() {
				let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
				if (cells.length === 0) return;
			
				let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
				if (values.length === 0) return;
			
				let min = Math.min(...values);
				let max = Math.max(...values);
				let range = max - min || 1;
			
				cells.forEach(el => {
					let value = parseFloat(el.textContent);
					if (isNaN(value)) return;
			
					let ratio = (value - min) / range;
					let red = Math.round(255 * ratio);
					let green = Math.round(255 * (1 - ratio));
			
					el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_results() {
				result_names.forEach((name, index) => {
					let minMax = result_min_max[index];
					let selector_query = `[data-column-id="${name}"]`;
					let cells = [...document.querySelectorAll(selector_query)];
					if (cells.length === 0) return;
			
					let values = cells.map(el => parseFloat(el.textContent)).filter(v => v > 0 && !isNaN(v));
					if (values.length === 0) return;
			
					let logValues = values.map(v => Math.log(v));
					let logMin = Math.min(...logValues);
					let logMax = Math.max(...logValues);
					let logRange = logMax - logMin || 1;
			
					cells.forEach(el => {
						let value = parseFloat(el.textContent);
						if (isNaN(value) || value <= 0) return;
			
						let logValue = Math.log(value);
						let ratio = (logValue - logMin) / logRange;
						if (minMax === "max") ratio = 1 - ratio;
			
						let red = Math.round(255 * ratio);
						let green = Math.round(255 * (1 - ratio));
			
						el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
						el.classList.add("invert_in_dark_mode");
					});
				});
			}
			
			function _colorize_table_entries_by_generation_node_or_hostname() {
				["hostname", "generation_node"].forEach(element => {
					let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
					let cells = [...document.querySelectorAll(selector_query)];
					if (cells.length === 0) return;
			
					let uniqueValues = [...new Set(cells.map(el => el.textContent.trim()))];
					let colorMap = {};
			
					uniqueValues.forEach((value, index) => {
						let hue = Math.round((360 / uniqueValues.length) * index);
						colorMap[value] = `hsl(${hue}, 70%, 60%)`;
					});
			
					cells.forEach(el => {
						let value = el.textContent.trim();
						if (colorMap[value]) {
							el.style.backgroundColor = colorMap[value];
							el.classList.add("invert_in_dark_mode");
						}
					});
				});
			}
			
			function colorize_table_entries () {
				setTimeout(() => {
					if (typeof result_names !== "undefined" && Array.isArray(result_names) && result_names.length > 0) {
						_colorize_table_entries_by_trial_status();
						_colorize_table_entries_by_results();
						_colorize_table_entries_by_run_time();
						_colorize_table_entries_by_generation_method();
						_colorize_table_entries_by_generation_node_or_hostname();
						if (typeof apply_theme_based_on_system_preferences === 'function') {
							apply_theme_based_on_system_preferences();
						}
					}
				}, 300);
			}
			
			function add_colorize_to_gridjs_table () {
				let searchInput = document.querySelector(".gridjs-search-input");
				if (searchInput) {
					searchInput.addEventListener("input", colorize_table_entries);
				}
			}
			
			function updatePreWidths() {
				var width = window.innerWidth * 0.95;
				var pres = document.getElementsByTagName('pre');
				for (var i = 0; i < pres.length; i++) {
					pres[i].style.width = width + 'px';
				}
			}
			
			function demo_mode(nr_sec = 3) {
				let i = 0;
				let tabs = $('menu[role="tablist"] > button');
			
				setInterval(() => {
					tabs.attr('aria-selected', 'false').removeClass('active');
			
					let tab = tabs.eq(i % tabs.length);
					tab.attr('aria-selected', 'true').addClass('active');
			
					tab.trigger('click');
			
					i++;
				}, nr_sec * 1000);
			}
			
			function resizePlotlyCharts() {
				const plotlyElements = document.querySelectorAll('.js-plotly-plot');
			
				if (plotlyElements.length) {
					const windowWidth = window.innerWidth;
					const windowHeight = window.innerHeight;
			
					const newWidth = windowWidth * 0.9;
					const newHeight = windowHeight * 0.9;
			
					plotlyElements.forEach(function(element, index) {
						const layout = {
							width: newWidth,
							height: newHeight,
							plot_bgcolor: 'rgba(0, 0, 0, 0)',
							paper_bgcolor: 'rgba(0, 0, 0, 0)',
						};
			
						Plotly.relayout(element, layout)
					});
				}
			
				make_text_in_parallel_plot_nicer();
				apply_theme_based_on_system_preferences();
			}
			
			function plotTimelineFromGlobals() {
				if (
					typeof tab_results_headers_json === "undefined" ||
					typeof tab_results_csv_json === "undefined" ||
					!Array.isArray(tab_results_headers_json) ||
					!Array.isArray(tab_results_csv_json)
				) {
					console.warn("Global variables 'tab_results_headers_json' or 'tab_results_csv_json' missing or invalid.");
					return null;
				}
			
				const headers = tab_results_headers_json;
				const data = tab_results_csv_json;
			
				const col = name => headers.indexOf(name);
				const ix_trial_index = col("trial_index");
				const ix_start_time = col("start_time");
				const ix_end_time = col("end_time");
				const ix_status = col("trial_status");
			
				if ([ix_trial_index, ix_start_time, ix_end_time, ix_status].some(ix => ix === -1)) {
					console.warn("One or more needed columns missing");
					return null;
				}
			
				const traces = [];
			
				// Add dummy traces for legend
				traces.push({
					type: "scatter",
					mode: "lines",
					x: [null, null],
					y: [null, null],
					line: { color: "green", width: 4 },
					name: "COMPLETED",
					showlegend: true,
					hoverinfo: "none"
				});
				traces.push({
					type: "scatter",
					mode: "lines",
					x: [null, null],
					y: [null, null],
					line: { color: "yellow", width: 4 },
					name: "RUNNING",
					showlegend: true,
					hoverinfo: "none"
				});
				traces.push({
					type: "scatter",
					mode: "lines",
					x: [null, null],
					y: [null, null],
					line: { color: "red", width: 4 },
					name: "FAILED/OTHER",
					showlegend: true,
					hoverinfo: "none"
				});
			
				for (const row of data) {
					const trial_index = row[ix_trial_index];
					const start = row[ix_start_time];
					const end = row[ix_end_time];
					const status = row[ix_status];
			
					if (
						trial_index === "" || start === "" || end === "" ||
						isNaN(start) || isNaN(end)
					) continue;
			
					let color = "red"; // default
					if (status === "COMPLETED") color = "green";
					else if (status === "RUNNING") color = "yellow";
			
					traces.push({
						type: "scatter",
						mode: "lines",
						x: [new Date(start * 1000), new Date(end * 1000)],
						y: [trial_index, trial_index],
						line: { color: color, width: 4 },
						name: `Trial ${trial_index} (${status})`,
						showlegend: false,
						hoverinfo: "x+y+name"
					});
				}
			
				if (traces.length <= 3) { // only dummy traces added
					console.warn("No valid data for plotting found.");
					return null;
				}
			
				const layout = {
					title: "Trial Timeline",
					xaxis: {
						title: "Time",
						type: "date"
					},
					yaxis: {
						title: "Trial Index",
						autorange: "reversed"
					},
					margin: { t: 50 }
				};
			
				Plotly.newPlot('plot_timeline', traces, add_default_layout_data(layout));
				return true;
			}
			
			window.addEventListener('load', updatePreWidths);
			window.addEventListener('resize', updatePreWidths);
			
			$(document).ready(function() {
				colorize_table_entries();
			
				add_up_down_arrows_for_scrolling();
			
				add_colorize_to_gridjs_table();
			});
			
			window.addEventListener('resize', function() {
				resizePlotlyCharts();
			});
			
			"use strict";
			
			function get_row_by_index(idx) {
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
			
				var trial_index_col_idx = tab_results_headers_json.indexOf("trial_index");
			
				if(trial_index_col_idx == -1) {
					error(`"trial_index" could not be found in tab_results_headers_json. Cannot continue`);
			
					return null;
				}
			
				for (var i = 0; i < tab_results_csv_json.length; i++) {
					var row = tab_results_csv_json[i];
					var trial_index = row[trial_index_col_idx];
			
					if (trial_index == idx) {
						return row;
					}
				}
			
				return null;
			}
			
			function load_pareto_graph_from_idxs () {
				if (!Object.keys(window).includes("pareto_idxs")) {
					error("pareto_idxs is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
				if(pareto_idxs === null) {
					var err_msg = "pareto_idxs is null. Cannot plot or create tables from empty data. This can be caused by a defective <tt>pareto_idxs.json</tt> file. Please try reloading, or re-calculating the pareto-front and re-submitting if this problem persists.";
					$("#pareto_from_idxs_table").html(`<div class="caveat alarm">${err_msg}</div>`);
					return;	
				}
			
				var table = get_pareto_table_data_from_idx();
			
				var html_tables = createParetoTablesFromData(table);
			
				$("#pareto_from_idxs_table").html(html_tables);
			
				renderParetoFrontPlots(table);
			
				apply_theme_based_on_system_preferences();
			}
			
			function renderParetoFrontPlots(data) {
				try {
					let container = document.getElementById("pareto_front_idxs_plot_container");
					if (!container) {
						console.error("DIV with id 'pareto_front_idxs_plot_container' not found.");
						return;
					}
			
					container.innerHTML = "";
			
					if(data === undefined || data === null) {
						var err_msg = "There was an error getting the data for Pareto-Fronts. See the developer's console to see further details.";
						$("#pareto_from_idxs_table").html(`<div class="caveat alarm">${err_msg}</div>`);
						return;
					}
			
					Object.keys(data).forEach((key, idx) => {
						if (!key.startsWith("Pareto front for ")) return;
			
						let label = key.replace("Pareto front for ", "");
						let [xKey, yKey] = label.split("/");
			
						if (!xKey || !yKey) {
							console.warn("Could not extract two objectives from key:", key);
							return;
						}
			
						let entries = data[key];
						let x = [];
						let y = [];
						let hoverTexts = [];
			
						entries.forEach((entry) => {
							let results = entry.results || {};
							let values = entry.values || {};
			
							let xVal = (results[xKey] || [])[0];
							let yVal = (results[yKey] || [])[0];
			
							if (xVal === undefined || yVal === undefined) {
								console.warn("Missing values for", xKey, yKey, "in", entry);
								return;
							}
			
							x.push(xVal);
							y.push(yVal);
			
							let hoverInfo = [];
			
							if ("trial_index" in values) {
								hoverInfo.push(`<b>Trial Index:</b> ${values.trial_index[0]}`);
							}
			
							Object.keys(values)
								.filter(k => k !== "trial_index")
								.sort()
								.forEach(k => {
									hoverInfo.push(`<b>${k}:</b> ${values[k][0]}`);
								});
			
							Object.keys(results)
								.sort()
								.forEach(k => {
									hoverInfo.push(`<b>${k}:</b> ${results[k][0]}`);
								});
			
							hoverTexts.push(hoverInfo.join("<br>"));
						});
			
						let wrapper = document.createElement("div");
						wrapper.style.marginBottom = "30px";
			
						let titleEl = document.createElement("h3");
						titleEl.textContent = `Pareto Front: ${xKey} (${getMinMaxByResultName(xKey)}) vs ${yKey} (${getMinMaxByResultName(yKey)})`;
						wrapper.appendChild(titleEl);
			
						let divId = `pareto_plot_${idx}`;
						let plotDiv = document.createElement("div");
						plotDiv.id = divId;
						plotDiv.style.width = "100%";
						plotDiv.style.height = "400px";
			
						wrapper.appendChild(plotDiv);
						container.appendChild(wrapper);
			
						let trace = {
							x: x,
							y: y,
							text: hoverTexts,
							hoverinfo: "text",
							mode: "markers",
							type: "scatter",
							marker: {
								size: 8,
								color: 'rgb(31, 119, 180)',
								line: {
									width: 1,
									color: 'black'
								}
							},
							name: label
						};
			
						let layout = {
							xaxis: { title: { text: xKey } },
							yaxis: { title: { text: yKey } },
							margin: { t: 10, l: 60, r: 20, b: 50 },
							hovermode: "closest",
							showlegend: false
						};
			
						Plotly.newPlot(divId, [trace], add_default_layout_data(layout, 1));
					});
				} catch (e) {
					console.error("Error while rendering Pareto front plots:", e);
				}
			}
			
			function createParetoTablesFromData(data) {
				try {
					var container = document.createElement("div");
			
					var parsedData;
					try {
						parsedData = typeof data === "string" ? JSON.parse(data) : data;
					} catch (e) {
						console.error("JSON parsing failed:", e);
						return container;
					}
			
					for (var sectionTitle in parsedData) {
						if (!parsedData.hasOwnProperty(sectionTitle)) {
							continue;
						}
			
						var sectionData = parsedData[sectionTitle];
						var heading = document.createElement("h2");
						heading.textContent = sectionTitle;
						container.appendChild(heading);
			
						var table = document.createElement("table");
						table.style.borderCollapse = "collapse";
						table.style.marginBottom = "2em";
						table.style.width = "100%";
			
						var thead = document.createElement("thead");
						var headerRow = document.createElement("tr");
			
						var allValueKeys = new Set();
						var allResultKeys = new Set();
			
						sectionData.forEach(entry => {
							var values = entry.values || {};
							var results = entry.results || {};
			
							Object.keys(values).forEach(key => {
								allValueKeys.add(key);
							});
			
							Object.keys(results).forEach(key => {
								allResultKeys.add(key);
							});
						});
			
						var sortedValueKeys = Array.from(allValueKeys).sort();
						var sortedResultKeys = Array.from(allResultKeys).sort();
			
						if (sortedValueKeys.includes("trial_index")) {
							sortedValueKeys = sortedValueKeys.filter(k => k !== "trial_index");
							sortedValueKeys.unshift("trial_index");
						}
			
						var allColumns = [...sortedValueKeys, ...sortedResultKeys];
			
						allColumns.forEach(col => {
							var th = document.createElement("th");
							th.textContent = col;
							th.style.border = "1px solid black";
							th.style.padding = "4px";
							headerRow.appendChild(th);
						});
			
						thead.appendChild(headerRow);
						table.appendChild(thead);
			
						var tbody = document.createElement("tbody");
			
						sectionData.forEach(entry => {
							var tr = document.createElement("tr");
			
							allColumns.forEach(col => {
								var td = document.createElement("td");
								td.style.border = "1px solid black";
								td.style.padding = "4px";
			
								var value = null;
			
								if (col in entry.values) {
									value = entry.values[col];
								} else if (col in entry.results) {
									value = entry.results[col];
								}
			
								if (Array.isArray(value)) {
									td.textContent = value.join(", ");
								} else {
									td.textContent = value !== null && value !== undefined ? value : "";
								}
			
								tr.appendChild(td);
							});
			
							tbody.appendChild(tr);
						});
			
						table.appendChild(tbody);
						container.appendChild(table);
					}
			
					return container;
				} catch (err) {
					console.error("Unexpected error:", err);
					var errorDiv = document.createElement("div");
					errorDiv.textContent = "Error generating tables.";
					return errorDiv;
				}
			}
			
			function get_pareto_table_data_from_idx () {
				if (!Object.keys(window).includes("pareto_idxs")) {
					error("pareto_idxs is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
				var x_keys = Object.keys(pareto_idxs);
			
				var tables = {};
			
				for (var i = 0; i < x_keys.length; i++) {
					var x_key = x_keys[i];
					var y_keys = Object.keys(pareto_idxs[x_key]);
			
					for (var j = 0; j < y_keys.length; j++) {
						var y_key = y_keys[j];
			
						var indices = pareto_idxs[x_key][y_key];
			
						for (var k = 0; k < indices.length; k++) {
							var idx = indices[k];
							var row = get_row_by_index(idx);
			
							if(row === null) {
								error(`Error getting the row for index ${idx}`);
								return;
							}
			
							var row_dict = {
								"results": {},
								"values": {},
							};
			
							for (var l = 0; l < tab_results_headers_json.length; l++) {
								var header = tab_results_headers_json[l];
			
								if (!special_col_names.includes(header) || header == "trial_index") {
									var val = row[l];
			
									if (result_names.includes(header)) {
										if (!Object.keys(row_dict["results"]).includes(header)) {
											row_dict["results"][header] = [];
										}
			
										row_dict["results"][header].push(val);
									} else {
										if (!Object.keys(row_dict["values"]).includes(header)) {
											row_dict["values"][header] = [];
										}
										row_dict["values"][header].push(val);
									}
								}
								
							}
			
							var table_key = `Pareto front for ${x_key}/${y_key}`;
			
							if(!Object.keys(tables).includes(table_key)) {
								tables[table_key] = [];
							}
			
							tables[table_key].push(row_dict);
						}
			
					}
				}
			
				return tables;
			}
			
			function getMinMaxByResultName(resultName) {
				try {
					if (typeof resultName !== "string") {
						error("Parameter resultName must be a string");
						return;
					}
			
					if (!Array.isArray(result_names)) {
						error("Global variable result_names is not an array or undefined");
						return;
					}
					if (!Array.isArray(result_min_max)) {
						error("Global variable result_min_max is not an array or undefined");
						return;
					}
			
					if (result_names.length !== result_min_max.length) {
						error("Global arrays result_names and result_min_max must have the same length");
						return;
					}
			
					var index = result_names.indexOf(resultName);
					if (index === -1) {
						error("Result name '" + resultName + "' not found in result_names");
						return;
					}
			
					var minMaxValue = result_min_max[index];
			
					if (minMaxValue !== "min" && minMaxValue !== "max") {
						error("Value for result name '" + resultName + "' is invalid: expected 'min' or 'max'");
						return;
					}
			
					return minMaxValue;
			
				} catch (e) {
					error("Unexpected error: " + e.message);
				}
			}
			

			$(document).ready(function() {
				colorize_table_entries();;
				plotWorkerUsage();;
				plotCPUAndRAMUsage();;
				createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
				plotScatter2d();;
				plotScatter3d();
				plotJobStatusDistribution();;
				plotBoxplot();;
				plotViolin();;
				plotHistogram();;
				plotHeatmap();;
				plotResultPairs();

				colorize_table_entries();
			});
		</script>

					<h1>&nbsp;Overview</h1>
			<h2>Experiment overview:              </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Setting</th><th>Value           </th></tr></thead><tbody><tr><td> Model for non-random steps</td><td>BOTORCH_MODULAR </td></tr><tr><td> Max. nr. evaluations</td><td>500             </td></tr><tr><td> Number random steps</td><td>20              </td></tr><tr><td> Nr. of workers (parameter)</td><td>30              </td></tr><tr><td> Main process memory (GB)</td><td>8               </td></tr><tr><td> Worker memory (GB)</td><td>32              </td></tr></tbody></table><h2>Best RUNTIME, min (total: 239, failed: 269):                                         </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> ACCURACY</th><th>recent_samples_size</th><th>n_samples</th><th>confidence</th><th>feature_proportion</th><th>n_clusters</th><th>RUNTIME            </th></tr></thead><tbody><tr><td> 0.36183177570093455</td><td>2019</td><td>1000</td><td>0.001</td><td>0.2</td><td>4</td><td>26.830154180526733 </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><th>Log Scale? </th></tr></thead><tbody><tr><td> recent_samples_size</td><td>range</td><td>10</td><td>4000</td><td></td><td>int</td><td>No         </td></tr><tr><td> n_samples</td><td>range</td><td>100</td><td>1000</td><td></td><td>int</td><td>No         </td></tr><tr><td> confidence</td><td>choice</td><td></td><td></td><td>0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25</td><td></td><td></td></tr><tr><td> feature_proportion</td><td>range</td><td>0</td><td>0.2</td><td></td><td>float</td><td>No         </td></tr><tr><td> n_clusters</td><td>range</td><td>1</td><td>4</td><td></td><td>int</td><td>No         </td></tr></tbody></table><h2>Number of evaluations</h2>
			<table>
			<tbody>
			<tr>
			<th>Failed</th>
			<th>Succeeded</th>
			<th>Running</th>
			<th>Total</th>
			</tr>
			<tr>
			<td>269</td>
			<td>239</td>
			<td>2</td>
			<td>510</td>
			</tr>
			</tbody>
			</table>
			<h2>Result names and types</h2>
			<table>
			<tr><th>name</th><th>min/max</th></tr>
			<tr>
			<td>ACCURACY</td>
			<td>max</td>
			</tr>
			<tr>
			<td>RUNTIME</td>
			<td>min</td>
			</tr>
			</table>
			
			<h1>&nbsp;Pareto-Fronts-Estimation</h1>
			<div class='caveat warning'>The old algorithm for calculating the pareto-front was buggy. Please re-calculate them using <tt>bash omniopt --calculate_pareto_front_of_job runs/path_to_job/run_nr --live_share</tt>.</div><div id='pareto_front_graphs_container'></div>
<pre>                                     Pareto Frontier Results for ACCURACY/RUNTIME:                                     
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓
┃ recent_samples_size ┃ n_samples ┃ confidence ┃  feature_proportion  ┃ n_clusters ┃   ACCURACY    ┃     RUNTIME      ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩
│         114         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 98.361 &plusmn; 10.036  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         113         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 98.057 &plusmn; 10.053  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         257         │    100    │   0.001    │         0.2          │     4      │ 0.636 &plusmn; 0.004 │ 147.330 &plusmn; 12.067 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         113         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 98.057 &plusmn; 10.053  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         256         │    100    │   0.001    │         0.2          │     4      │ 0.636 &plusmn; 0.004 │ 146.958 &plusmn; 12.057 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         256         │    100    │   0.001    │         0.2          │     4      │ 0.636 &plusmn; 0.004 │ 146.958 &plusmn; 12.057 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         113         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 98.057 &plusmn; 10.053  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         254         │    100    │   0.001    │         0.2          │     4      │ 0.636 &plusmn; 0.004 │ 146.215 &plusmn; 12.038 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         254         │    100    │   0.001    │         0.2          │     4      │ 0.636 &plusmn; 0.004 │ 146.215 &plusmn; 12.038 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         113         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 98.057 &plusmn; 10.053  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         253         │    100    │   0.001    │         0.2          │     4      │ 0.636 &plusmn; 0.004 │ 145.844 &plusmn; 12.029 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         112         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.754 &plusmn; 10.070  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         112         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.754 &plusmn; 10.070  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         112         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.754 &plusmn; 10.070  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         251         │    100    │   0.001    │         0.2          │     4      │ 0.636 &plusmn; 0.004 │ 145.103 &plusmn; 12.011 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         250         │    100    │   0.001    │         0.2          │     4      │ 0.636 &plusmn; 0.004 │ 144.733 &plusmn; 12.003 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         112         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.754 &plusmn; 10.070  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         112         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.754 &plusmn; 10.070  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         112         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.754 &plusmn; 10.070  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         112         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.754 &plusmn; 10.070  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         247         │    100    │   0.001    │         0.2          │     3      │ 0.635 &plusmn; 0.004 │ 143.589 &plusmn; 11.281 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         111         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.452 &plusmn; 10.088  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         246         │    100    │   0.001    │         0.2          │     3      │ 0.635 &plusmn; 0.004 │ 143.219 &plusmn; 11.268 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         111         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.452 &plusmn; 10.088  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         111         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.452 &plusmn; 10.088  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         111         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.452 &plusmn; 10.088  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         111         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.452 &plusmn; 10.088  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         242         │    100    │   0.001    │         0.2          │     3      │ 0.635 &plusmn; 0.004 │ 141.742 &plusmn; 11.219 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         111         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.452 &plusmn; 10.088  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         240         │    100    │   0.001    │         0.2          │     2      │ 0.635 &plusmn; 0.004 │ 141.003 &plusmn; 11.137 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         110         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.150 &plusmn; 10.107  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         110         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.150 &plusmn; 10.107  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         110         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.150 &plusmn; 10.107  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         236         │    100    │   0.001    │         0.2          │     2      │ 0.635 &plusmn; 0.004 │ 139.529 &plusmn; 11.074 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         110         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.150 &plusmn; 10.107  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         110         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.150 &plusmn; 10.107  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         230         │    100    │   0.001    │         0.2          │     1      │ 0.635 &plusmn; 0.005 │ 137.368 &plusmn; 11.574 │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         110         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.150 &plusmn; 10.107  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         110         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.150 &plusmn; 10.107  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         110         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 97.150 &plusmn; 10.107  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         109         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.849 &plusmn; 10.126  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         108         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.548 &plusmn; 10.146  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         107         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 96.248 &plusmn; 10.166  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         106         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.949 &plusmn; 10.187  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.651 &plusmn; 10.209  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.353 &plusmn; 10.231  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.353 &plusmn; 10.231  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.353 &plusmn; 10.231  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.353 &plusmn; 10.231  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.353 &plusmn; 10.231  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.353 &plusmn; 10.231  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.353 &plusmn; 10.231  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.353 &plusmn; 10.231  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.055 &plusmn; 10.254  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.055 &plusmn; 10.254  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.055 &plusmn; 10.254  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.055 &plusmn; 10.254  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.055 &plusmn; 10.254  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.055 &plusmn; 10.254  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.055 &plusmn; 10.254  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    100    │    0.25    │         0.2          │     1      │ 0.642 &plusmn; 0.003 │ 95.055 &plusmn; 10.254  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.759 &plusmn; 10.277  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.759 &plusmn; 10.277  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.759 &plusmn; 10.277  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.759 &plusmn; 10.277  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.759 &plusmn; 10.277  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.759 &plusmn; 10.277  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.759 &plusmn; 10.277  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         282         │    265    │   0.001    │         0.2          │     4      │ 0.616 &plusmn; 0.004 │ 79.023 &plusmn; 10.090  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.463 &plusmn; 10.302  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.463 &plusmn; 10.302  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.463 &plusmn; 10.302  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.463 &plusmn; 10.302  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.463 &plusmn; 10.302  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         281         │    269    │   0.001    │         0.2          │     4      │ 0.616 &plusmn; 0.004 │ 77.762 &plusmn; 10.053  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.463 &plusmn; 10.302  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.167 &plusmn; 10.326  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.167 &plusmn; 10.326  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.167 &plusmn; 10.326  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.167 &plusmn; 10.326  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.167 &plusmn; 10.326  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.167 &plusmn; 10.326  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 94.167 &plusmn; 10.326  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         99          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.873 &plusmn; 10.352  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         99          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.873 &plusmn; 10.352  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         99          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.873 &plusmn; 10.352  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         99          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.873 &plusmn; 10.352  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         99          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.873 &plusmn; 10.352  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         99          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.873 &plusmn; 10.352  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.578 &plusmn; 10.378  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.578 &plusmn; 10.378  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.578 &plusmn; 10.378  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.578 &plusmn; 10.378  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.578 &plusmn; 10.378  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    100    │    0.25    │         0.2          │     1      │ 0.641 &plusmn; 0.003 │ 93.578 &plusmn; 10.378  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         97          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 93.285 &plusmn; 10.404  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         97          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 93.285 &plusmn; 10.404  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         97          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 93.285 &plusmn; 10.404  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         97          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 93.285 &plusmn; 10.404  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         97          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 93.285 &plusmn; 10.404  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         97          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 93.285 &plusmn; 10.404  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         96          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.992 &plusmn; 10.432  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         96          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.992 &plusmn; 10.432  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         96          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.992 &plusmn; 10.432  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         96          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.992 &plusmn; 10.432  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         96          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.992 &plusmn; 10.432  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         95          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.700 &plusmn; 10.459  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         95          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.700 &plusmn; 10.459  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         95          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.700 &plusmn; 10.459  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         95          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.700 &plusmn; 10.459  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         95          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.700 &plusmn; 10.459  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         94          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.408 &plusmn; 10.488  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         94          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.408 &plusmn; 10.488  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         94          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.408 &plusmn; 10.488  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         94          │    100    │    0.25    │         0.2          │     1      │ 0.640 &plusmn; 0.003 │ 92.408 &plusmn; 10.488  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         93          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 92.118 &plusmn; 10.517  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         93          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 92.118 &plusmn; 10.517  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         93          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 92.118 &plusmn; 10.517  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         93          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 92.118 &plusmn; 10.517  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         92          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 91.827 &plusmn; 10.547  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         92          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 91.827 &plusmn; 10.547  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         92          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 91.827 &plusmn; 10.547  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         268         │    293    │   0.001    │  0.1059849476573541  │     4      │ 0.608 &plusmn; 0.004 │ 69.397 &plusmn; 10.480  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         91          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 91.538 &plusmn; 10.577  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         91          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 91.538 &plusmn; 10.577  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         91          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 91.538 &plusmn; 10.577  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         91          │    100    │    0.25    │         0.2          │     1      │ 0.639 &plusmn; 0.003 │ 91.538 &plusmn; 10.577  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         93          │    120    │    0.25    │         0.2          │     1      │ 0.630 &plusmn; 0.003 │  84.491 &plusmn; 8.655  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         94          │    130    │    0.25    │         0.2          │     1      │ 0.625 &plusmn; 0.003 │  81.056 &plusmn; 8.151  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         96          │    140    │    0.25    │         0.2          │     1      │ 0.621 &plusmn; 0.003 │  77.983 &plusmn; 7.855  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         263         │    296    │   0.001    │ 0.06790312096412675  │     4      │ 0.606 &plusmn; 0.004 │ 67.767 &plusmn; 11.072  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         262         │    296    │   0.001    │ 0.06432735824415892  │     4      │ 0.606 &plusmn; 0.004 │ 67.575 &plusmn; 11.132  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    157    │    0.25    │         0.2          │     1      │ 0.614 &plusmn; 0.002 │  72.629 &plusmn; 7.744  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         99          │    165    │    0.25    │         0.2          │     1      │ 0.611 &plusmn; 0.002 │  70.263 &plusmn; 7.772  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    165    │    0.25    │         0.2          │     1      │ 0.611 &plusmn; 0.002 │  70.009 &plusmn; 7.790  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    173    │    0.25    │         0.2          │     2      │ 0.607 &plusmn; 0.002 │  67.760 &plusmn; 7.930  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    177    │    0.25    │         0.2          │     2      │ 0.606 &plusmn; 0.002 │  66.532 &plusmn; 7.944  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    180    │    0.25    │         0.2          │     2      │ 0.605 &plusmn; 0.002 │  65.872 &plusmn; 7.936  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    184    │    0.25    │         0.2          │     2      │ 0.603 &plusmn; 0.002 │  64.699 &plusmn; 7.948  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         256         │    299    │   0.001    │ 0.026936218714924003 │     4      │ 0.604 &plusmn; 0.005 │ 65.921 &plusmn; 11.889  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    190    │    0.25    │ 0.18579742932563867  │     2      │ 0.600 &plusmn; 0.002 │  63.097 &plusmn; 7.954  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    191    │    0.25    │ 0.17533589607439196  │     2      │ 0.599 &plusmn; 0.002 │  62.494 &plusmn; 8.007  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    196    │    0.25    │  0.1667615459641593  │     2      │ 0.598 &plusmn; 0.002 │  61.524 &plusmn; 8.024  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    202    │    0.25    │ 0.16487491826623216  │     3      │ 0.595 &plusmn; 0.003 │  59.983 &plusmn; 8.751  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    204    │    0.25    │ 0.15471845956305744  │     3      │ 0.594 &plusmn; 0.003 │  59.388 &plusmn; 8.804  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    205    │    0.25    │ 0.14841531537391567  │     3      │ 0.594 &plusmn; 0.003 │  59.085 &plusmn; 8.849  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    208    │    0.25    │ 0.14199094828335884  │     3      │ 0.592 &plusmn; 0.003 │  58.302 &plusmn; 8.881  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         103         │    209    │    0.25    │  0.1429473979507954  │     3      │ 0.591 &plusmn; 0.003 │  57.865 &plusmn; 8.874  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    213    │    0.25    │ 0.12416619106513659  │     3      │ 0.590 &plusmn; 0.003 │  57.195 &plusmn; 9.027  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    215    │    0.25    │ 0.12631983877679698  │     3      │ 0.589 &plusmn; 0.003 │  56.563 &plusmn; 8.984  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    217    │    0.25    │  0.1194178012542712  │     3      │ 0.588 &plusmn; 0.003 │  56.071 &plusmn; 9.054  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    219    │    0.25    │ 0.11370748032640474  │     3      │ 0.587 &plusmn; 0.003 │  55.601 &plusmn; 9.112  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    220    │    0.25    │  0.105860397210948   │     3      │ 0.587 &plusmn; 0.003 │  55.332 &plusmn; 9.224  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    223    │    0.25    │ 0.09877917493056806  │     3      │ 0.585 &plusmn; 0.003 │  54.669 &plusmn; 9.302  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    226    │    0.25    │ 0.09691604861731899  │     3      │ 0.584 &plusmn; 0.003 │  54.259 &plusmn; 9.281  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    226    │    0.25    │  0.0875941780581194  │     3      │ 0.584 &plusmn; 0.003 │  54.003 &plusmn; 9.465  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    229    │    0.25    │ 0.08427917147647507  │     3      │ 0.582 &plusmn; 0.003 │  53.419 &plusmn; 9.483  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    230    │    0.25    │ 0.07368847860349136  │     3      │ 0.582 &plusmn; 0.004 │  53.166 &plusmn; 9.686  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    231    │    0.1     │  0.0684939776972866  │     3      │ 0.581 &plusmn; 0.004 │  52.704 &plusmn; 9.522  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    236    │    0.1     │ 0.06377352661228687  │     4      │ 0.579 &plusmn; 0.004 │ 51.851 &plusmn; 10.552  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    238    │    0.05    │ 0.057412303967072566 │     4      │ 0.577 &plusmn; 0.004 │ 51.252 &plusmn; 10.486  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         105         │    240    │    0.05    │ 0.052522205765035905 │     4      │ 0.576 &plusmn; 0.004 │ 50.900 &plusmn; 10.554  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    241    │    0.05    │ 0.045113043117750495 │     4      │ 0.575 &plusmn; 0.004 │ 50.530 &plusmn; 10.712  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    244    │    0.05    │ 0.04516600465647614  │     4      │ 0.574 &plusmn; 0.004 │ 50.078 &plusmn; 10.646  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    246    │   0.025    │ 0.03485244460700634  │     4      │ 0.572 &plusmn; 0.005 │ 49.528 &plusmn; 10.809  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    247    │   0.025    │ 0.03242815997453843  │     4      │ 0.572 &plusmn; 0.005 │ 49.375 &plusmn; 10.849  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         104         │    248    │   0.025    │ 0.03188836701933606  │     4      │ 0.572 &plusmn; 0.005 │ 49.235 &plusmn; 10.842  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         102         │    249    │   0.025    │ 0.024173727452873103 │     4      │ 0.570 &plusmn; 0.005 │ 48.738 &plusmn; 11.032  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    250    │   0.025    │ 0.022622034831296073 │     4      │ 0.569 &plusmn; 0.005 │ 48.441 &plusmn; 11.058  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         101         │    253    │    0.01    │ 0.015107306210067612 │     4      │ 0.567 &plusmn; 0.005 │ 47.853 &plusmn; 11.261  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         100         │    253    │    0.01    │ 0.013024637729397846 │     4      │ 0.567 &plusmn; 0.005 │ 47.687 &plusmn; 11.323  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         99          │    255    │    0.01    │ 0.006690024290129336 │     4      │ 0.565 &plusmn; 0.005 │ 47.273 &plusmn; 11.461  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         98          │    256    │    0.01    │ 0.003127210005117575 │     4      │ 0.564 &plusmn; 0.005 │ 46.994 &plusmn; 11.546  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         97          │    258    │   0.005    │         0.0          │     4      │ 0.562 &plusmn; 0.005 │ 46.463 &plusmn; 11.766  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         96          │    259    │   0.005    │         0.0          │     4      │ 0.561 &plusmn; 0.005 │ 46.211 &plusmn; 11.754  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         94          │    260    │   0.005    │         0.0          │     4      │ 0.559 &plusmn; 0.005 │ 45.818 &plusmn; 11.748  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         93          │    261    │   0.005    │         0.0          │     4      │ 0.558 &plusmn; 0.005 │ 45.577 &plusmn; 11.737  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         91          │    262    │   0.005    │         0.0          │     4      │ 0.557 &plusmn; 0.005 │ 45.197 &plusmn; 11.733  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│        2343         │    946    │   0.001    │         0.2          │     4      │ 0.514 &plusmn; 0.007 │ 36.580 &plusmn; 11.012  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│        2342         │    946    │   0.001    │         0.2          │     4      │ 0.514 &plusmn; 0.007 │ 36.560 &plusmn; 11.012  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│        2341         │    946    │   0.001    │         0.2          │     4      │ 0.513 &plusmn; 0.007 │ 36.541 &plusmn; 11.013  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│        2341         │    946    │   0.001    │         0.2          │     4      │ 0.513 &plusmn; 0.007 │ 36.541 &plusmn; 11.013  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│        2340         │    947    │   0.001    │         0.2          │     4      │ 0.513 &plusmn; 0.007 │ 36.515 &plusmn; 11.001  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│        2339         │    947    │   0.001    │         0.2          │     4      │ 0.513 &plusmn; 0.007 │ 36.498 &plusmn; 11.002  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         218         │    939    │   0.005    │ 0.13754970812823716  │     1      │ 0.484 &plusmn; 0.006 │ 32.524 &plusmn; 11.587  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         215         │    939    │   0.005    │  0.1352517688448764  │     1      │ 0.484 &plusmn; 0.005 │ 32.427 &plusmn; 11.595  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         211         │    939    │   0.005    │ 0.13334900486171905  │     1      │ 0.483 &plusmn; 0.005 │ 32.305 &plusmn; 11.608  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         208         │    939    │   0.005    │ 0.13121572793086203  │     1      │ 0.482 &plusmn; 0.005 │ 32.218 &plusmn; 11.622  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         204         │    939    │   0.005    │  0.129161681498328   │     1      │ 0.481 &plusmn; 0.005 │ 32.108 &plusmn; 11.643  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         199         │    939    │    0.01    │ 0.12754074135103868  │     1      │ 0.479 &plusmn; 0.005 │ 31.960 &plusmn; 11.450  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         195         │    939    │    0.01    │  0.1254708356745268  │     1      │ 0.478 &plusmn; 0.005 │ 31.865 &plusmn; 11.480  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         190         │    939    │    0.01    │ 0.12378676018195975  │     1      │ 0.476 &plusmn; 0.005 │ 31.757 &plusmn; 11.523  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         183         │    940    │    0.01    │ 0.12199145994923392  │     1      │ 0.473 &plusmn; 0.004 │ 31.620 &plusmn; 11.553  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         178         │    939    │    0.01    │ 0.12050676371694279  │     1      │ 0.471 &plusmn; 0.004 │ 31.541 &plusmn; 11.657  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         169         │    939    │    0.01    │ 0.11915949989043428  │     1      │ 0.468 &plusmn; 0.004 │ 31.422 &plusmn; 11.788  │
├─────────────────────┼───────────┼────────────┼──────────────────────┼────────────┼───────────────┼──────────────────┤
│         160         │    939    │    0.01    │ 0.11777839237982567  │     1      │ 0.464 &plusmn; 0.004 │ 31.338 &plusmn; 11.952  │
└─────────────────────┴───────────┴────────────┴──────────────────────┴────────────┴───────────────┴──────────────────┘
</pre>
			<h1>&nbsp;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 &raquo;results.csv&laquo; as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,generation_node,ACCURACY,RUNTIME,recent_samples_size,n_samples,confidence,feature_proportion,n_clusters
0,0_0,COMPLETED,Sobol,GenerationStep_0,0.578890965732087203754474558082,76.307706832885742187500000000000,2675,927,0.005000000000000000104083408559,0.087955921888351440429687500000,3
1,1_0,COMPLETED,Sobol,GenerationStep_0,0.614056074766355153293773128098,148.295463323593139648437500000000,908,398,0.100000000000000005551115123126,0.196303996071219455377132590002,2
2,2_0,COMPLETED,Sobol,GenerationStep_0,0.616996884735202533178721751028,173.789375543594360351562500000000,1348,625,0.010000000000000000208166817117,0.021565718203783036666099093281,1
3,3_0,COMPLETED,Sobol,GenerationStep_0,0.621632398753894088727633970848,306.627836227416992187500000000000,3105,249,0.100000000000000005551115123126,0.112729337811470042840511496252,4
4,4_0,COMPLETED,Sobol,GenerationStep_0,0.610591900311526436517794991232,139.963609457015991210937500000000,3515,683,0.025000000000000001387778780781,0.144360431656241433584497713127,4
5,5_0,COMPLETED,Sobol,GenerationStep_0,0.611725856697819314611308527674,495.709876298904418945312500000000,1811,192,0.001000000000000000020816681712,0.039921658113598823547363281250,1
6,6_0,COMPLETED,Sobol,GenerationStep_0,0.548436137071651064189836688456,56.915634155273437500000000000000,508,872,0.250000000000000000000000000000,0.165453389286994934082031250000,2
7,7_0,COMPLETED,Sobol,GenerationStep_0,0.552186915887850515716195332061,60.339310884475708007812500000000,2202,453,0.025000000000000001387778780781,0.068817967921495443173185435626,3
8,8_0,COMPLETED,Sobol,GenerationStep_0,0.564012461059190006729124888807,78.945506572723388671875000000000,2329,552,0.250000000000000000000000000000,0.035103990510106090894293373594,2
9,9_0,COMPLETED,Sobol,GenerationStep_0,0.547763239875389440491915138409,51.339449644088745117187500000000,128,322,0.010000000000000000208166817117,0.130662178620696067810058593750,3
10,10_0,COMPLETED,Sobol,GenerationStep_0,0.608884735202492199945822903828,139.324911117553710937500000000000,1692,997,0.050000000000000002775557561563,0.056155810505151754208341685626,4
11,11_0,COMPLETED,Sobol,GenerationStep_0,0.619339563862928321746892379451,309.678549051284790039062500000000,3887,329,0.001000000000000000020816681712,0.159523427486419677734375000000,1
12,12_0,COMPLETED,Sobol,GenerationStep_0,0.608872274143302139037814413314,144.498249053955078125000000000000,3484,826,0.010000000000000000208166817117,0.178672837838530551568538840002,1
13,13_0,COMPLETED,Sobol,GenerationStep_0,0.614230529595015561916682145238,191.918526649475097656250000000000,1221,499,0.100000000000000005551115123126,0.087017847970128070489437277502,4
14,14_0,COMPLETED,Sobol,GenerationStep_0,0.586679127725856730535269889515,69.122207164764404296875000000000,536,720,0.005000000000000000104083408559,0.112293255329132091180355246252,3
15,15_0,COMPLETED,Sobol,GenerationStep_0,0.619464174454828708782372359565,326.203475713729858398437500000000,2794,156,0.050000000000000002775557561563,0.003459940105676651347921257695,2
16,16_0,COMPLETED,Sobol,GenerationStep_0,0.589619937694703999397916049929,85.284116744995117187500000000000,2894,764,0.025000000000000001387778780781,0.170522647723555575982601340002,4
17,17_0,COMPLETED,Sobol,GenerationStep_0,0.628137071651090339585721267213,307.531135797500610351562500000000,670,111,0.250000000000000000000000000000,0.063737078383564946260086969687,1
18,18_0,COMPLETED,Sobol,GenerationStep_0,0.593644859813084124766646709759,97.338754177093505859375000000000,1103,784,0.001000000000000000020816681712,0.138510495424270629882812500000,2
19,19_0,COMPLETED,Sobol,GenerationStep_0,0.609607476635514067275778415933,156.718510389328002929687500000000,3338,541,0.050000000000000002775557561563,0.045784369856119160047125404844,3
20,20_0,COMPLETED,Sobol,GenerationStep_0,0.605993769470404952670605780440,131.568467855453491210937500000000,3800,952,0.100000000000000005551115123126,0.014141078665852548079673312031,3
21,21_0,COMPLETED,Sobol,GenerationStep_0,0.612448598130841070918961577263,259.473201274871826171875000000000,1513,374,0.010000000000000000208166817117,0.120142346248030662536621093750,2
22,22_0,COMPLETED,Sobol,GenerationStep_0,0.522529595015576275862656530080,45.371855020523071289062500000000,197,593,0.050000000000000002775557561563,0.094599882513284688778654185626,1
23,23_0,COMPLETED,Sobol,GenerationStep_0,0.613881619937694744670864110958,116.140657663345336914062500000000,2494,281,0.005000000000000000104083408559,0.189672809839248668328792746252,4
24,24_0,COMPLETED,Sobol,GenerationStep_0,0.458056074766355125760242117394,37.709308147430419921875000000000,2118,857,0.050000000000000002775557561563,0.104880824312567719203137528439,1
25,25_0,COMPLETED,Sobol,GenerationStep_0,0.578866043613707192960760039568,88.989662885665893554687500000000,328,467,0.001000000000000000020816681712,0.010885154083371163455384866836,4
26,26_0,COMPLETED,Sobol,GenerationStep_0,0.611152647975077845110547514196,174.559259653091430664062500000000,1882,695,0.100000000000000005551115123126,0.185304591059684775622429242503,3
27,27_0,COMPLETED,Sobol,GenerationStep_0,0.621856697819314629960274487530,479.509878158569335937500000000000,3678,180,0.005000000000000000104083408559,0.080374456197023394499190374063,2
28,28_0,COMPLETED,Sobol,GenerationStep_0,0.607040498442367626452664808312,133.314219474792480468750000000000,3207,640,0.001000000000000000020816681712,0.061237274482846264234137123594,2
29,29_0,COMPLETED,Sobol,GenerationStep_0,0.620149532710280393388302400126,361.218223333358764648437500000000,1479,234,0.025000000000000001387778780781,0.154454746469855325186060213127,3
30,30_0,COMPLETED,BoTorch,GenerationStep_1,0.551065420560747698530690286134,61.055417776107788085937500000000,2486,882,0.010000000000000000208166817117,0.027739278107548157037420821780,1
31,31_0,FAILED,BoTorch,GenerationStep_1,,,30,307,0.100000000000000005551115123126,0.159968432140086130877065784262,4
32,32_0,COMPLETED,BoTorch,GenerationStep_1,0.585781931464174454582405360270,71.923722505569458007812500000000,476,569,0.010000000000000000208166817117,0.200000000000000011102230246252,1
33,33_0,FAILED,BoTorch,GenerationStep_1,,,10,308,0.001000000000000000020816681712,0.200000000000000011102230246252,4
34,34_0,FAILED,BoTorch,GenerationStep_1,,,3056,871,0.050000000000000002775557561563,0.000000000000000000000000000000,3
35,35_0,COMPLETED,BoTorch,GenerationStep_1,0.439813084112149532689528541596,34.796980857849121093750000000000,47,586,0.050000000000000002775557561563,0.189124521534947104273172158173,3
36,36_0,FAILED,BoTorch,GenerationStep_1,,,10,874,0.005000000000000000104083408559,0.000000000000000000000000000000,1
37,37_0,COMPLETED,BoTorch,GenerationStep_1,0.430242990654205625933315104703,28.528933525085449218750000000000,10,305,0.100000000000000005551115123126,0.000852059359741888684702804113,4
38,38_0,FAILED,BoTorch,GenerationStep_1,,,277,831,0.250000000000000000000000000000,0.000000000000000000000000000000,3
39,39_0,COMPLETED,BoTorch,GenerationStep_1,0.604261682242990705304919174523,157.356042146682739257812500000000,3016,579,0.001000000000000000020816681712,0.200000000000000011102230246252,1
40,40_0,COMPLETED,BoTorch,GenerationStep_1,0.457744548286604380216147092142,31.434023380279541015625000000000,65,567,0.250000000000000000000000000000,0.200000000000000011102230246252,1
41,41_0,COMPLETED,BoTorch,GenerationStep_1,0.603663551401869113988141180016,93.808329582214355468750000000000,2867,598,0.100000000000000005551115123126,0.168646739338756596060520109859,4
42,42_0,COMPLETED,BoTorch,GenerationStep_1,0.594691588785046687526403275115,80.368528366088867187500000000000,927,893,0.001000000000000000020816681712,0.108187641617833063545361937940,1
43,43_0,COMPLETED,BoTorch,GenerationStep_1,0.540386292834890924474677831313,46.270461320877075195312500000000,168,462,0.001000000000000000020816681712,0.171778270760550771933949931736,1
44,44_0,FAILED,BoTorch,GenerationStep_1,,,178,755,0.050000000000000002775557561563,0.000000000000000000000000000000,3
45,45_0,FAILED,BoTorch,GenerationStep_1,,,1818,860,0.025000000000000001387778780781,0.000000000000000000000000000000,4
46,46_0,FAILED,BoTorch,GenerationStep_1,,,3191,738,0.005000000000000000104083408559,0.000000000000000000000000000000,2
47,47_0,FAILED,BoTorch,GenerationStep_1,,,1006,891,0.250000000000000000000000000000,0.000000000000000000000000000000,1
48,48_0,FAILED,BoTorch,GenerationStep_1,,,2271,596,0.100000000000000005551115123126,0.000000000000000000000000000000,4
49,49_0,COMPLETED,BoTorch,GenerationStep_1,0.611838006230529640738780017273,155.504429817199707031250000000000,3620,583,0.250000000000000000000000000000,0.200000000000000011102230246252,1
50,50_0,FAILED,BoTorch,GenerationStep_1,,,4000,897,0.001000000000000000020816681712,0.000000000000000000000000000000,1
51,51_0,COMPLETED,BoTorch,GenerationStep_1,0.609595015576324006367769925419,128.371963977813720703125000000000,3913,889,0.010000000000000000208166817117,0.181092001923647855088361779963,2
52,52_0,COMPLETED,BoTorch,GenerationStep_1,0.608560747663551393493719388061,79.666688442230224609375000000000,2758,736,0.010000000000000000208166817117,0.092143910147417115719115088268,1
53,53_0,FAILED,BoTorch,GenerationStep_1,,,10,757,0.050000000000000002775557561563,0.000000000000000000000000000000,2
54,54_0,FAILED,BoTorch,GenerationStep_1,,,10,317,0.050000000000000002775557561563,0.000000000000000000000000000000,4
55,55_0,COMPLETED,BoTorch,GenerationStep_1,0.545196261682242999668801530788,54.008094072341918945312500000000,2158,451,0.010000000000000000208166817117,0.124785291614737045562399941900,1
56,56_0,COMPLETED,BoTorch,GenerationStep_1,0.605906542056074748359151271870,115.426237583160400390625000000000,1610,906,0.001000000000000000020816681712,0.200000000000000011102230246252,1
57,57_0,COMPLETED,BoTorch,GenerationStep_1,0.608423676012461056572533379949,156.295476913452148437500000000000,3841,882,0.100000000000000005551115123126,0.108338022403342407185000695335,4
58,58_0,COMPLETED,BoTorch,GenerationStep_1,0.603688473520249235804158161045,85.721725702285766601562500000000,2713,722,0.001000000000000000020816681712,0.031279995614651764923586085843,1
59,59_0,COMPLETED,BoTorch,GenerationStep_1,0.610093457943925221442782458325,144.490674018859863281250000000000,3962,856,0.025000000000000001387778780781,0.062884683825181655891789489488,4
60,60_0,FAILED,BoTorch,GenerationStep_1,,,1690,575,0.250000000000000000000000000000,0.000000000000000000000000000000,4
61,61_0,COMPLETED,BoTorch,GenerationStep_1,0.612984423676012468717999581713,230.385140419006347656250000000000,1756,439,0.250000000000000000000000000000,0.200000000000000011102230246252,4
62,62_0,FAILED,BoTorch,GenerationStep_1,,,699,851,0.025000000000000001387778780781,0.000000000000000000000000000000,1
63,63_0,FAILED,BoTorch,GenerationStep_1,,,846,574,0.250000000000000000000000000000,0.000000000000000000000000000000,4
64,64_0,FAILED,BoTorch,GenerationStep_1,,,1232,438,0.250000000000000000000000000000,0.000000000000000000000000000000,1
65,65_0,COMPLETED,BoTorch,GenerationStep_1,0.418492211838006222812680334755,30.072325229644775390625000000000,10,297,0.001000000000000000020816681712,0.200000000000000011102230246252,4
66,66_0,FAILED,BoTorch,GenerationStep_1,,,825,571,0.250000000000000000000000000000,0.000000000000000000000000000000,1
67,67_0,FAILED,BoTorch,GenerationStep_1,,,1763,749,0.250000000000000000000000000000,0.000000000000000000000000000000,4
68,68_0,FAILED,BoTorch,GenerationStep_1,,,1762,295,0.250000000000000000000000000000,0.000000000000000000000000000000,4
69,69_0,COMPLETED,BoTorch,GenerationStep_1,0.609919003115264812819873441185,284.574575185775756835937500000000,1612,296,0.250000000000000000000000000000,0.200000000000000011102230246252,3
70,70_0,FAILED,BoTorch,GenerationStep_1,,,10,847,0.001000000000000000020816681712,0.000000000000000000000000000000,1
71,71_0,COMPLETED,BoTorch,GenerationStep_1,0.619202492211837984825706371339,215.662546873092651367187500000000,3855,434,0.001000000000000000020816681712,0.200000000000000011102230246252,4
72,72_0,COMPLETED,BoTorch,GenerationStep_1,0.614205607476635551122967626725,147.261192083358764648437500000000,3053,436,0.250000000000000000000000000000,0.200000000000000011102230246252,1
73,73_0,FAILED,BoTorch,GenerationStep_1,,,958,843,0.250000000000000000000000000000,0.000000000000000000000000000000,1
74,74_0,COMPLETED,BoTorch,GenerationStep_1,0.614890965732087235728897667286,141.335652112960815429687500000000,673,296,0.250000000000000000000000000000,0.200000000000000011102230246252,1
75,75_0,COMPLETED,BoTorch,GenerationStep_1,0.613744548286604407749678102846,272.639654159545898437500000000000,1945,434,0.250000000000000000000000000000,0.200000000000000011102230246252,4
76,76_0,COMPLETED,BoTorch,GenerationStep_1,0.404473520249221174527320954439,30.190459251403808593750000000000,10,435,0.001000000000000000020816681712,0.200000000000000011102230246252,4
77,77_0,FAILED,BoTorch,GenerationStep_1,,,10,955,0.001000000000000000020816681712,0.000000000000000000000000000000,4
78,78_0,FAILED,BoTorch,GenerationStep_1,,,385,850,0.001000000000000000020816681712,0.000000000000000000000000000000,4
79,79_0,COMPLETED,BoTorch,GenerationStep_1,0.613507788161993805609029095649,145.820077180862426757812500000000,1694,749,0.250000000000000000000000000000,0.200000000000000011102230246252,4
80,80_0,FAILED,BoTorch,GenerationStep_1,,,1325,855,0.250000000000000000000000000000,0.000000000000000000000000000000,1
81,81_0,FAILED,BoTorch,GenerationStep_1,,,10,298,0.001000000000000000020816681712,0.000000000000000000000000000000,1
82,82_0,COMPLETED,BoTorch,GenerationStep_1,0.602392523364486009995744097978,97.816546440124511718750000000000,1523,954,0.250000000000000000000000000000,0.200000000000000011102230246252,3
83,83_0,FAILED,BoTorch,GenerationStep_1,,,492,749,0.001000000000000000020816681712,0.000000000000000000000000000000,4
84,84_0,COMPLETED,BoTorch,GenerationStep_1,0.607750778816199321852309367387,121.544776439666748046875000000000,3693,952,0.001000000000000000020816681712,0.200000000000000011102230246252,4
85,85_0,FAILED,BoTorch,GenerationStep_1,,,762,434,0.050000000000000002775557561563,0.000000000000000000000000000000,1
86,86_0,FAILED,BoTorch,GenerationStep_1,,,1419,294,0.250000000000000000000000000000,0.000000000000000000000000000000,1
87,87_0,COMPLETED,BoTorch,GenerationStep_1,0.615588785046728981242836198362,290.821623802185058593750000000000,1763,295,0.250000000000000000000000000000,0.200000000000000011102230246252,4
88,88_0,FAILED,BoTorch,GenerationStep_1,,,424,574,0.250000000000000000000000000000,0.000000000000000000000000000000,4
89,89_0,FAILED,BoTorch,GenerationStep_1,,,4000,428,0.001000000000000000020816681712,0.000000000000000000000000000000,4
90,90_0,FAILED,BoTorch,GenerationStep_1,,,519,303,0.005000000000000000104083408559,0.002478598260425365126108365743,4
91,91_0,COMPLETED,BoTorch,GenerationStep_1,0.609993769470404956223319459241,118.273207902908325195312500000000,673,449,0.025000000000000001387778780781,0.008752717803697169715593240369,2
92,92_0,FAILED,BoTorch,GenerationStep_1,,,678,853,0.100000000000000005551115123126,0.000000000000000000000000000000,1
93,93_0,FAILED,BoTorch,GenerationStep_1,,,1631,750,0.001000000000000000020816681712,0.000000000000000000000000000000,4
94,94_0,COMPLETED,BoTorch,GenerationStep_1,0.592523364485981307581141663832,107.685655355453491210937500000000,696,591,0.250000000000000000000000000000,0.017008800266036471632302706780,4
95,95_0,FAILED,BoTorch,GenerationStep_1,,,676,297,0.001000000000000000020816681712,0.000000000000000000000000000000,4
96,96_0,FAILED,BoTorch,GenerationStep_1,,,606,900,0.100000000000000005551115123126,0.000000000000000000000000000000,1
97,97_0,COMPLETED,BoTorch,GenerationStep_1,0.600959501557632447266144026798,118.945063114166259765625000000000,1459,817,0.250000000000000000000000000000,0.037971603349025107032588266520,2
98,98_0,FAILED,BoTorch,GenerationStep_1,,,661,883,0.001000000000000000020816681712,0.000000000000000000000000000000,4
99,99_0,FAILED,BoTorch,GenerationStep_1,,,741,465,0.100000000000000005551115123126,0.000000000000000000000000000000,1
100,100_0,COMPLETED,BoTorch,GenerationStep_1,0.613433021806853551183280615078,140.413797378540039062500000000000,734,310,0.001000000000000000020816681712,0.039228975618062024433019274738,4
101,101_0,FAILED,BoTorch,GenerationStep_1,,,774,868,0.025000000000000001387778780781,0.000000000000000000000000000000,2
102,102_0,FAILED,BoTorch,GenerationStep_1,,,575,438,0.050000000000000002775557561563,0.000000000000000000000000000000,1
103,103_0,FAILED,BoTorch,GenerationStep_1,,,873,486,0.010000000000000000208166817117,0.000000000000000000000000000000,1
104,104_0,COMPLETED,BoTorch,GenerationStep_1,0.567912772585669745062375568523,75.207271099090576171875000000000,588,816,0.250000000000000000000000000000,0.026851543684910335330062736148,1
105,105_0,FAILED,BoTorch,GenerationStep_1,,,1674,301,0.001000000000000000020816681712,0.000000000000000000000000000000,4
106,106_0,FAILED,BoTorch,GenerationStep_1,,,786,833,0.025000000000000001387778780781,0.000000000000000000000000000000,4
107,107_0,FAILED,BoTorch,GenerationStep_1,,,699,949,0.025000000000000001387778780781,0.000000000000000000000000000000,1
108,108_0,FAILED,BoTorch,GenerationStep_1,,,4000,468,0.050000000000000002775557561563,0.000000000000000000000000000000,1
109,109_0,FAILED,BoTorch,GenerationStep_1,,,633,442,0.250000000000000000000000000000,0.000000000000000000000000000000,1
110,110_0,FAILED,BoTorch,GenerationStep_1,,,658,774,0.050000000000000002775557561563,0.000000000000000000000000000000,3
111,111_0,COMPLETED,BoTorch,GenerationStep_1,0.572386292834890952896387261717,65.690876245498657226562500000000,732,981,0.001000000000000000020816681712,0.020766204835648633536537843725,1
112,112_0,COMPLETED,BoTorch,GenerationStep_1,0.623514018691588733922515075392,275.482978343963623046875000000000,532,100,0.001000000000000000020816681712,0.129094048764109570193170384300,4
113,113_0,FAILED,BoTorch,GenerationStep_1,,,814,284,0.001000000000000000020816681712,0.000000000000000000000000000000,4
114,114_0,FAILED,BoTorch,GenerationStep_1,,,589,904,0.025000000000000001387778780781,0.000000000000000000000000000000,4
115,115_0,FAILED,BoTorch,GenerationStep_1,,,684,593,0.001000000000000000020816681712,0.000000000000000000000000000000,4
116,116_0,COMPLETED,BoTorch,GenerationStep_1,0.604137071651090318269439194410,90.253977060317993164062500000000,626,460,0.010000000000000000208166817117,0.200000000000000011102230246252,2
117,117_0,COMPLETED,BoTorch,GenerationStep_1,0.614168224299065368398942155181,154.266240358352661132812500000000,2658,288,0.001000000000000000020816681712,0.084129290333639222820849568052,4
118,118_0,FAILED,BoTorch,GenerationStep_1,,,1402,782,0.005000000000000000104083408559,0.000000000000000000000000000000,4
119,119_0,FAILED,BoTorch,GenerationStep_1,,,666,293,0.001000000000000000020816681712,0.000000000000000000000000000000,2
120,120_0,COMPLETED,BoTorch,GenerationStep_1,0.594342679127725870280585240835,124.485344886779785156250000000000,2397,487,0.001000000000000000020816681712,0.003689519962727331060109436933,1
121,121_0,COMPLETED,BoTorch,GenerationStep_1,0.631725856697819332374876921676,140.766286849975585937500000000000,236,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4
122,122_0,COMPLETED,BoTorch,GenerationStep_1,0.602193146417445479556818099809,78.909715414047241210937500000000,2434,448,0.001000000000000000020816681712,0.200000000000000011102230246252,1
123,123_0,FAILED,BoTorch,GenerationStep_1,,,243,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
124,124_0,FAILED,BoTorch,GenerationStep_1,,,2722,479,0.100000000000000005551115123126,0.000000000000000000000000000000,1
125,125_0,COMPLETED,BoTorch,GenerationStep_1,0.570828660436137114153609672940,63.100116729736328125000000000000,2358,526,0.001000000000000000020816681712,0.200000000000000011102230246252,4
126,126_0,COMPLETED,BoTorch,GenerationStep_1,0.623875389408099723098644062702,91.880923748016357421875000000000,252,193,0.001000000000000000020816681712,0.200000000000000011102230246252,4
127,127_0,FAILED,BoTorch,GenerationStep_1,,,2426,373,0.001000000000000000020816681712,0.000000000000000000000000000000,1
128,128_0,COMPLETED,BoTorch,GenerationStep_1,0.607015576323987504636647827283,98.428467273712158203125000000000,2727,465,0.010000000000000000208166817117,0.200000000000000011102230246252,1
129,129_0,COMPLETED,BoTorch,GenerationStep_1,0.611090342679127762615109986655,129.482469558715820312500000000000,2590,456,0.010000000000000000208166817117,0.200000000000000011102230246252,1
130,130_0,COMPLETED,BoTorch,GenerationStep_1,0.625109034267912755389318135713,230.733539819717407226562500000000,2369,191,0.001000000000000000020816681712,0.200000000000000011102230246252,4
131,131_0,COMPLETED,BoTorch,GenerationStep_1,0.641246105919003106521358859027,173.089097738265991210937500000000,262,100,0.001000000000000000020816681712,0.200000000000000011102230246252,4
132,132_0,FAILED,BoTorch,GenerationStep_1,,,2340,533,0.001000000000000000020816681712,0.000000000000000000000000000000,1
133,133_0,FAILED,BoTorch,GenerationStep_1,,,2398,486,0.001000000000000000020816681712,0.000000000000000000000000000000,4
134,134_0,COMPLETED,BoTorch,GenerationStep_1,0.589632398753894060305924540444,65.196099281311035156250000000000,2384,509,0.001000000000000000020816681712,0.200000000000000011102230246252,1
135,135_0,COMPLETED,BoTorch,GenerationStep_1,0.620348909657320923827228398295,84.031931161880493164062500000000,240,209,0.250000000000000000000000000000,0.200000000000000011102230246252,4
136,136_0,FAILED,BoTorch,GenerationStep_1,,,3283,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
137,137_0,COMPLETED,BoTorch,GenerationStep_1,0.633844236760124579710407033417,182.321251153945922851562500000000,2339,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4
138,138_0,FAILED,BoTorch,GenerationStep_1,,,2413,433,0.001000000000000000020816681712,0.000000000000000000000000000000,4
139,139_0,FAILED,BoTorch,GenerationStep_1,,,2779,439,0.001000000000000000020816681712,0.000000000000000000000000000000,1
140,140_0,COMPLETED,BoTorch,GenerationStep_1,0.614180685358255429306950645696,204.352341413497924804687500000000,4000,514,0.250000000000000000000000000000,0.200000000000000011102230246252,1
141,141_0,COMPLETED,BoTorch,GenerationStep_1,0.469445482866043595215899131290,34.920335531234741210937500000000,174,974,0.001000000000000000020816681712,0.200000000000000011102230246252,4
142,142_0,FAILED,BoTorch,GenerationStep_1,,,2320,582,0.001000000000000000020816681712,0.000000000000000000000000000000,4
143,143_0,COMPLETED,BoTorch,GenerationStep_1,0.611950155763239855843949044356,91.489401340484619140625000000000,2387,348,0.001000000000000000020816681712,0.200000000000000011102230246252,4
144,144_0,COMPLETED,BoTorch,GenerationStep_1,0.607813084112149515370049357443,154.681811094284057617187500000000,2731,398,0.250000000000000000000000000000,0.200000000000000011102230246252,1
145,145_0,FAILED,BoTorch,GenerationStep_1,,,247,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
146,146_0,FAILED,BoTorch,GenerationStep_1,,,3166,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
147,147_0,COMPLETED,BoTorch,GenerationStep_1,0.594093457943925207231927743123,103.204618215560913085937500000000,1253,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4
148,148_0,COMPLETED,BoTorch,GenerationStep_1,0.606791277258566963404007310601,101.040822505950927734375000000000,2597,439,0.100000000000000005551115123126,0.200000000000000011102230246252,1
149,149_0,FAILED,BoTorch,GenerationStep_1,,,2374,422,0.001000000000000000020816681712,0.000000000000000000000000000000,1
150,150_0,COMPLETED,BoTorch,GenerationStep_1,0.603663551401869113988141180016,97.403449296951293945312500000000,2504,498,0.001000000000000000020816681712,0.111983394807356875788606487276,4
151,151_0,COMPLETED,BoTorch,GenerationStep_1,0.543389408099688497877366444300,49.684279680252075195312500000000,161,419,0.250000000000000000000000000000,0.155723399567828796330459795172,4
152,152_0,COMPLETED,BoTorch,GenerationStep_1,0.485781931464174476786865852773,34.428333282470703125000000000000,188,924,0.001000000000000000020816681712,0.082868987335306440455973131520,4
153,153_0,COMPLETED,BoTorch,GenerationStep_1,0.607090342679127759062396307854,92.501557111740112304687500000000,2568,459,0.001000000000000000020816681712,0.114160817493183117110611135558,1
154,154_0,COMPLETED,BoTorch,GenerationStep_1,0.561171339563862892063639264961,62.081666707992553710937500000000,2667,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,1
155,155_0,COMPLETED,BoTorch,GenerationStep_1,0.525956386292834920936911657918,54.149929761886596679687500000000,450,1000,0.001000000000000000020816681712,0.087459044990944270758781442510,4
156,156_0,COMPLETED,BoTorch,GenerationStep_1,0.556485981308411203904995545599,56.018726110458374023437500000000,2490,822,0.250000000000000000000000000000,0.067138609687520861557530338359,4
157,157_0,COMPLETED,BoTorch,GenerationStep_1,0.560610591900311483470886741998,60.652454614639282226562500000000,563,895,0.001000000000000000020816681712,0.200000000000000011102230246252,4
158,158_0,COMPLETED,BoTorch,GenerationStep_1,0.488623052959501535941200245361,50.173628330230712890625000000000,76,443,0.250000000000000000000000000000,0.034730300015906413746424874489,1
159,159_0,COMPLETED,BoTorch,GenerationStep_1,0.593595015576323992156915210217,71.098054647445678710937500000000,2408,511,0.001000000000000000020816681712,0.110357134786626037703527458689,4
160,160_0,COMPLETED,BoTorch,GenerationStep_1,0.489208722741433010838818518096,41.259029150009155273437500000000,173,784,0.001000000000000000020816681712,0.200000000000000011102230246252,4
161,161_0,FAILED,BoTorch,GenerationStep_1,,,266,168,0.001000000000000000020816681712,0.000000000000000000000000000000,4
162,162_0,COMPLETED,BoTorch,GenerationStep_1,0.433320872274143287228298504488,31.110738277435302734375000000000,111,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,1
163,163_0,FAILED,BoTorch,GenerationStep_1,,,222,397,0.250000000000000000000000000000,0.000000000000000000000000000000,4
164,164_0,COMPLETED,BoTorch,GenerationStep_1,0.451663551401869145518475079371,34.870040416717529296875000000000,158,1000,0.250000000000000000000000000000,0.003329566215779033479249537919,4
165,165_0,COMPLETED,BoTorch,GenerationStep_1,0.462130841121495328227553045508,34.546654462814331054687500000000,123,779,0.100000000000000005551115123126,0.200000000000000011102230246252,1
166,166_0,FAILED,BoTorch,GenerationStep_1,,,3281,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
167,167_0,COMPLETED,BoTorch,GenerationStep_1,0.574168224299065443894107829692,73.523483276367187500000000000000,2404,593,0.001000000000000000020816681712,0.101216233031466554259658607862,4
168,168_0,COMPLETED,BoTorch,GenerationStep_1,0.418566978193146421727277584068,30.797226667404174804687500000000,79,1000,0.001000000000000000020816681712,0.075473157465120899178856461731,1
169,169_0,COMPLETED,BoTorch,GenerationStep_1,0.549258566978193196739255199645,61.759251356124877929687500000000,578,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,1
170,170_0,FAILED,BoTorch,GenerationStep_1,,,2792,822,0.250000000000000000000000000000,0.000000000000000000000000000000,4
171,171_0,COMPLETED,BoTorch,GenerationStep_1,0.604211838006230572695187674981,102.797202825546264648437500000000,2749,519,0.001000000000000000020816681712,0.099276406930915259141379181074,4
172,172_0,COMPLETED,BoTorch,GenerationStep_1,0.539202492211838024793735257845,49.816471099853515625000000000000,487,1000,0.250000000000000000000000000000,0.091865652592296287437534374476,1
173,173_0,COMPLETED,BoTorch,GenerationStep_1,0.581482866043613655371302684216,70.899470567703247070312500000000,875,1000,0.250000000000000000000000000000,0.066354397380029170316895203996,4
174,174_0,COMPLETED,BoTorch,GenerationStep_1,0.606990654205607493842933308770,138.719579458236694335937500000000,1817,783,0.001000000000000000020816681712,0.084433018171788748462702756115,4
175,175_0,FAILED,BoTorch,GenerationStep_1,,,262,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
176,176_0,COMPLETED,BoTorch,GenerationStep_1,0.563962616822429874119393389265,64.895271778106689453125000000000,2672,1000,0.250000000000000000000000000000,0.090964005123798943874824374234,4
177,177_0,COMPLETED,BoTorch,GenerationStep_1,0.551401869158878454868499829900,61.303530216217041015625000000000,2560,997,0.001000000000000000020816681712,0.200000000000000011102230246252,1
178,178_0,COMPLETED,BoTorch,GenerationStep_1,0.579376947040498468943781062990,59.173320293426513671875000000000,238,386,0.250000000000000000000000000000,0.095861634697190029053004423076,4
179,179_0,COMPLETED,BoTorch,GenerationStep_1,0.543962616822429856355824995262,44.732726573944091796875000000000,131,377,0.250000000000000000000000000000,0.200000000000000011102230246252,4
180,180_0,COMPLETED,BoTorch,GenerationStep_1,0.568361370716510938549959064403,58.087967872619628906250000000000,2511,836,0.250000000000000000000000000000,0.200000000000000011102230246252,1
181,181_0,COMPLETED,BoTorch,GenerationStep_1,0.598093457943925210784641421924,68.883549928665161132812500000000,245,318,0.001000000000000000020816681712,0.200000000000000011102230246252,4
182,182_0,COMPLETED,BoTorch,GenerationStep_1,0.626903426791277307295047194202,106.225252389907836914062500000000,267,161,0.001000000000000000020816681712,0.200000000000000011102230246252,4
183,183_0,COMPLETED,BoTorch,GenerationStep_1,0.548498442367601257707576678513,48.797197818756103515625000000000,93,309,0.250000000000000000000000000000,0.200000000000000011102230246252,1
184,184_0,FAILED,BoTorch,GenerationStep_1,,,3156,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
185,185_0,COMPLETED,BoTorch,GenerationStep_1,0.589769470404984397227110548556,58.806325435638427734375000000000,203,313,0.250000000000000000000000000000,0.200000000000000011102230246252,1
186,186_0,FAILED,BoTorch,GenerationStep_1,,,233,878,0.001000000000000000020816681712,0.000000000000000000000000000000,1
187,187_0,COMPLETED,BoTorch,GenerationStep_1,0.577557632398753906244337485987,64.915504693984985351562500000000,2642,841,0.001000000000000000020816681712,0.200000000000000011102230246252,1
188,188_0,COMPLETED,BoTorch,GenerationStep_1,0.504535825545171290329449220735,39.213952302932739257812500000000,173,656,0.001000000000000000020816681712,0.200000000000000011102230246252,1
189,189_0,FAILED,BoTorch,GenerationStep_1,,,157,792,0.250000000000000000000000000000,0.000000000000000000000000000000,4
190,190_0,COMPLETED,BoTorch,GenerationStep_1,0.602118380062305336153372081753,119.765000820159912109375000000000,1787,912,0.250000000000000000000000000000,0.200000000000000011102230246252,1
191,191_0,COMPLETED,BoTorch,GenerationStep_1,0.518267912772585659375579325570,62.069527387619018554687500000000,2416,887,0.250000000000000000000000000000,0.200000000000000011102230246252,1
192,192_0,COMPLETED,BoTorch,GenerationStep_1,0.616760124610591931038072743831,78.368792772293090820312500000000,261,266,0.001000000000000000020816681712,0.200000000000000011102230246252,4
193,193_0,FAILED,BoTorch,GenerationStep_1,,,202,268,0.001000000000000000020816681712,0.000000000000000000000000000000,1
194,194_0,COMPLETED,BoTorch,GenerationStep_1,0.594454828660436085385754267918,90.183513879776000976562500000000,3250,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4
195,195_0,FAILED,BoTorch,GenerationStep_1,,,4000,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
196,196_0,FAILED,BoTorch,GenerationStep_1,,,2743,889,0.250000000000000000000000000000,0.000000000000000000000000000000,1
197,197_0,FAILED,BoTorch,GenerationStep_1,,,2444,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
198,198_0,COMPLETED,BoTorch,GenerationStep_1,0.552336448598130802523087368172,60.654381990432739257812500000000,2421,817,0.001000000000000000020816681712,0.200000000000000011102230246252,1
199,199_0,FAILED,BoTorch,GenerationStep_1,,,148,682,0.001000000000000000020816681712,0.000000000000000000000000000000,1
200,200_0,COMPLETED,BoTorch,GenerationStep_1,0.598168224299065465210389902495,95.442717313766479492187500000000,3314,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,1
201,201_0,COMPLETED,BoTorch,GenerationStep_1,0.567464174454828662597094535158,50.381419897079467773437500000000,153,322,0.250000000000000000000000000000,0.200000000000000011102230246252,1
202,202_0,FAILED,BoTorch,GenerationStep_1,,,627,889,0.001000000000000000020816681712,0.000000000000000000000000000000,4
203,203_0,FAILED,BoTorch,GenerationStep_1,,,3189,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
204,204_0,COMPLETED,BoTorch,GenerationStep_1,0.576623052959501558589749947714,69.873201131820678710937500000000,171,308,0.250000000000000000000000000000,0.200000000000000011102230246252,4
205,205_0,FAILED,BoTorch,GenerationStep_1,,,422,822,0.250000000000000000000000000000,0.000000000000000000000000000000,4
206,206_0,COMPLETED,BoTorch,GenerationStep_1,0.520660436137071691575783916051,45.446992635726928710937500000000,366,889,0.001000000000000000020816681712,0.200000000000000011102230246252,1
207,207_0,COMPLETED,BoTorch,GenerationStep_1,0.567489096573208673390809053672,56.158847093582153320312500000000,2462,732,0.250000000000000000000000000000,0.200000000000000011102230246252,1
208,208_0,COMPLETED,BoTorch,GenerationStep_1,0.607277258566978228593313815509,129.616455316543579101562500000000,4000,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,1
209,209_0,COMPLETED,BoTorch,GenerationStep_1,0.550031152647975085656639748777,54.581964492797851562500000000000,2488,877,0.250000000000000000000000000000,0.200000000000000011102230246252,4
210,210_0,COMPLETED,BoTorch,GenerationStep_1,0.489395638629283480369736025750,43.534347534179687500000000000000,192,779,0.001000000000000000020816681712,0.002256141414090021956456721952,4
211,211_0,COMPLETED,BoTorch,GenerationStep_1,0.513171339563862960453377581871,37.061887979507446289062500000000,53,275,0.250000000000000000000000000000,0.082275050786764197807698906217,1
212,212_0,COMPLETED,BoTorch,GenerationStep_1,0.532660436137071702233924952452,46.829876899719238281250000000000,241,628,0.250000000000000000000000000000,0.200000000000000011102230246252,4
213,213_0,FAILED,BoTorch,GenerationStep_1,,,176,293,0.001000000000000000020816681712,0.000000000000000000000000000000,1
214,214_0,COMPLETED,BoTorch,GenerationStep_1,0.462778816199376941131760077042,40.390241146087646484375000000000,126,796,0.250000000000000000000000000000,0.048103921523861434272895110098,1
215,215_0,FAILED,BoTorch,GenerationStep_1,,,217,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
216,216_0,FAILED,BoTorch,GenerationStep_1,,,3231,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
217,217_0,FAILED,BoTorch,GenerationStep_1,,,247,232,0.001000000000000000020816681712,0.000000000000000000000000000000,1
218,218_0,COMPLETED,BoTorch,GenerationStep_1,0.569133956386292827467343613534,64.280584812164306640625000000000,2527,795,0.001000000000000000020816681712,0.131133340438320616927470041446,4
219,219_0,FAILED,BoTorch,GenerationStep_1,,,224,271,0.001000000000000000020816681712,0.000000000000000000000000000000,4
220,220_0,COMPLETED,BoTorch,GenerationStep_1,0.623190031152647927470411559625,98.553036689758300781250000000000,232,157,0.250000000000000000000000000000,0.200000000000000011102230246252,1
221,221_0,COMPLETED,BoTorch,GenerationStep_1,0.486704049844236763533444900531,38.811905860900878906250000000000,258,959,0.250000000000000000000000000000,0.117412783708891743650326588977,1
222,222_0,FAILED,BoTorch,GenerationStep_1,,,87,232,0.001000000000000000020816681712,0.000000000000000000000000000000,1
223,223_0,COMPLETED,BoTorch,GenerationStep_1,0.505084112149532749036495715700,44.226315021514892578125000000000,239,724,0.250000000000000000000000000000,0.200000000000000011102230246252,4
224,224_0,COMPLETED,BoTorch,GenerationStep_1,0.593570093457943870340898229188,87.983737230300903320312500000000,877,823,0.250000000000000000000000000000,0.200000000000000011102230246252,1
225,225_0,FAILED,BoTorch,GenerationStep_1,,,405,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
226,226_0,COMPLETED,BoTorch,GenerationStep_1,0.472809968847352046772414269071,42.493875503540039062500000000000,39,352,0.250000000000000000000000000000,0.104981785322788845338237706528,1
227,227_0,FAILED,BoTorch,GenerationStep_1,,,47,145,0.250000000000000000000000000000,0.000000000000000000000000000000,4
228,228_0,FAILED,BoTorch,GenerationStep_1,,,3066,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
229,229_0,FAILED,BoTorch,GenerationStep_1,,,448,901,0.250000000000000000000000000000,0.000000000000000000000000000000,4
230,230_0,COMPLETED,BoTorch,GenerationStep_1,0.572361370716510942102672743204,60.874253749847412109375000000000,526,742,0.250000000000000000000000000000,0.200000000000000011102230246252,4
231,231_0,FAILED,BoTorch,GenerationStep_1,,,2845,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
232,232_0,FAILED,BoTorch,GenerationStep_1,,,2438,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
233,233_0,COMPLETED,BoTorch,GenerationStep_1,0.634242990654205640588259029755,136.333097457885742187500000000000,252,125,0.001000000000000000020816681712,0.200000000000000011102230246252,1
234,234_0,COMPLETED,BoTorch,GenerationStep_1,0.486791277258566967844899409101,37.040899753570556640625000000000,149,700,0.250000000000000000000000000000,0.124047816434128391205327091029,4
235,235_0,FAILED,BoTorch,GenerationStep_1,,,2918,910,0.250000000000000000000000000000,0.000000000000000000000000000000,1
236,236_0,FAILED,BoTorch,GenerationStep_1,,,4000,616,0.001000000000000000020816681712,0.000000000000000000000000000000,4
237,237_0,FAILED,BoTorch,GenerationStep_1,,,966,916,0.250000000000000000000000000000,0.000000000000000000000000000000,4
238,238_0,COMPLETED,BoTorch,GenerationStep_1,0.454866043613707138337787228011,36.211555242538452148437500000000,147,992,0.250000000000000000000000000000,0.131215901328070927434055192862,4
239,239_0,FAILED,BoTorch,GenerationStep_1,,,182,885,0.250000000000000000000000000000,0.000000000000000000000000000000,1
240,240_0,FAILED,BoTorch,GenerationStep_1,,,3154,856,0.250000000000000000000000000000,0.000000000000000000000000000000,1
241,241_0,COMPLETED,BoTorch,GenerationStep_1,0.558193146417445440476967633003,69.627045154571533203125000000000,2253,482,0.001000000000000000020816681712,0.200000000000000011102230246252,4
242,242_0,COMPLETED,BoTorch,GenerationStep_1,0.591975077881619959896397631383,162.016562700271606445312500000000,2957,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,1
243,243_0,COMPLETED,BoTorch,GenerationStep_1,0.520236760124610619904217401199,44.867469549179077148437500000000,2232,693,0.001000000000000000020816681712,0.200000000000000011102230246252,4
244,244_0,FAILED,BoTorch,GenerationStep_1,,,30,156,0.250000000000000000000000000000,0.000000000000000000000000000000,1
245,245_0,FAILED,BoTorch,GenerationStep_1,,,2055,967,0.250000000000000000000000000000,0.000000000000000000000000000000,4
246,246_0,FAILED,BoTorch,GenerationStep_1,,,186,216,0.250000000000000000000000000000,0.000000000000000000000000000000,1
247,247_0,COMPLETED,BoTorch,GenerationStep_1,0.595464174454828687466090286762,88.895566225051879882812500000000,3138,853,0.250000000000000000000000000000,0.200000000000000011102230246252,4
248,248_0,COMPLETED,BoTorch,GenerationStep_1,0.571040498442367594478241699107,72.774924039840698242187500000000,2290,450,0.001000000000000000020816681712,0.200000000000000011102230246252,4
249,249_0,COMPLETED,BoTorch,GenerationStep_1,0.592984423676012450954431187711,81.697422981262207031250000000000,3055,903,0.250000000000000000000000000000,0.200000000000000011102230246252,1
250,250_0,FAILED,BoTorch,GenerationStep_1,,,1122,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
251,251_0,FAILED,BoTorch,GenerationStep_1,,,3234,824,0.250000000000000000000000000000,0.000000000000000000000000000000,4
252,252_0,FAILED,BoTorch,GenerationStep_1,,,3006,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
253,253_0,COMPLETED,BoTorch,GenerationStep_1,0.536000000000000031974423109205,46.510665655136108398437500000000,2224,539,0.001000000000000000020816681712,0.200000000000000011102230246252,4
254,254_0,FAILED,BoTorch,GenerationStep_1,,,984,639,0.001000000000000000020816681712,0.000000000000000000000000000000,1
255,255_0,COMPLETED,BoTorch,GenerationStep_1,0.361831775700934554773624540758,26.830154180526733398437500000000,2019,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
256,256_0,COMPLETED,BoTorch,GenerationStep_1,0.629258566978193156771226313140,539.998976707458496093750000000000,1053,182,0.250000000000000000000000000000,0.200000000000000011102230246252,1
257,257_0,FAILED,BoTorch,GenerationStep_1,,,2207,763,0.250000000000000000000000000000,0.000000000000000000000000000000,4
258,258_0,COMPLETED,BoTorch,GenerationStep_1,0.494666666666666643425998017847,43.270508050918579101562500000000,2200,732,0.250000000000000000000000000000,0.200000000000000011102230246252,4
259,259_0,FAILED,BoTorch,GenerationStep_1,,,111,203,0.250000000000000000000000000000,0.000000000000000000000000000000,1
260,260_0,COMPLETED,BoTorch,GenerationStep_1,0.617395638629283483034271284851,230.708984613418579101562500000000,1027,242,0.001000000000000000020816681712,0.200000000000000011102230246252,4
261,261_0,COMPLETED,BoTorch,GenerationStep_1,0.596834890965732056677950367884,100.598099470138549804687500000000,965,627,0.250000000000000000000000000000,0.200000000000000011102230246252,1
262,262_0,FAILED,BoTorch,GenerationStep_1,,,2969,921,0.250000000000000000000000000000,0.000000000000000000000000000000,1
263,263_0,FAILED,BoTorch,GenerationStep_1,,,2361,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
264,264_0,COMPLETED,BoTorch,GenerationStep_1,0.594616822429906544122957257059,77.051469802856445312500000000000,2657,642,0.001000000000000000020816681712,0.200000000000000011102230246252,1
265,265_0,FAILED,BoTorch,GenerationStep_1,,,465,353,0.001000000000000000020816681712,0.200000000000000011102230246252,4
266,266_0,COMPLETED,BoTorch,GenerationStep_1,0.606965732087227372026916327741,112.611510992050170898437500000000,1958,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
267,267_0,COMPLETED,BoTorch,GenerationStep_1,0.545084112149532673541330041189,61.038481473922729492187500000000,2330,704,0.250000000000000000000000000000,0.200000000000000011102230246252,4
268,268_0,COMPLETED,BoTorch,GenerationStep_1,0.379028037383177585084581551200,28.275927543640136718750000000000,2033,908,0.250000000000000000000000000000,0.200000000000000011102230246252,4
269,269_0,COMPLETED,BoTorch,GenerationStep_1,0.591563862928348949132839607046,47.863183975219726562500000000000,28,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
270,270_0,COMPLETED,BoTorch,GenerationStep_1,0.612137071651090325374866552011,117.342608451843261718750000000000,1898,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4
271,271_0,COMPLETED,BoTorch,GenerationStep_1,0.486168224299065421245558127339,43.312919855117797851562500000000,2233,1000,0.005000000000000000104083408559,0.200000000000000011102230246252,1
272,272_0,COMPLETED,BoTorch,GenerationStep_1,0.606330218068535820030717786722,154.337209701538085937500000000000,1932,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4
273,273_0,COMPLETED,BoTorch,GenerationStep_1,0.607464174454828698124231323163,122.478520631790161132812500000000,920,519,0.001000000000000000020816681712,0.200000000000000011102230246252,1
274,274_0,COMPLETED,BoTorch,GenerationStep_1,0.587214953271028017312005431450,85.655349969863891601562500000000,2758,813,0.250000000000000000000000000000,0.200000000000000011102230246252,4
275,275_0,COMPLETED,BoTorch,GenerationStep_1,0.483875389408099709775967767200,45.047884702682495117187500000000,2201,1000,0.100000000000000005551115123126,0.200000000000000011102230246252,1
276,276_0,FAILED,BoTorch,GenerationStep_1,,,78,149,0.010000000000000000208166817117,0.000000000000000000000000000000,1
277,277_0,FAILED,BoTorch,GenerationStep_1,,,1919,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
278,278_0,COMPLETED,BoTorch,GenerationStep_1,0.603900311526479716128790187213,124.915198564529418945312500000000,1919,944,0.250000000000000000000000000000,0.200000000000000011102230246252,4
279,279_0,FAILED,BoTorch,GenerationStep_1,,,1339,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
280,280_0,FAILED,BoTorch,GenerationStep_1,,,2241,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
281,281_0,COMPLETED,BoTorch,GenerationStep_1,0.608760124610591923932645386230,116.212612390518188476562500000000,1887,1000,0.010000000000000000208166817117,0.200000000000000011102230246252,4
282,282_0,COMPLETED,BoTorch,GenerationStep_1,0.551651090342679117917157327611,39.075759410858154296875000000000,39,186,0.010000000000000000208166817117,0.200000000000000011102230246252,1
283,283_0,COMPLETED,BoTorch,GenerationStep_1,0.507339563862928333293211835553,43.501038551330566406250000000000,2292,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
284,284_0,COMPLETED,BoTorch,GenerationStep_1,0.602317757009345755569995617407,122.927308082580566406250000000000,936,539,0.001000000000000000020816681712,0.200000000000000011102230246252,4
285,285_0,COMPLETED,BoTorch,GenerationStep_1,0.619601246105919045703558367677,83.025027751922607421875000000000,2370,336,0.250000000000000000000000000000,0.200000000000000011102230246252,1
286,286_0,COMPLETED,BoTorch,GenerationStep_1,0.478890965732087225958935050585,38.441001892089843750000000000000,2212,1000,0.010000000000000000208166817117,0.200000000000000011102230246252,4
287,287_0,COMPLETED,BoTorch,GenerationStep_1,0.602080996884735153429346610210,375.356345176696777343750000000000,838,490,0.001000000000000000020816681712,0.200000000000000011102230246252,1
288,288_0,FAILED,BoTorch,GenerationStep_1,,,84,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
289,289_0,COMPLETED,BoTorch,GenerationStep_1,0.476859813084112127423708216156,45.655609846115112304687500000000,2208,937,0.001000000000000000020816681712,0.200000000000000011102230246252,1
290,290_0,FAILED,BoTorch,GenerationStep_1,,,1943,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
291,291_0,COMPLETED,BoTorch,GenerationStep_1,0.594130841121495278933650752151,84.199326992034912109375000000000,1057,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
292,292_0,FAILED,BoTorch,GenerationStep_1,,,1888,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
293,293_0,COMPLETED,BoTorch,GenerationStep_1,0.612735202492211805669342084002,110.624966382980346679687500000000,1894,1000,0.050000000000000002775557561563,0.200000000000000011102230246252,1
294,294_0,FAILED,BoTorch,GenerationStep_1,,,2268,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
295,295_0,FAILED,BoTorch,GenerationStep_1,,,2742,824,0.250000000000000000000000000000,0.000000000000000000000000000000,1
296,296_0,FAILED,BoTorch,GenerationStep_1,,,92,235,0.010000000000000000208166817117,0.000000000000000000000000000000,1
297,297_0,COMPLETED,BoTorch,GenerationStep_1,0.604062305295950174865993176354,116.312829256057739257812500000000,1914,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4
298,298_0,FAILED,BoTorch,GenerationStep_1,,,1196,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
299,299_0,FAILED,BoTorch,GenerationStep_1,,,1886,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
300,300_0,FAILED,BoTorch,GenerationStep_1,,,1907,785,0.001000000000000000020816681712,0.000000000000000000000000000000,1
301,301_0,FAILED,BoTorch,GenerationStep_1,,,424,305,0.250000000000000000000000000000,0.000000000000000000000000000000,4
302,302_0,FAILED,BoTorch,GenerationStep_1,,,3411,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
303,303_0,COMPLETED,BoTorch,GenerationStep_1,0.590180685358255407990668572893,107.205716133117675781250000000000,2573,540,0.250000000000000000000000000000,0.200000000000000011102230246252,4
304,304_0,FAILED,BoTorch,GenerationStep_1,,,2832,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
305,305_0,COMPLETED,BoTorch,GenerationStep_1,0.620610591900311536761591924005,148.323220729827880859375000000000,300,165,0.250000000000000000000000000000,0.200000000000000011102230246252,1
306,306_0,FAILED,BoTorch,GenerationStep_1,,,3567,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
307,307_0,FAILED,BoTorch,GenerationStep_1,,,1163,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
308,308_0,COMPLETED,BoTorch,GenerationStep_1,0.609993769470404956223319459241,151.470262050628662109375000000000,1910,699,0.001000000000000000020816681712,0.200000000000000011102230246252,1
309,309_0,COMPLETED,BoTorch,GenerationStep_1,0.594878504672897157057320782769,71.388866186141967773437500000000,2506,597,0.250000000000000000000000000000,0.200000000000000011102230246252,2
310,310_0,FAILED,BoTorch,GenerationStep_1,,,1903,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
311,311_0,COMPLETED,BoTorch,GenerationStep_1,0.578890965732087203754474558082,78.048439502716064453125000000000,2822,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
312,312_0,FAILED,BoTorch,GenerationStep_1,,,1334,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
313,313_0,COMPLETED,BoTorch,GenerationStep_1,0.614168224299065368398942155181,149.687733411788940429687500000000,1713,747,0.001000000000000000020816681712,0.200000000000000011102230246252,1
314,314_0,FAILED,BoTorch,GenerationStep_1,,,2426,922,0.001000000000000000020816681712,0.000000000000000000000000000000,4
315,315_0,COMPLETED,BoTorch,GenerationStep_1,0.613644859813084142530215103761,145.603988409042358398437500000000,3729,744,0.250000000000000000000000000000,0.200000000000000011102230246252,1
316,316_0,COMPLETED,BoTorch,GenerationStep_1,0.644996884735202447025415040116,111.879597187042236328125000000000,121,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
317,317_0,COMPLETED,BoTorch,GenerationStep_1,0.606691588785046698184544311516,126.143934726715087890625000000000,3549,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
318,318_0,COMPLETED,BoTorch,GenerationStep_1,0.515015576323987533946535677387,66.389416217803955078125000000000,99,401,0.250000000000000000000000000000,0.200000000000000011102230246252,2
319,319_0,FAILED,BoTorch,GenerationStep_1,,,157,357,0.250000000000000000000000000000,0.000000000000000000000000000000,2
320,320_0,FAILED,BoTorch,GenerationStep_1,,,3572,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
321,321_0,FAILED,BoTorch,GenerationStep_1,,,783,904,0.250000000000000000000000000000,0.000000000000000000000000000000,4
322,322_0,FAILED,BoTorch,GenerationStep_1,,,989,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
323,323_0,COMPLETED,BoTorch,GenerationStep_1,0.604834890965732063783377725485,100.235060453414916992187500000000,316,376,0.250000000000000000000000000000,0.200000000000000011102230246252,3
324,324_0,FAILED,BoTorch,GenerationStep_1,,,3412,840,0.001000000000000000020816681712,0.000000000000000000000000000000,4
325,325_0,FAILED,BoTorch,GenerationStep_1,,,305,267,0.250000000000000000000000000000,0.000000000000000000000000000000,2
326,326_0,COMPLETED,BoTorch,GenerationStep_1,0.545894080996884745182740061864,51.367344856262207031250000000000,2356,669,0.001000000000000000020816681712,0.200000000000000011102230246252,1
327,327_0,FAILED,BoTorch,GenerationStep_1,,,2576,546,0.250000000000000000000000000000,0.000000000000000000000000000000,4
328,328_0,COMPLETED,BoTorch,GenerationStep_1,0.613744548286604407749678102846,162.465665578842163085937500000000,3424,653,0.250000000000000000000000000000,0.200000000000000011102230246252,1
329,329_0,FAILED,BoTorch,GenerationStep_1,,,1705,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
330,330_0,FAILED,BoTorch,GenerationStep_1,,,3421,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
331,331_0,FAILED,BoTorch,GenerationStep_1,,,2468,939,0.001000000000000000020816681712,0.000000000000000000000000000000,4
332,332_0,COMPLETED,BoTorch,GenerationStep_1,0.627364485981308450668336718081,79.796999454498291015625000000000,117,140,0.250000000000000000000000000000,0.200000000000000011102230246252,1
333,333_0,FAILED,BoTorch,GenerationStep_1,,,153,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
334,334_0,FAILED,BoTorch,GenerationStep_1,,,1339,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
335,335_0,RUNNING,BoTorch,GenerationStep_1,,,222,853,0.250000000000000000000000000000,0.200000000000000011102230246252,1
336,336_0,COMPLETED,BoTorch,GenerationStep_1,0.621445482866043619196716463193,100.639867782592773437500000000000,401,263,0.250000000000000000000000000000,0.200000000000000011102230246252,4
337,337_0,FAILED,BoTorch,GenerationStep_1,,,123,161,0.001000000000000000020816681712,0.000000000000000000000000000000,1
338,338_0,FAILED,BoTorch,GenerationStep_1,,,947,880,0.250000000000000000000000000000,0.000000000000000000000000000000,4
339,339_0,FAILED,BoTorch,GenerationStep_1,,,631,914,0.250000000000000000000000000000,0.000000000000000000000000000000,4
340,340_0,FAILED,BoTorch,GenerationStep_1,,,322,862,0.250000000000000000000000000000,0.000000000000000000000000000000,2
341,341_0,FAILED,BoTorch,GenerationStep_1,,,125,248,0.001000000000000000020816681712,0.000000000000000000000000000000,1
342,342_0,FAILED,BoTorch,GenerationStep_1,,,991,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
343,343_0,COMPLETED,BoTorch,GenerationStep_1,0.607999999999999984900966865098,87.659255981445312500000000000000,396,364,0.250000000000000000000000000000,0.200000000000000011102230246252,4
344,344_0,FAILED,BoTorch,GenerationStep_1,,,781,916,0.250000000000000000000000000000,0.000000000000000000000000000000,4
345,345_0,FAILED,BoTorch,GenerationStep_1,,,1170,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
346,346_0,FAILED,BoTorch,GenerationStep_1,,,1905,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,1
347,347_0,FAILED,BoTorch,GenerationStep_1,,,46,269,0.001000000000000000020816681712,0.000000000000000000000000000000,1
348,348_0,COMPLETED,BoTorch,GenerationStep_1,0.518168224299065394156116326485,46.735487937927246093750000000000,2412,940,0.001000000000000000020816681712,0.200000000000000011102230246252,4
349,349_0,COMPLETED,BoTorch,GenerationStep_1,0.595800623052959554826202293043,83.498578071594238281250000000000,972,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
350,350_0,FAILED,BoTorch,GenerationStep_1,,,633,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
351,351_0,COMPLETED,BoTorch,GenerationStep_1,0.605345794392523339766398748907,103.132764816284179687500000000000,1373,871,0.250000000000000000000000000000,0.200000000000000011102230246252,4
352,352_0,FAILED,BoTorch,GenerationStep_1,,,382,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
353,353_0,COMPLETED,BoTorch,GenerationStep_1,0.592760124610591909721790671028,93.792900085449218750000000000000,3411,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4
354,354_0,FAILED,BoTorch,GenerationStep_1,,,588,394,0.001000000000000000020816681712,0.000000000000000000000000000000,4
355,355_0,FAILED,BoTorch,GenerationStep_1,,,431,422,0.001000000000000000020816681712,0.000000000000000000000000000000,4
356,356_0,FAILED,BoTorch,GenerationStep_1,,,410,211,0.250000000000000000000000000000,0.000000000000000000000000000000,4
357,357_0,FAILED,BoTorch,GenerationStep_1,,,3393,877,0.001000000000000000020816681712,0.000000000000000000000000000000,4
358,358_0,FAILED,BoTorch,GenerationStep_1,,,1036,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
359,359_0,FAILED,BoTorch,GenerationStep_1,,,2361,848,0.001000000000000000020816681712,0.000000000000000000000000000000,4
360,360_0,COMPLETED,BoTorch,GenerationStep_1,0.602292834890965744776281098893,97.334382057189941406250000000000,912,725,0.250000000000000000000000000000,0.200000000000000011102230246252,4
361,361_0,FAILED,BoTorch,GenerationStep_1,,,117,233,0.001000000000000000020816681712,0.000000000000000000000000000000,1
362,362_0,FAILED,BoTorch,GenerationStep_1,,,10,214,0.001000000000000000020816681712,0.000000000000000000000000000000,1
363,363_0,FAILED,BoTorch,GenerationStep_1,,,2384,977,0.250000000000000000000000000000,0.000000000000000000000000000000,1
364,364_0,COMPLETED,BoTorch,GenerationStep_1,0.635289719626168203348015595111,112.999103069305419921875000000000,150,100,0.001000000000000000020816681712,0.200000000000000011102230246252,1
365,365_0,FAILED,BoTorch,GenerationStep_1,,,804,742,0.250000000000000000000000000000,0.000000000000000000000000000000,4
366,366_0,COMPLETED,BoTorch,GenerationStep_1,0.554193146417445436924253954203,56.767404079437255859375000000000,356,695,0.250000000000000000000000000000,0.200000000000000011102230246252,1
367,367_0,FAILED,BoTorch,GenerationStep_1,,,133,184,0.250000000000000000000000000000,0.000000000000000000000000000000,1
368,368_0,FAILED,BoTorch,GenerationStep_1,,,118,619,0.001000000000000000020816681712,0.000000000000000000000000000000,1
369,369_0,FAILED,BoTorch,GenerationStep_1,,,52,216,0.250000000000000000000000000000,0.000000000000000000000000000000,4
370,370_0,FAILED,BoTorch,GenerationStep_1,,,2469,690,0.001000000000000000020816681712,0.000000000000000000000000000000,4
371,371_0,COMPLETED,BoTorch,GenerationStep_1,0.456224299065420557663941281135,32.433261632919311523437500000000,130,900,0.250000000000000000000000000000,0.200000000000000011102230246252,1
372,372_0,COMPLETED,BoTorch,GenerationStep_1,0.521009345794392508821601950331,44.554792642593383789062500000000,181,543,0.001000000000000000020816681712,0.200000000000000011102230246252,4
373,373_0,FAILED,BoTorch,GenerationStep_1,,,2319,901,0.001000000000000000020816681712,0.000000000000000000000000000000,4
374,374_0,FAILED,BoTorch,GenerationStep_1,,,1901,901,0.001000000000000000020816681712,0.000000000000000000000000000000,4
375,375_0,FAILED,BoTorch,GenerationStep_1,,,300,290,0.001000000000000000020816681712,0.000000000000000000000000000000,4
376,376_0,FAILED,BoTorch,GenerationStep_1,,,931,901,0.001000000000000000020816681712,0.000000000000000000000000000000,4
377,377_0,FAILED,BoTorch,GenerationStep_1,,,194,626,0.001000000000000000020816681712,0.000000000000000000000000000000,4
378,378_0,FAILED,BoTorch,GenerationStep_1,,,3621,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
379,379_0,COMPLETED,BoTorch,GenerationStep_1,0.587364485981308415141199930076,54.154119491577148437500000000000,105,209,0.250000000000000000000000000000,0.200000000000000011102230246252,1
380,380_0,FAILED,BoTorch,GenerationStep_1,,,346,923,0.250000000000000000000000000000,0.000000000000000000000000000000,4
381,381_0,COMPLETED,BoTorch,GenerationStep_1,0.585345794392523322002830354904,64.692959308624267578125000000000,2519,695,0.001000000000000000020816681712,0.200000000000000011102230246252,4
382,382_0,COMPLETED,BoTorch,GenerationStep_1,0.605806853582554483139688272786,107.333359718322753906250000000000,3760,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,1
383,383_0,FAILED,BoTorch,GenerationStep_1,,,346,691,0.250000000000000000000000000000,0.000000000000000000000000000000,1
384,384_0,FAILED,BoTorch,GenerationStep_1,,,87,266,0.001000000000000000020816681712,0.000000000000000000000000000000,1
385,385_0,FAILED,BoTorch,GenerationStep_1,,,472,312,0.250000000000000000000000000000,0.000000000000000000000000000000,4
386,386_0,FAILED,BoTorch,GenerationStep_1,,,534,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
387,387_0,FAILED,BoTorch,GenerationStep_1,,,71,208,0.250000000000000000000000000000,0.000000000000000000000000000000,1
388,388_0,COMPLETED,BoTorch,GenerationStep_1,0.478143302180685347835265019967,34.885934352874755859375000000000,114,614,0.001000000000000000020816681712,0.200000000000000011102230246252,4
389,389_0,FAILED,BoTorch,GenerationStep_1,,,155,898,0.001000000000000000020816681712,0.000000000000000000000000000000,1
390,390_0,FAILED,BoTorch,GenerationStep_1,,,97,236,0.001000000000000000020816681712,0.000000000000000000000000000000,1
391,391_0,FAILED,BoTorch,GenerationStep_1,,,142,196,0.001000000000000000020816681712,0.000000000000000000000000000000,1
392,392_0,FAILED,BoTorch,GenerationStep_1,,,21,221,0.001000000000000000020816681712,0.000000000000000000000000000000,1
393,393_0,FAILED,BoTorch,GenerationStep_1,,,195,627,0.250000000000000000000000000000,0.000000000000000000000000000000,4
394,394_0,FAILED,BoTorch,GenerationStep_1,,,311,637,0.001000000000000000020816681712,0.000000000000000000000000000000,1
395,395_0,FAILED,BoTorch,GenerationStep_1,,,1902,890,0.001000000000000000020816681712,0.000000000000000000000000000000,4
396,396_0,FAILED,BoTorch,GenerationStep_1,,,2322,905,0.250000000000000000000000000000,0.000000000000000000000000000000,4
397,397_0,FAILED,BoTorch,GenerationStep_1,,,2508,706,0.001000000000000000020816681712,0.000000000000000000000000000000,4
398,398_0,FAILED,BoTorch,GenerationStep_1,,,347,614,0.250000000000000000000000000000,0.000000000000000000000000000000,4
399,399_0,FAILED,BoTorch,GenerationStep_1,,,1146,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
400,400_0,FAILED,BoTorch,GenerationStep_1,,,337,278,0.001000000000000000020816681712,0.000000000000000000000000000000,4
401,401_0,FAILED,BoTorch,GenerationStep_1,,,55,189,0.250000000000000000000000000000,0.000000000000000000000000000000,4
402,402_0,FAILED,BoTorch,GenerationStep_1,,,386,905,0.001000000000000000020816681712,0.000000000000000000000000000000,4
403,403_0,FAILED,BoTorch,GenerationStep_1,,,101,209,0.250000000000000000000000000000,0.000000000000000000000000000000,1
404,404_0,FAILED,BoTorch,GenerationStep_1,,,2486,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
405,405_0,COMPLETED,BoTorch,GenerationStep_1,0.514267912772585655822865646769,43.485713958740234375000000000000,2308,852,0.001000000000000000020816681712,0.200000000000000011102230246252,4
406,406_0,FAILED,BoTorch,GenerationStep_1,,,277,298,0.001000000000000000020816681712,0.000000000000000000000000000000,4
407,407_0,COMPLETED,BoTorch,GenerationStep_1,0.458529595015576330041540131788,29.603178024291992187500000000000,10,227,0.001000000000000000020816681712,0.200000000000000011102230246252,1
408,408_0,FAILED,BoTorch,GenerationStep_1,,,508,950,0.250000000000000000000000000000,0.000000000000000000000000000000,4
409,409_0,FAILED,BoTorch,GenerationStep_1,,,376,724,0.001000000000000000020816681712,0.000000000000000000000000000000,1
410,410_0,FAILED,BoTorch,GenerationStep_1,,,122,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
411,411_0,FAILED,BoTorch,GenerationStep_1,,,271,869,0.250000000000000000000000000000,0.000000000000000000000000000000,4
412,412_0,COMPLETED,BoTorch,GenerationStep_1,0.610791277258566966956720989401,84.364272117614746093750000000000,118,170,0.250000000000000000000000000000,0.200000000000000011102230246252,1
413,413_0,COMPLETED,BoTorch,GenerationStep_1,0.599676012461059171343435991730,110.195054531097412109375000000000,1318,948,0.001000000000000000020816681712,0.200000000000000011102230246252,1
414,414_0,FAILED,BoTorch,GenerationStep_1,,,2396,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
415,415_0,COMPLETED,BoTorch,GenerationStep_1,0.594816199376947074561883255228,89.324652910232543945312500000000,987,970,0.250000000000000000000000000000,0.200000000000000011102230246252,1
416,416_0,FAILED,BoTorch,GenerationStep_1,,,436,261,0.250000000000000000000000000000,0.000000000000000000000000000000,1
417,417_0,FAILED,BoTorch,GenerationStep_1,,,986,911,0.250000000000000000000000000000,0.000000000000000000000000000000,4
418,418_0,FAILED,BoTorch,GenerationStep_1,,,2369,926,0.001000000000000000020816681712,0.000000000000000000000000000000,1
419,419_0,COMPLETED,BoTorch,GenerationStep_1,0.588598130841121447431874003087,92.248456954956054687500000000000,1350,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,1
420,420_0,FAILED,BoTorch,GenerationStep_1,,,106,234,0.001000000000000000020816681712,0.000000000000000000000000000000,1
421,421_0,FAILED,BoTorch,GenerationStep_1,,,100,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
422,422_0,FAILED,BoTorch,GenerationStep_1,,,77,231,0.250000000000000000000000000000,0.000000000000000000000000000000,4
423,423_0,FAILED,BoTorch,GenerationStep_1,,,2373,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
424,424_0,FAILED,BoTorch,GenerationStep_1,,,146,576,0.250000000000000000000000000000,0.000000000000000000000000000000,4
425,425_0,FAILED,BoTorch,GenerationStep_1,,,244,269,0.001000000000000000020816681712,0.000000000000000000000000000000,2
426,426_0,FAILED,BoTorch,GenerationStep_1,,,298,617,0.001000000000000000020816681712,0.000000000000000000000000000000,4
427,427_0,FAILED,BoTorch,GenerationStep_1,,,57,255,0.001000000000000000020816681712,0.000000000000000000000000000000,4
428,428_0,FAILED,BoTorch,GenerationStep_1,,,102,193,0.250000000000000000000000000000,0.000000000000000000000000000000,4
429,429_0,FAILED,BoTorch,GenerationStep_1,,,2315,907,0.250000000000000000000000000000,0.000000000000000000000000000000,4
430,430_0,FAILED,BoTorch,GenerationStep_1,,,1903,898,0.001000000000000000020816681712,0.000000000000000000000000000000,1
431,431_0,FAILED,BoTorch,GenerationStep_1,,,304,909,0.250000000000000000000000000000,0.000000000000000000000000000000,4
432,432_0,FAILED,BoTorch,GenerationStep_1,,,376,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
433,433_0,FAILED,BoTorch,GenerationStep_1,,,1074,678,0.001000000000000000020816681712,0.000000000000000000000000000000,4
434,434_0,FAILED,BoTorch,GenerationStep_1,,,216,917,0.250000000000000000000000000000,0.000000000000000000000000000000,1
435,435_0,FAILED,BoTorch,GenerationStep_1,,,26,203,0.250000000000000000000000000000,0.000000000000000000000000000000,4
436,436_0,FAILED,BoTorch,GenerationStep_1,,,2506,676,0.001000000000000000020816681712,0.000000000000000000000000000000,4
437,437_0,COMPLETED,BoTorch,GenerationStep_1,0.544971962616822458436161014106,48.596277475357055664062500000000,2492,997,0.250000000000000000000000000000,0.200000000000000011102230246252,4
438,438_0,FAILED,BoTorch,GenerationStep_1,,,375,297,0.001000000000000000020816681712,0.000000000000000000000000000000,1
439,439_0,FAILED,BoTorch,GenerationStep_1,,,100,122,0.250000000000000000000000000000,0.000000000000000000000000000000,4
440,440_0,FAILED,BoTorch,GenerationStep_1,,,111,310,0.001000000000000000020816681712,0.000000000000000000000000000000,4
441,441_0,FAILED,BoTorch,GenerationStep_1,,,400,629,0.250000000000000000000000000000,0.000000000000000000000000000000,4
442,442_0,FAILED,BoTorch,GenerationStep_1,,,209,666,0.001000000000000000020816681712,0.000000000000000000000000000000,1
443,443_0,FAILED,BoTorch,GenerationStep_1,,,104,171,0.250000000000000000000000000000,0.000000000000000000000000000000,1
444,444_0,FAILED,BoTorch,GenerationStep_1,,,396,856,0.250000000000000000000000000000,0.000000000000000000000000000000,4
445,445_0,COMPLETED,BoTorch,GenerationStep_1,0.591352024922118357785905118362,102.425573587417602539062500000000,924,929,0.250000000000000000000000000000,0.200000000000000011102230246252,4
446,446_0,FAILED,BoTorch,GenerationStep_1,,,650,938,0.250000000000000000000000000000,0.000000000000000000000000000000,4
447,447_0,COMPLETED,BoTorch,GenerationStep_1,0.543152647975077895736717437103,51.191238164901733398437500000000,370,733,0.001000000000000000020816681712,0.200000000000000011102230246252,1
448,448_0,FAILED,BoTorch,GenerationStep_1,,,2337,960,0.250000000000000000000000000000,0.000000000000000000000000000000,1
449,449_0,COMPLETED,BoTorch,GenerationStep_1,0.592984423676012450954431187711,151.953000307083129882812500000000,1165,918,0.250000000000000000000000000000,0.200000000000000011102230246252,4
450,450_0,FAILED,BoTorch,GenerationStep_1,,,1013,404,0.001000000000000000020816681712,0.194820071688176138513526325369,4
451,451_0,FAILED,BoTorch,GenerationStep_1,,,1011,403,0.001000000000000000020816681712,0.000000000000000000000000000000,1
452,452_0,COMPLETED,BoTorch,GenerationStep_1,0.607875389408099708887789347500,162.288460493087768554687500000000,1072,398,0.001000000000000000020816681712,0.200000000000000011102230246252,4
453,453_0,COMPLETED,BoTorch,GenerationStep_1,0.621183800623053006262352937483,235.386987447738647460937500000000,976,348,0.001000000000000000020816681712,0.200000000000000011102230246252,4
454,454_0,FAILED,BoTorch,GenerationStep_1,,,1030,417,0.001000000000000000020816681712,0.000000000000000000000000000000,4
455,455_0,FAILED,BoTorch,GenerationStep_1,,,1088,396,0.250000000000000000000000000000,0.000000000000000000000000000000,4
456,456_0,COMPLETED,BoTorch,GenerationStep_1,0.612436137071651121033255549264,206.842712879180908203125000000000,1025,414,0.250000000000000000000000000000,0.200000000000000011102230246252,1
457,457_0,COMPLETED,BoTorch,GenerationStep_1,0.615214953271028042181001183053,178.095405817031860351562500000000,1106,388,0.001000000000000000020816681712,0.200000000000000011102230246252,1
458,458_0,FAILED,BoTorch,GenerationStep_1,,,987,374,0.001000000000000000020816681712,0.000000000000000000000000000000,1
459,459_0,FAILED,BoTorch,GenerationStep_1,,,953,311,0.001000000000000000020816681712,0.000000000000000000000000000000,4
460,460_0,COMPLETED,BoTorch,GenerationStep_1,0.608348909657320913169087361894,729.475465536117553710937500000000,3400,100,0.001000000000000000020816681712,0.200000000000000011102230246252,1
461,461_0,FAILED,BoTorch,GenerationStep_1,,,1000,389,0.250000000000000000000000000000,0.000000000000000000000000000000,4
462,462_0,FAILED,BoTorch,GenerationStep_1,,,2177,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
463,463_0,COMPLETED,BoTorch,GenerationStep_1,0.610093457943925221442782458325,146.880753755569458007812500000000,1033,419,0.001000000000000000020816681712,0.200000000000000011102230246252,1
464,464_0,FAILED,BoTorch,GenerationStep_1,,,1057,391,0.001000000000000000020816681712,0.000000000000000000000000000000,1
465,465_0,COMPLETED,BoTorch,GenerationStep_1,0.616610591900311533208878245205,136.814507484436035156250000000000,1021,443,0.001000000000000000020816681712,0.200000000000000011102230246252,4
466,466_0,COMPLETED,BoTorch,GenerationStep_1,0.617732087227414350394383291132,172.473083019256591796875000000000,1050,402,0.001000000000000000020816681712,0.200000000000000011102230246252,4
467,467_0,FAILED,BoTorch,GenerationStep_1,,,713,768,0.250000000000000000000000000000,0.000000000000000000000000000000,4
468,468_0,COMPLETED,BoTorch,GenerationStep_1,0.621445482866043619196716463193,292.183548688888549804687500000000,3405,286,0.001000000000000000020816681712,0.200000000000000011102230246252,1
469,469_0,FAILED,BoTorch,GenerationStep_1,,,536,375,0.250000000000000000000000000000,0.000000000000000000000000000000,4
470,470_0,FAILED,BoTorch,GenerationStep_1,,,890,225,0.001000000000000000020816681712,0.000000000000000000000000000000,4
471,471_0,FAILED,BoTorch,GenerationStep_1,,,3326,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
472,472_0,FAILED,BoTorch,GenerationStep_1,,,2120,598,0.001000000000000000020816681712,0.000000000000000000000000000000,1
473,473_0,FAILED,BoTorch,GenerationStep_1,,,3455,377,0.001000000000000000020816681712,0.000000000000000000000000000000,4
474,474_0,COMPLETED,BoTorch,GenerationStep_1,0.647302180685358274914165122027,79.564831018447875976562500000000,95,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
475,475_0,FAILED,BoTorch,GenerationStep_1,,,1016,439,0.250000000000000000000000000000,0.000000000000000000000000000000,4
476,476_0,FAILED,BoTorch,GenerationStep_1,,,599,590,0.250000000000000000000000000000,0.000000000000000000000000000000,4
477,477_0,FAILED,BoTorch,GenerationStep_1,,,3446,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
478,478_0,COMPLETED,BoTorch,GenerationStep_1,0.607738317757009371966603339388,149.792009592056274414062500000000,998,388,0.250000000000000000000000000000,0.200000000000000011102230246252,1
479,479_0,FAILED,BoTorch,GenerationStep_1,,,836,105,0.001000000000000000020816681712,0.000000000000000000000000000000,4
480,480_0,COMPLETED,BoTorch,GenerationStep_1,0.599750778816199425769184472301,95.643185615539550781250000000000,100,178,0.250000000000000000000000000000,0.200000000000000011102230246252,1
481,481_0,FAILED,BoTorch,GenerationStep_1,,,83,226,0.250000000000000000000000000000,0.000000000000000000000000000000,4
482,482_0,FAILED,BoTorch,GenerationStep_1,,,2418,1000,0.001000000000000000020816681712,0.000000000000000000000000000000,4
483,483_0,COMPLETED,BoTorch,GenerationStep_1,0.576299065420560752137646431947,60.447050333023071289062500000000,82,210,0.250000000000000000000000000000,0.200000000000000011102230246252,1
484,484_0,COMPLETED,BoTorch,GenerationStep_1,0.634816199376946999066717580718,76.210617542266845703125000000000,93,115,0.250000000000000000000000000000,0.200000000000000011102230246252,1
485,485_0,COMPLETED,BoTorch,GenerationStep_1,0.622242990654205629930117993354,89.996751785278320312500000000000,99,143,0.250000000000000000000000000000,0.200000000000000011102230246252,1
486,486_0,FAILED,BoTorch,GenerationStep_1,,,2330,923,0.250000000000000000000000000000,0.000000000000000000000000000000,1
487,487_0,FAILED,BoTorch,GenerationStep_1,,,1904,872,0.001000000000000000020816681712,0.000000000000000000000000000000,4
488,488_0,COMPLETED,BoTorch,GenerationStep_1,0.555626168224299110676156487898,53.344884157180786132812500000000,292,597,0.250000000000000000000000000000,0.200000000000000011102230246252,4
489,489_0,COMPLETED,BoTorch,GenerationStep_1,0.559065420560747705636117643735,48.399779319763183593750000000000,92,252,0.001000000000000000020816681712,0.200000000000000011102230246252,1
490,490_0,FAILED,BoTorch,GenerationStep_1,,,335,917,0.001000000000000000020816681712,0.000000000000000000000000000000,1
491,491_0,FAILED,BoTorch,GenerationStep_1,,,429,669,0.250000000000000000000000000000,0.000000000000000000000000000000,4
492,492_0,RUNNING,BoTorch,GenerationStep_1,,,37,225,0.001000000000000000020816681712,0.200000000000000011102230246252,4
493,493_0,FAILED,BoTorch,GenerationStep_1,,,4000,696,0.001000000000000000020816681712,0.000000000000000000000000000000,4
494,494_0,COMPLETED,BoTorch,GenerationStep_1,0.513682242990654236436398605292,45.641184806823730468750000000000,343,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4
495,495_0,FAILED,BoTorch,GenerationStep_1,,,2486,678,0.250000000000000000000000000000,0.000000000000000000000000000000,1
496,496_0,FAILED,BoTorch,GenerationStep_1,,,2359,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,4
497,497_0,COMPLETED,BoTorch,GenerationStep_1,0.499015576323987519735680962185,37.919765710830688476562500000000,125,561,0.250000000000000000000000000000,0.200000000000000011102230246252,1
498,498_0,COMPLETED,BoTorch,GenerationStep_1,0.560286604361370677018783226231,45.273358821868896484375000000000,65,221,0.001000000000000000020816681712,0.200000000000000011102230246252,4
499,499_0,FAILED,BoTorch,GenerationStep_1,,,352,1000,0.250000000000000000000000000000,0.000000000000000000000000000000,1
500,500_0,FAILED,BoTorch,GenerationStep_1,,,19,199,0.250000000000000000000000000000,0.000000000000000000000000000000,1
501,501_0,FAILED,BoTorch,GenerationStep_1,,,346,656,0.001000000000000000020816681712,0.000000000000000000000000000000,4
502,502_0,FAILED,BoTorch,GenerationStep_1,,,3128,788,0.250000000000000000000000000000,0.000000000000000000000000000000,4
503,503_0,FAILED,BoTorch,GenerationStep_1,,,95,202,0.001000000000000000020816681712,0.200000000000000011102230246252,4
504,504_0,COMPLETED,BoTorch,GenerationStep_1,0.614741433021806837899703168659,81.977911233901977539062500000000,293,293,0.001000000000000000020816681712,0.200000000000000011102230246252,1
505,505_0,FAILED,BoTorch,GenerationStep_1,,,416,815,0.250000000000000000000000000000,0.000000000000000000000000000000,1
506,506_0,FAILED,BoTorch,GenerationStep_1,,,81,272,0.001000000000000000020816681712,0.000000000000000000000000000000,4
507,507_0,FAILED,BoTorch,GenerationStep_1,,,2437,398,0.250000000000000000000000000000,0.000000000000000000000000000000,4
508,508_0,FAILED,BoTorch,GenerationStep_1,,,431,934,0.250000000000000000000000000000,0.000000000000000000000000000000,4
509,509_0,FAILED,BoTorch,GenerationStep_1,,,85,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
</pre>
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			<h1>&nbsp;Args Overview</h1>
			<h2>Arguments Overview:                                                                                           </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Key</th><th>Value                                                                                                                                                    </th></tr></thead><tbody><tr><td> config_yaml</td><td>None                                                                                                                                                     </td></tr><tr><td> config_toml</td><td>None                                                                                                                                                     </td></tr><tr><td> config_json</td><td>None                                                                                                                                                     </td></tr><tr><td> num_random_steps</td><td>20                                                                                                                                                       </td></tr><tr><td> max_eval</td><td>500                                                                                                                                                      </td></tr><tr><td> run_program</td><td>[[&#039;bW9kdWxlIGxvYWQgR0NDY29yZS8xMC4zLjAgUHl0aG9uICYmIHNvdXJjZSAvZGF0YS9ob3JzZS93cy9zNDEyMjQ4NS1jb21wUGVyZkREL2JlbmNobWFyay92ZW52L2Jpbi9hY3RpdmF0ZSAmJiBw&hellip; </td></tr><tr><td> experiment_name</td><td>CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACC-RUNTIME                                                                                          </td></tr><tr><td> mem_gb</td><td>32                                                                                                                                                       </td></tr><tr><td> parameter</td><td>[[&#039;recent_samples_size&#039;, &#039;range&#039;, &#039;10&#039;, &#039;4000&#039;, &#039;int&#039;], [&#039;n_samples&#039;, &#039;range&#039;, &#039;100&#039;, &#039;1000&#039;, &#039;int&#039;], [&#039;confidence&#039;, &#039;choice&#039;,                           </td></tr><tr><td></td><td>&#039;0.25,0.1,0.05,0.025,0.01,0.005,0.001&#039;], [&#039;feature_proportion&#039;, &#039;range&#039;, &#039;0.0&#039;, &#039;0.2&#039;, &#039;float&#039;], [&#039;n_clusters&#039;, &#039;range&#039;, &#039;1&#039;, &#039;4&#039;, &#039;int&#039;]]               </td></tr><tr><td> continue_previous_job</td><td>None                                                                                                                                                     </td></tr><tr><td> maximize</td><td>False                                                                                                                                                    </td></tr><tr><td> experiment_constraints</td><td>None                                                                                                                                                     </td></tr><tr><td> stderr_to_stdout</td><td>False                                                                                                                                                    </td></tr><tr><td> run_dir</td><td>runs                                                                                                                                                     </td></tr><tr><td> seed</td><td>None                                                                                                                                                     </td></tr><tr><td> decimalrounding</td><td>12                                                                                                                                                       </td></tr><tr><td> enforce_sequential_optimization</td><td>False                                                                                                                                                    </td></tr><tr><td> verbose_tqdm</td><td>False                                                                                                                                                    </td></tr><tr><td> model</td><td>BOTORCH_MODULAR                                                                                                                                          </td></tr><tr><td> gridsearch</td><td>False                                                                                                                                                    </td></tr><tr><td> occ</td><td>False                                                                                                                                                    </td></tr><tr><td> show_sixel_scatter</td><td>False                                                                                                                                                    </td></tr><tr><td> show_sixel_general</td><td>False                                                                                                                                                    </td></tr><tr><td> show_sixel_trial_index_result</td><td>False                                                                                                                                                    </td></tr><tr><td> follow</td><td>False                                                                                                                                                    </td></tr><tr><td> send_anonymized_usage_stats</td><td>True                                                                                                                                                     </td></tr><tr><td> ui_url</td><td>None                                                                                                                                                     </td></tr><tr><td> root_venv_dir</td><td>/home/s4122485                                                                                                                                           </td></tr><tr><td> exclude</td><td>None                                                                                                                                                     </td></tr><tr><td> main_process_gb</td><td>8                                                                                                                                                        </td></tr><tr><td> pareto_front_confidence</td><td>1                                                                                                                                                        </td></tr><tr><td> max_nr_of_zero_results</td><td>50                                                                                                                                                       </td></tr><tr><td> disable_search_space_exhaustion_detection</td><td>False                                                                                                                                                    </td></tr><tr><td> abbreviate_job_names</td><td>False                                                                                                                                                    </td></tr><tr><td> orchestrator_file</td><td>None                                                                                                                                                     </td></tr><tr><td> checkout_to_latest_tested_version</td><td>False                                                                                                                                                    </td></tr><tr><td> live_share</td><td>False                                                                                                                                                    </td></tr><tr><td> disable_tqdm</td><td>False                                                                                                                                                    </td></tr><tr><td> workdir</td><td>False                                                                                                                                                    </td></tr><tr><td> max_parallelism</td><td>max_eval_times_thousand_plus_thousand                                                                                                                    </td></tr><tr><td> occ_type</td><td>euclid                                                                                                                                                   </td></tr><tr><td> result_names</td><td>[&#039;ACCURACY=max&#039;, &#039;RUNTIME=min&#039;]                                                                                                                          </td></tr><tr><td> minkowski_p</td><td>2                                                                                                                                                        </td></tr><tr><td> signed_weighted_euclidean_weights</td><td></td></tr><tr><td> generation_strategy</td><td>None                                                                                                                                                     </td></tr><tr><td> generate_all_jobs_at_once</td><td>True                                                                                                                                                     </td></tr><tr><td> revert_to_random_when_seemingly_exhausted</td><td>True                                                                                                                                                     </td></tr><tr><td> num_parallel_jobs</td><td>30                                                                                                                                                       </td></tr><tr><td> worker_timeout</td><td>30                                                                                                                                                       </td></tr><tr><td> slurm_use_srun</td><td>False                                                                                                                                                    </td></tr><tr><td> time</td><td>1260                                                                                                                                                     </td></tr><tr><td> partition</td><td>romeo                                                                                                                                                    </td></tr><tr><td> reservation</td><td>None                                                                                                                                                     </td></tr><tr><td> force_local_execution</td><td>False                                                                                                                                                    </td></tr><tr><td> slurm_signal_delay_s</td><td>0                                                                                                                                                        </td></tr><tr><td> nodes_per_job</td><td>1                                                                                                                                                        </td></tr><tr><td> cpus_per_task</td><td>1                                                                                                                                                        </td></tr><tr><td> account</td><td>None                                                                                                                                                     </td></tr><tr><td> gpus</td><td>0                                                                                                                                                        </td></tr><tr><td> run_mode</td><td>local                                                                                                                                                    </td></tr><tr><td> verbose</td><td>False                                                                                                                                                    </td></tr><tr><td> verbose_break_run_search_table</td><td>False                                                                                                                                                    </td></tr><tr><td> debug</td><td>False                                                                                                                                                    </td></tr><tr><td> no_sleep</td><td>False                                                                                                                                                    </td></tr><tr><td> tests</td><td>False                                                                                                                                                    </td></tr><tr><td> show_worker_percentage_table_at_end</td><td>False                                                                                                                                                    </td></tr><tr><td> auto_exclude_defective_hosts</td><td>False                                                                                                                                                    </td></tr><tr><td> run_tests_that_fail_on_taurus</td><td>False                                                                                                                                                    </td></tr><tr><td> raise_in_eval</td><td>False                                                                                                                                                    </td></tr><tr><td> show_ram_every_n_seconds</td><td>False                                                                                                                                                    </td></tr></tbody></table>
			<h1>&nbsp;Worker-Usage</h1>
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<pre id="pre_tab_worker_usage">1742403747.6485472,30,0,0
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1742429678.301037,30,15,50
1742429678.3377492,30,15,50
1742429678.4882252,30,15,50
1742429683.5776181,30,16,53
1742429683.621449,30,16,53
1742429683.8972332,30,16,53
1742429685.5444736,30,17,57
1742429685.5812516,30,17,57
1742429685.7169235,30,17,57
1742429691.215522,30,18,60
1742429691.2578568,30,18,60
1742429691.4678597,30,18,60
1742429693.2104383,30,19,63
1742429693.254267,30,19,63
1742429693.42186,30,19,63
1742429699.5085745,30,20,67
1742429699.5512037,30,20,67
1742429699.6984003,30,20,67
1742429701.207596,30,21,70
1742429701.2743268,30,21,70
1742429701.488729,30,21,70
1742429706.5901322,30,22,73
1742429706.7618098,30,22,73
1742429706.9203918,30,22,73
1742429708.4771125,30,23,77
1742429708.5156748,30,23,77
1742429708.6633139,30,23,77
1742429714.0216808,30,24,80
1742429714.0600243,30,24,80
1742429714.2224348,30,24,80
1742429715.8929784,30,25,83
1742429716.04773,30,25,83
1742429716.2076523,30,25,83
1742429718.2525165,30,26,87
1742429718.2917156,30,26,87
1742429718.4745088,30,26,87
1742429720.1971014,30,27,90
1742429720.4374423,30,27,90
1742429720.6566477,30,27,90
1742429722.1871324,30,28,93
1742429722.2244744,30,28,93
1742429722.3825574,30,28,93
1742429724.3099215,30,29,97
1742429724.3576365,30,29,97
1742429724.6535907,30,29,97
1742429726.2633812,30,30,100
1742429726.4814043,30,30,100
1742429728.1966364,30,29,97
1742429729.1713536,30,28,93
1742429730.2544522,30,27,90
1742429731.2906005,30,26,87
1742429732.7816863,30,25,83
1742429734.1139817,30,24,80
1742429735.1686666,30,23,77
1742429736.2781098,30,22,73
1742429737.2890391,30,21,70
1742429738.870225,30,20,67
1742429740.3348992,30,19,63
1742429741.593318,30,18,60
1742429742.8431475,30,17,57
1742429743.8795733,30,16,53
1742429745.4016407,30,15,50
1742429748.7141936,30,14,47
1742429748.836019,30,14,47
1742429752.378018,30,14,47
1742429758.0196223,30,14,47
1742429760.0492895,30,13,43
1742429763.5643797,30,12,40
1742429763.6951842,30,12,40
1742429771.272863,30,12,40
1742429778.9092948,30,12,40
1742429784.2084212,30,12,40
1742429787.2870374,30,11,37
1742429787.3579788,30,11,37
1742429795.2245066,30,11,37
1742429802.8732703,30,11,37
1742429810.504135,30,11,37
1742429817.7582202,30,11,37
1742429823.0196805,30,11,37
1742429823.1821942,30,11,37
1742429826.337376,30,10,33
1742429826.402946,30,10,33
1742429834.0078917,30,10,33
1742429839.338512,30,10,33
1742429839.4762936,30,10,33
1742429842.5981739,30,9,30
1742429842.6755552,30,9,30
1742429848.0009155,30,9,30
1742429848.0929027,30,9,30
1742429851.3120282,30,8,27
1742429851.4292243,30,8,27
1742429856.6964636,30,8,27
1742429856.7726233,30,8,27
1742429859.8869145,30,7,23
1742429860.0498893,30,7,23
1742429867.298357,30,7,23
1742429872.5428462,30,7,23
1742429872.7362797,30,7,23
1742429875.8113852,30,6,20
1742429876.0077345,30,6,20
1742429883.4347303,30,6,20
1742429888.7830336,30,6,20
1742429888.871365,30,6,20
1742429892.0940425,30,5,17
1742429892.23983,30,5,17
1742429899.3955863,30,5,17
1742429904.7880197,30,5,17
1742429904.877648,30,5,17
1742429906.2029321,30,4,13
1742429906.3128586,30,4,13
1742429909.1367245,30,3,10
1742429909.2229452,30,3,10
1742429914.684345,30,3,10
1742429914.76617,30,3,10
1742429917.8109004,30,2,7
1742429917.9183323,30,2,7
1742429925.3395104,30,2,7
1742429932.8849914,30,2,7
1742429940.1667147,30,2,7
1742429947.7089355,30,2,7
1742429955.2458956,30,2,7
1742429962.5612042,30,2,7
1742429970.1696641,30,2,7
1742429977.777208,30,2,7
1742429985.1085193,30,2,7
1742429992.7062595,30,2,7
1742430000.152947,30,2,7
1742430007.9027572,30,2,7
1742430013.3046663,30,2,7
1742430013.383005,30,2,7
1742430016.337688,30,1,3
1742430016.5588503,30,1,3
1742430024.1033952,30,1,3
1742430031.5595527,30,1,3
1742430038.7183552,30,1,3
1742430046.1845474,30,1,3
1742430053.6975808,30,1,3
1742430060.857049,30,1,3
1742430068.2658446,30,1,3
1742430075.820115,30,1,3
1742430083.0226283,30,1,3
1742430090.529243,30,1,3
1742430098.0887709,30,1,3
1742430105.2642484,30,1,3
1742430112.8305333,30,1,3
1742430120.3693485,30,1,3
1742430127.5637488,30,1,3
1742430135.1017816,30,1,3
1742430142.6270063,30,1,3
1742430149.8458145,30,1,3
1742430157.3977633,30,1,3
1742430164.6124763,30,1,3
1742430172.03449,30,1,3
1742430179.6456175,30,1,3
1742430186.956566,30,1,3
1742430194.5430837,30,1,3
1742430201.9896467,30,1,3
1742430209.1682806,30,1,3
1742430216.7114992,30,1,3
1742430224.3781068,30,1,3
1742430231.6020956,30,1,3
1742430239.0936139,30,1,3
1742430246.6134772,30,1,3
1742430253.7887037,30,1,3
1742430261.285968,30,1,3
1742430268.8066204,30,1,3
1742430276.0691476,30,1,3
1742430283.8573601,30,1,3
1742430291.8965538,30,1,3
1742430299.23672,30,1,3
1742430306.9277337,30,1,3
1742430314.7325938,30,1,3
1742430322.0901845,30,1,3
1742430329.9220433,30,1,3
1742430337.6262848,30,1,3
1742430344.9615746,30,1,3
1742430352.760539,30,1,3
1742430360.4240456,30,1,3
1742430367.7155776,30,1,3
1742430375.1803992,30,1,3
1742430382.3825173,30,1,3
1742430390.0496507,30,1,3
1742430397.673292,30,1,3
1742430404.8807123,30,1,3
1742430412.4512722,30,1,3
1742430419.897908,30,1,3
1742430427.1185737,30,1,3
1742430432.358349,30,1,3
1742430432.5217085,30,1,3
1742430435.5319345,30,0,0
1742430437.9698393,30,0,0
1742430440.52998,30,0,0
1742430496.122651,30,0,0
1742430545.878839,30,0,0
1742430595.5853186,30,0,0
1742430641.5514345,30,0,0
1742430712.5059927,30,0,0
1742430766.6524582,30,0,0
1742430839.8956172,30,0,0
1742430897.227218,30,0,0
1742430951.5006082,30,0,0
1742431008.3175168,30,0,0
1742431061.0194335,30,0,0
1742431114.2186468,30,0,0
1742431185.5070117,30,0,0
1742431270.0708597,30,0,0
1742431350.8950555,30,0,0
1742431425.6312578,30,0,0
1742431516.2058651,30,0,0
1742431600.183927,30,0,0
1742431687.8356621,30,0,0
1742431760.3512676,30,0,0
1742431843.2923388,30,0,0
1742431911.3020296,30,0,0
1742431991.3603642,30,0,0
1742432073.9757998,30,0,0
1742432166.1697056,30,0,0
1742432262.519616,30,0,0
1742432343.9195983,30,0,0
1742432423.4034503,30,0,0
1742432500.7110622,30,0,0
1742432589.744004,30,0,0
1742432590.1185088,30,0,0
1742432592.0976906,30,1,3
1742432592.2889578,30,1,3
1742432592.5815263,30,1,3
1742432594.3299656,30,2,7
1742432594.3713977,30,2,7
1742432594.5717416,30,2,7
1742432597.1126099,30,3,10
1742432597.2323472,30,3,10
1742432597.4400432,30,3,10
1742432599.2601454,30,4,13
1742432599.316586,30,4,13
1742432599.6804907,30,4,13
1742432601.3750784,30,5,17
1742432601.4416459,30,5,17
1742432601.8119764,30,5,17
1742432604.4866402,30,6,20
1742432604.5367563,30,6,20
1742432604.8242328,30,6,20
1742432606.497343,30,7,23
1742432606.5504763,30,7,23
1742432606.8790717,30,7,23
1742432608.7462907,30,8,27
1742432608.8818283,30,8,27
1742432609.2145922,30,8,27
1742432611.1441436,30,9,30
1742432611.2416027,30,9,30
1742432611.549094,30,9,30
1742432613.341205,30,10,33
1742432613.4695055,30,10,33
1742432613.74431,30,10,33
1742432615.3847814,30,11,37
1742432615.441975,30,11,37
1742432615.765139,30,11,37
1742432617.570797,30,12,40
1742432617.6272259,30,12,40
1742432617.9230287,30,12,40
1742432619.7588139,30,13,43
1742432619.813658,30,13,43
1742432620.1080725,30,13,43
1742432622.1734614,30,14,47
1742432622.2326732,30,14,47
1742432622.736587,30,14,47
1742432624.6371562,30,15,50
1742432624.7368317,30,15,50
1742432625.053465,30,15,50
1742432627.2885342,30,16,53
1742432627.340375,30,16,53
1742432627.6809733,30,16,53
1742432629.5964336,30,17,57
1742432629.6940565,30,17,57
1742432630.0806248,30,17,57
1742432632.0632403,30,18,60
1742432632.1715715,30,18,60
1742432632.4431498,30,18,60
1742432634.3919613,30,19,63
1742432634.4591634,30,19,63
1742432634.7449472,30,19,63
1742432636.604979,30,20,67
1742432636.655997,30,20,67
1742432636.9139757,30,20,67
1742432638.5911367,30,21,70
1742432638.6977577,30,21,70
1742432638.9661222,30,21,70
1742432640.9662478,30,22,73
1742432641.1334972,30,22,73
1742432641.4703515,30,22,73
1742432643.3802242,30,23,77
1742432643.4343941,30,23,77
1742432643.7779646,30,23,77
1742432646.548002,30,24,80
1742432646.6428232,30,24,80
1742432646.920372,30,24,80
1742432648.5574815,30,25,83
1742432648.6261616,30,25,83
1742432648.9085922,30,25,83
1742432650.5705366,30,26,87
1742432650.6149726,30,26,87
1742432650.8567774,30,26,87
1742432652.865331,30,27,90
1742432652.9956918,30,27,90
1742432653.3111832,30,27,90
1742432655.3899858,30,28,93
1742432655.4516425,30,28,93
1742432655.7512848,30,28,93
1742432657.4261837,30,29,97
1742432657.4767087,30,29,97
1742432657.8480964,30,29,97
1742432659.7429183,30,30,100
1742432660.2573977,30,30,100
1742432662.3327072,30,29,97
1742432664.3453095,30,28,93
1742432665.9290786,30,27,90
1742432667.5076144,30,26,87
1742432669.665808,30,25,83
1742432671.2992525,30,24,80
1742432672.9199693,30,23,77
1742432674.5145438,30,22,73
1742432676.2681577,30,21,70
1742432678.356795,30,20,67
1742432680.1128085,30,19,63
1742432681.9454863,30,18,60
1742432683.6272275,30,17,57
1742432687.7618966,30,16,53
1742432688.046845,30,15,50
1742432688.586304,30,15,50
1742432688.7375154,30,15,50
1742432690.0797217,30,14,47
1742432690.2839031,30,14,47
1742432694.1884944,30,13,43
1742432695.5105011,30,13,43
1742432700.845807,30,13,43
1742432700.980331,30,13,43
1742432702.512884,30,12,40
1742432702.651402,30,12,40
1742432703.9239461,30,11,37
1742432704.1421978,30,11,37
1742432705.471576,30,10,33
1742432707.420126,30,9,30
1742432709.5252125,30,8,27
1742432711.1433408,30,7,23
1742432715.065136,30,6,20
1742432715.1867225,30,6,20
1742432720.6613476,30,6,20
1742432720.8163013,30,6,20
1742432722.0839705,30,5,17
1742432722.2401085,30,5,17
1742432723.56666,30,4,13
1742432723.7278967,30,4,13
1742432727.6015027,30,3,10
1742432727.8354192,30,3,10
1742432733.4294114,30,3,10
1742432733.572064,30,3,10
1742432737.4013426,30,2,7
1742432737.5263906,30,2,7
1742432745.5826705,30,2,7
1742432751.0076401,30,2,7
1742432754.9032655,30,1,3
1742432755.1197357,30,1,3
1742432762.5166638,30,1,3
1742432770.6011639,30,1,3
1742432778.7934647,30,1,3
1742432786.3585324,30,1,3
1742432791.9718688,30,1,3
1742432792.1299202,30,1,3
1742432796.1782303,30,0,0
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'> Download &raquo;worker_usage.csv&laquo; as file</button>
			
			<h1>&nbsp;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 &raquo;cpu_ram_usage.csv&laquo; as file</button>
<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1742403747,590.19140625,0.5
1742403747,590.23046875,1.3
1742403747,590.33203125,0.8
1742403747,590.33203125,2.1
1742403747,590.33203125,0.0
1742403747,590.33203125,1.2
1742403747,590.33203125,0.0
1742403840,603.21875,1.0
1742403840,603.28515625,0.7
1742403840,603.28515625,1.3
1742403840,603.28515625,0.0
1742405477,686.8046875,2.1
1742405477,686.8046875,1.1
1742405477,686.8046875,1.5
1742405477,686.8046875,1.5
1742406960,690.76171875,3.7
1742406960,690.76171875,8.6
1742406960,690.76171875,5.5
1742406960,690.76171875,5.2
1742408623,687.4453125,5.1
1742408623,687.4453125,2.9
1742408623,687.4453125,3.8
1742408623,687.4453125,3.5
1742409750,701.34375,5.8
1742409750,701.34375,4.0
1742409750,701.34375,5.3
1742409750,701.34375,6.0
1742410847,710.8203125,6.2
1742410847,710.8203125,3.6
1742410847,710.8203125,4.2
1742410847,710.8203125,5.7
1742412065,717.51953125,5.8
1742412065,717.51953125,6.5
1742412065,717.51953125,6.7
1742412065,717.51953125,6.4
1742413569,729.06640625,6.9
1742413569,729.06640625,9.8
1742413569,729.06640625,8.1
1742413569,729.06640625,5.7
1742414899,732.9296875,6.7
1742414899,732.9296875,7.9
1742414899,732.9296875,7.5
1742414899,732.9296875,5.4
1742416988,724.8828125,6.9
1742416988,724.8828125,7.0
1742416988,724.8828125,5.7
1742416988,724.8828125,5.8
1742419172,769.88671875,6.6
1742419172,769.88671875,4.0
1742419172,769.88671875,5.5
1742419172,769.88671875,5.9
1742421225,746.51171875,7.1
1742421225,746.51171875,3.4
1742421226,746.51171875,5.8
1742421226,746.51171875,5.7
1742423198,738.0,6.5
1742423198,738.0,5.0
1742423198,738.0,5.9
1742423198,738.0,2.9
1742425492,741.16015625,6.8
1742425492,741.16015625,6.1
1742425492,741.16015625,6.0
1742425492,741.16015625,6.7
1742427869,744.48046875,6.4
1742427869,744.48046875,2.6
1742427869,744.48046875,7.0
1742427869,744.48046875,7.7
1742429752,804.95703125,5.9
1742429752,804.95703125,2.8
1742429752,804.95703125,4.9
1742429752,804.95703125,8.6
1742432695,757.5234375,6.5
1742432695,757.5234375,6.2
1742432695,757.5234375,6.3
1742432695,757.5234375,5.6
1742432799,757.41796875,5.5
1742432799,757.41796875,2.8
</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 &raquo;cpu_ram_usage.csv&laquo; as file</button>
			
			<h1>&nbsp;Parallel Plot</h1>
			<div class="invert_in_dark_mode" id="parallel-plot"></div>
			<h1>&nbsp;Scatter-2D</h1>
			<div class='invert_in_dark_mode' id='plotScatter2d'></div>
			<h1>&nbsp;Scatter-3D</h1>
			<div class='invert_in_dark_mode' id='plotScatter3d'></div>
			<h1>&nbsp;Job Status Distribution</h1>
			<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
			<h1>&nbsp;Boxplots</h1>
			<div class="invert_in_dark_mode" id="plotBoxplot"></div>
			<h1>&nbsp;Violin</h1>
			<div class="invert_in_dark_mode" id="plotViolin"></div>
			<h1>&nbsp;Histogram</h1>
			<div class="invert_in_dark_mode" id="plotHistogram"></div>
			<h1>&nbsp;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>
			
			<h1>&nbsp;Result-Pairs</h1>
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