OmniOpt2

Setting	Value
Max. nr. evaluations	50372
Max. nr. evaluations (from arguments)	50000
Number random steps	20
Nr. of workers (parameter)	20
Main process memory (GB)	8
Worker memory (GB)	32
Nr. imported jobs	372
Name	Type	Lower bound	Upper bound	Values	Type	Log Scale?
recent_samples_size	int	1	5000		int	No
n_samples	int	1	5000		int	No
confidence	choice			0.25, 0.1, 0.05, 0.025, 0.01, 0.005, 0.001
feature_proportion	float	0.001	0.999		float	No
n_clusters	int	1	50		int	No
Failed	Succeeded	Running	Total
22	550	2	574
name	min/max
ACCURACY	max
RUNTIME	min
Key	Value
config_yaml	None
config_toml	None
config_json	None
num_random_steps	20
max_eval	50000
run_program	None
experiment_name	None
mem_gb	32
parameter	None
continue_previous_job	/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/
experiment_constraints	None
run_dir	runs
seed	None
decimalrounding	4
enforce_sequential_optimization	False
verbose_tqdm	False
model	None
gridsearch	False
occ	False
show_sixel_scatter	False
show_sixel_general	False
show_sixel_trial_index_result	False
follow	False
send_anonymized_usage_stats	False
ui_url	None
root_venv_dir	/home/s4122485
exclude	None
main_process_gb	8
pareto_front_confidence	1
max_nr_of_zero_results	10
abbreviate_job_names	False
orchestrator_file	None
checkout_to_latest_tested_version	False
live_share	False
disable_tqdm	False
workdir	False
occ_type	euclid
result_names	['RESULT=min']
minkowski_p	2
signed_weighted_euclidean_weights
generation_strategy	None
generate_all_jobs_at_once	False
revert_to_random_when_seemingly_exhausted	True
load_data_from_existing_jobs	[]
n_estimators_randomforest	100
external_generator	None
username	None
max_failed_jobs	None
num_parallel_jobs	20
worker_timeout	120
slurm_use_srun	False
time
partition
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
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		<title>Exported &raquo;s4122485/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2&laquo; from OmniOpt2-Share</title>
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				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 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			}
			
			: :-webkit-scrollbar-button: vertical: end{
				height: 17px;
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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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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1221 n_samples 874 confidence 0.05 feature_proportion 0.999 n_clusters 50",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1325 n_samples 1162 confidence 0.005 feature_proportion 0.6173842264574706 n_clusters 15",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1221 n_samples 827 confidence 0.25 feature_proportion 0.999 n_clusters 1",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4869 n_samples 1858 confidence 0.005 feature_proportion 0.999 n_clusters 1",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1206 n_samples 651 confidence 0.001 feature_proportion 0.999 n_clusters 1",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 3041 confidence 0.05 feature_proportion 0.001 n_clusters 50",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1246 n_samples 1896 confidence 0.01 feature_proportion 0.999 n_clusters 1",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4325 n_samples 1113 confidence 0.001 feature_proportion 0.999 n_clusters 50",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 935 n_samples 340 confidence 0.25 feature_proportion 0.8708379373913727 n_clusters 25",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 789 n_samples 355 confidence 0.25 feature_proportion 0.49394866974225377 n_clusters 22",
			        789,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 965 n_samples 140 confidence 0.005 feature_proportion 0.001 n_clusters 23",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4363 n_samples 1638 confidence 0.005 feature_proportion 0.001 n_clusters 30",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 2456 confidence 0.005 feature_proportion 0.31614175063027894 n_clusters 16",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4016 confidence 0.05 feature_proportion 0.999 n_clusters 16",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3663 n_samples 1 confidence 0.001 feature_proportion 0.001 n_clusters 1",
			        3663,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1913 n_samples 3945 confidence 0.25 feature_proportion 0.999 n_clusters 1",
			        1913,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1124 n_samples 1732 confidence 0.05 feature_proportion 0.5664110527195414 n_clusters 12",
			        1124,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1253 n_samples 2271 confidence 0.25 feature_proportion 0.001 n_clusters 19",
			        1253,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 530 n_samples 5000 confidence 0.25 feature_proportion 0.5946829330262926 n_clusters 32",
			        530,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1350 n_samples 4367 confidence 0.25 feature_proportion 0.6161967915231903 n_clusters 15",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1179 n_samples 4709 confidence 0.25 feature_proportion 0.6439500211813417 n_clusters 1",
			        1179,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1085 n_samples 2089 confidence 0.25 feature_proportion 0.5528543477525104 n_clusters 21",
			        1085,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1211 n_samples 1583 confidence 0.01 feature_proportion 0.5997690717413093 n_clusters 1",
			        1211,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3967 n_samples 676 confidence 0.005 feature_proportion 0.7412528086286566 n_clusters 10",
			        3967,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 1375 confidence 0.001 feature_proportion 0.999 n_clusters 46",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4543 n_samples 1354 confidence 0.001 feature_proportion 0.999 n_clusters 40",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 983 n_samples 585 confidence 0.005 feature_proportion 0.7168167307325456 n_clusters 9",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1493 n_samples 2809 confidence 0.25 feature_proportion 0.45139207834002476 n_clusters 1",
			        1493,
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			        976,
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			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1093 n_samples 911 confidence 0.05 feature_proportion 0.5783342353747654 n_clusters 1",
			        1093,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1040 n_samples 2379 confidence 0.25 feature_proportion 0.18676550828139954 n_clusters 1",
			        1040,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1564 n_samples 2098 confidence 0.1 feature_proportion 0.7871024008182782 n_clusters 38",
			        1564,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4389 confidence 0.1 feature_proportion 0.999 n_clusters 50",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1539 n_samples 3020 confidence 0.25 feature_proportion 0.999 n_clusters 21",
			        1539,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1124 n_samples 931 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        1124,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 2489 confidence 0.05 feature_proportion 0.001 n_clusters 21",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1046 n_samples 432 confidence 0.005 feature_proportion 0.999 n_clusters 21",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1407 n_samples 3556 confidence 0.25 feature_proportion 0.999 n_clusters 14",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 2645 confidence 0.025 feature_proportion 0.999 n_clusters 32",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1019 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 1",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1270 n_samples 4901 confidence 0.25 feature_proportion 0.45350148912551475 n_clusters 10",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4237 n_samples 720 confidence 0.005 feature_proportion 0.001 n_clusters 18",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4793 confidence 0.025 feature_proportion 0.7979787634784464 n_clusters 40",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4160 n_samples 749 confidence 0.001 feature_proportion 0.9872199710181769 n_clusters 9",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4331 confidence 0.1 feature_proportion 0.9143415524779519 n_clusters 15",
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			        5000,
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			        1656,
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			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1340 n_samples 904 confidence 0.025 feature_proportion 0.6305474548196001 n_clusters 10",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 2737 confidence 0.001 feature_proportion 0.2522083027831727 n_clusters 31",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 2664 confidence 0.001 feature_proportion 0.999 n_clusters 50",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4764 confidence 0.1 feature_proportion 0.001 n_clusters 43",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4181 n_samples 608 confidence 0.025 feature_proportion 0.999 n_clusters 50",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1296 n_samples 1900 confidence 0.05 feature_proportion 0.5774292542859283 n_clusters 38",
			        1296,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4385 n_samples 1897 confidence 0.001 feature_proportion 0.9901363874606842 n_clusters 29",
			        4385,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 960 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 1",
			        960,
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			        342,
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			        1704,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1428 n_samples 1602 confidence 0.25 feature_proportion 0.999 n_clusters 11",
			        1428,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3192 n_samples 3084 confidence 0.025 feature_proportion 0.5833299632014701 n_clusters 24",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4887 confidence 0.05 feature_proportion 0.9903132438344163 n_clusters 17",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1692 n_samples 1520 confidence 0.025 feature_proportion 0.665169349761729 n_clusters 24",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1675 n_samples 3800 confidence 0.025 feature_proportion 0.3405896075796695 n_clusters 23",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1066 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 1",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4043 confidence 0.1 feature_proportion 0.999 n_clusters 37",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1647 n_samples 2674 confidence 0.25 feature_proportion 0.999 n_clusters 1",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1120 n_samples 4822 confidence 0.001 feature_proportion 0.3689036491224752 n_clusters 3",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 3672 confidence 0.025 feature_proportion 0.2447762676736633 n_clusters 17",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4550 n_samples 1068 confidence 0.001 feature_proportion 0.9005491670079306 n_clusters 40",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1143 n_samples 1 confidence 0.25 feature_proportion 0.001 n_clusters 1",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4398 n_samples 1271 confidence 0.001 feature_proportion 0.999 n_clusters 18",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 2990 confidence 0.05 feature_proportion 0.7555655099250024 n_clusters 29",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4027 n_samples 952 confidence 0.005 feature_proportion 0.999 n_clusters 9",
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			    [
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			        918.7421875,
			        18.4
			    ],
			    [
			        1746356629,
			        944.59375,
			        15.7
			    ],
			    [
			        1746356629,
			        944.59375,
			        16.7
			    ],
			    [
			        1746356630,
			        944.59375,
			        16.7
			    ],
			    [
			        1746356630,
			        944.59375,
			        17.1
			    ],
			    [
			        1746415961,
			        975.6875,
			        18.4
			    ],
			    [
			        1746415961,
			        975.6875,
			        12.8
			    ],
			    [
			        1746415961,
			        975.6875,
			        13
			    ],
			    [
			        1746415961,
			        975.6875,
			        16.3
			    ],
			    [
			        1746478536,
			        1003.18359375,
			        16.1
			    ],
			    [
			        1746478536,
			        1003.18359375,
			        13.7
			    ],
			    [
			        1746478536,
			        1003.18359375,
			        13
			    ],
			    [
			        1746478536,
			        1003.18359375,
			        17.5
			    ],
			    [
			        1746555407,
			        1005.59375,
			        15.9
			    ],
			    [
			        1746555407,
			        1005.59375,
			        14.9
			    ],
			    [
			        1746555407,
			        1005.59375,
			        15
			    ],
			    [
			        1746555407,
			        1005.59375,
			        15
			    ],
			    [
			        1746645937,
			        1133.29296875,
			        18.2
			    ],
			    [
			        1746645937,
			        1133.29296875,
			        22.2
			    ],
			    [
			        1746645937,
			        1133.29296875,
			        21
			    ],
			    [
			        1746645938,
			        1133.29296875,
			        27.3
			    ],
			    [
			        1746747202,
			        1046.046875,
			        20
			    ],
			    [
			        1746747203,
			        1046.046875,
			        16.5
			    ],
			    [
			        1746747203,
			        1046.046875,
			        16.4
			    ],
			    [
			        1746747203,
			        1046.046875,
			        17.6
			    ]
			];
			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) &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 2) {
						console.error("Not enough columns for Scatter-Plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							let xCol = numericColumns[i];
							let yCol = numericColumns[j];
			
							let xIndex = tab_results_headers_json.indexOf(xCol);
							let yIndex = tab_results_headers_json.indexOf(yCol);
			
							let data = tab_results_csv_json.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
							}));
			
							data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
							let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
							let layout = {
								title: layoutTitle,
								xaxis: {
									title: get_axis_title_data(xCol)
								},
								yaxis: {
									title: get_axis_title_data(yCol)
								},
								showlegend: false
							};
							let subDiv = document.createElement("div");
			
							let spinnerContainer = document.createElement("div");
							spinnerContainer.style.display = "flex";
							spinnerContainer.style.alignItems = "center";
							spinnerContainer.style.justifyContent = "center";
							spinnerContainer.style.width = layout.width + "px";
							spinnerContainer.style.height = layout.height + "px";
							spinnerContainer.style.position = "relative";
			
							let spinner = document.createElement("div");
							spinner.className = "spinner";
							spinner.style.width = "40px";
							spinner.style.height = "40px";
			
							let loadingText = document.createElement("span");
							loadingText.innerText = `Loading ${layoutTitle}`;
							loadingText.style.marginLeft = "10px";
			
							spinnerContainer.appendChild(spinner);
							spinnerContainer.appendChild(loadingText);
			
							plotDiv.appendChild(spinnerContainer);
			
							await new Promise(resolve => setTimeout(resolve, 50));
			
							let colors = data.map(d => {
								if (d.result === null) {
									return 'rgb(0, 0, 0)';
								} else {
									let norm = (d.result - minResult) / (maxResult - minResult);
									if (invertColor) {
										norm = 1 - norm;
									}
									return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
								}
							});
			
							let trace = {
								x: data.map(d => d.x),
								y: data.map(d => d.y),
								mode: 'markers',
								marker: {
									size: get_marker_size(),
									color: data.map(d => d.result !== null ? d.result : null),
									colorscale: invertColor ? [
										[0, 'red'],
										[1, 'green']
									] : [
										[0, 'green'],
										[1, 'red']
									],
									colorbar: {
										title: 'Result',
										tickvals: [minResult, maxResult],
										ticktext: [`${minResult}`, `${maxResult}`]
									},
									symbol: data.map(d => d.result === null ? 'x' : 'circle'),
								},
								text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
								type: 'scatter',
								showlegend: false
							};
			
							try {
								plotDiv.replaceChild(subDiv, spinnerContainer);
							} catch (err) {
								//
							}
							Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
						}
					}
				}
			
				$("#plotScatter2d").data("loaded", "true");
			}
			
			function plotScatter3d() {
				if ($("#plotScatter3d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter3d");
				if (!plotDiv) {
					console.error("Div element with id 'plotScatter3d' not found");
					return;
				}
				plotDiv.innerHTML = "";
			
				var minInput3d = document.getElementById("minValue3d");
				var maxInput3d = document.getElementById("maxValue3d");
			
				if (!minInput3d || !maxInput3d) {
					minInput3d = document.createElement("input");
					minInput3d.id = "minValue3d";
					minInput3d.type = "number";
					minInput3d.placeholder = "Min Value";
					minInput3d.step = "any";
			
					maxInput3d = document.createElement("input");
					maxInput3d.id = "maxValue3d";
					maxInput3d.type = "number";
					maxInput3d.placeholder = "Max Value";
					maxInput3d.step = "any";
			
					var inputContainer3d = document.createElement("div");
					inputContainer3d.style.marginBottom = "10px";
					inputContainer3d.appendChild(minInput3d);
					inputContainer3d.appendChild(maxInput3d);
					plotDiv.appendChild(inputContainer3d);
				}
			
				var select3d = document.getElementById("select3dScatter");
				if (result_names.length > 1 && !select3d) {
					if (!select3d) {
						select3d = document.createElement("select");
						select3d.id = "select3dScatter";
						select3d.style.marginBottom = "10px";
						select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
			
						select3d.addEventListener("change", updatePlots3d);
			
						plotDiv.appendChild(select3d);
					}
				}
			
				minInput3d.addEventListener("input", updatePlots3d);
				maxInput3d.addEventListener("input", updatePlots3d);
			
				updatePlots3d();
			
				async function updatePlots3d() {
					var selectedResult = select3d ? select3d.value : result_names[0];
					var minValue3d = parseFloat(minInput3d.value);
					var maxValue3d = parseFloat(maxInput3d.value);
			
					if (isNaN(minValue3d)) minValue3d = -Infinity;
					if (isNaN(maxValue3d)) maxValue3d = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
					if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 3) {
						console.error("Not enough columns for 3D scatter plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							for (let k = j + 1; k < numericColumns.length; k++) {
								let xCol = numericColumns[i];
								let yCol = numericColumns[j];
								let zCol = numericColumns[k];
			
								let xIndex = tab_results_headers_json.indexOf(xCol);
								let yIndex = tab_results_headers_json.indexOf(yCol);
								let zIndex = tab_results_headers_json.indexOf(zCol);
			
								let data = tab_results_csv_json.map(row => ({
									x: parseFloat(row[xIndex]),
									y: parseFloat(row[yIndex]),
									z: parseFloat(row[zIndex]),
									result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
								}));
			
								data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
								let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
								let layout = {
									title: layoutTitle,
									scene: {
										xaxis: {
											title: get_axis_title_data(xCol)
										},
										yaxis: {
											title: get_axis_title_data(yCol)
										},
										zaxis: {
											title: get_axis_title_data(zCol)
										}
									},
									showlegend: false
								};
			
								let spinnerContainer = document.createElement("div");
								spinnerContainer.style.display = "flex";
								spinnerContainer.style.alignItems = "center";
								spinnerContainer.style.justifyContent = "center";
								spinnerContainer.style.width = layout.width + "px";
								spinnerContainer.style.height = layout.height + "px";
								spinnerContainer.style.position = "relative";
			
								let spinner = document.createElement("div");
								spinner.className = "spinner";
								spinner.style.width = "40px";
								spinner.style.height = "40px";
			
								let loadingText = document.createElement("span");
								loadingText.innerText = `Loading ${layoutTitle}`;
								loadingText.style.marginLeft = "10px";
			
								spinnerContainer.appendChild(spinner);
								spinnerContainer.appendChild(loadingText);
			
								plotDiv.appendChild(spinnerContainer);
			
								await new Promise(resolve => setTimeout(resolve, 50));
			
								let colors = data.map(d => {
									if (d.result === null) {
										return 'rgb(0, 0, 0)';
									} else {
										let norm = (d.result - minResult) / (maxResult - minResult);
										if (invertColor) {
											norm = 1 - norm;
										}
										return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
									}
								});
			
								let trace = {
									x: data.map(d => d.x),
									y: data.map(d => d.y),
									z: data.map(d => d.z),
									mode: 'markers',
									marker: {
										size: get_marker_size(),
										color: data.map(d => d.result !== null ? d.result : null),
										colorscale: invertColor ? [
											[0, 'red'],
											[1, 'green']
										] : [
											[0, 'green'],
											[1, 'red']
										],
										colorbar: {
											title: 'Result',
											tickvals: [minResult, maxResult],
											ticktext: [`${minResult}`, `${maxResult}`]
										},
									},
									text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
									type: 'scatter3d',
									showlegend: false
								};
			
								let subDiv = document.createElement("div");
								try {
									plotDiv.replaceChild(subDiv, spinnerContainer);
								} catch (err) {
									//
								}
								Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
							}
						}
					}
				}
			
				$("#plotScatter3d").data("loaded", "true");
			}
			
			async function plot_worker_cpu_ram() {
				if($("#worker_cpu_ram_pre").data("loaded") == "true") {
					return;
				}
			
				const logData = $("#worker_cpu_ram_pre").text();
				const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
			
				const hostData = {};
			
				logData.split("\n").forEach(line => {
					line = line.trim();
					const match = line.match(regex);
					if (match) {
						const timestamp = new Date(parseInt(match[1]) * 1000);
						const hostname = match[2];
						const cpu = parseFloat(match[3]);
						const ram = parseFloat(match[4]);
			
						if (!hostData[hostname]) {
							hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
						}
			
						hostData[hostname].timestamps.push(timestamp);
						hostData[hostname].cpuUsage.push(cpu);
						hostData[hostname].ramUsage.push(ram);
					}
				});
			
				if (!Object.keys(hostData).length) {
					console.log("No valid data found");
					return;
				}
			
				const container = document.getElementById("cpuRamWorkerChartContainer");
				container.innerHTML = "";
			
				var i = 1;
			
				Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
					const chartId = `workerChart_${index}`;
					const chartDiv = document.createElement("div");
					chartDiv.id = chartId;
					chartDiv.style.marginBottom = "40px";
					container.appendChild(chartDiv);
			
					const cpuTrace = {
						x: timestamps,
						y: cpuUsage,
						mode: "lines+markers",
						name: "CPU Usage (%)",
						yaxis: "y1",
						line: {
							color: "red"
						}
					};
			
					const ramTrace = {
						x: timestamps,
						y: ramUsage,
						mode: "lines+markers",
						name: "RAM Usage (MB)",
						yaxis: "y2",
						line: {
							color: "blue"
						}
					};
			
					const layout = {
						title: `Worker CPU and RAM Usage - ${hostname}`,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date")
						},
						yaxis: {
							title: get_axis_title_data("CPU Usage (%)"),
							side: "left",
							color: "red"
						},
						yaxis2: {
							title: get_axis_title_data("RAM Usage (MB)"),
							side: "right",
							overlaying: "y",
							color: "blue"
						},
						showlegend: true
					};
			
					Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
					i++;
				});
			
				$("#plot_worker_cpu_ram_button").remove();
				$("#worker_cpu_ram_pre").data("loaded", "true");
			}
			
			function load_log_file(log_nr, filename) {
				var pre_id = `single_run_${log_nr}_pre`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							if (data.data) {
								$("#" + pre_id).html(data.data);
								$("#" + pre_id).data("loaded", true);
							} else {
								log(`No 'data' key found in response.`);
							}
			
							$("#spinner_log_" + log_nr).remove();
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#spinner_log_" + log_nr).remove();
						});
				}
			}
			
			function load_debug_log () {
				var pre_id = `here_debuglogs_go`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							$("#debug_log_spinner").remove();
			
							if (data.data) {
								try {
									$("#" + pre_id).html(data.data);
								} catch (err) {
									$("#" + pre_id).text(`Error loading data: ${err}`);
								}
			
								$("#" + pre_id).data("loaded", true);
			
								if (typeof apply_theme_based_on_system_preferences === 'function') {
									apply_theme_based_on_system_preferences();
								}
							} else {
								log(`No 'data' key found in response.`);
							}
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#debug_log_spinner").remove();
						});
				}
			}
			
			function plotBoxplot() {
				if ($("#plotBoxplot").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough numeric columns for Boxplot");
					return;
				}
			
				var resultIndex = tab_results_headers_json.findIndex(function(header) {
					return result_names.includes(header.toLowerCase());
				});
				var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
				var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
				var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
				var plotDiv = document.getElementById("plotBoxplot");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'box',
						name: col,
						boxmean: 'sd',
						marker: {
							color: 'rgb(0, 255, 0)'
						},
					};
				});
			
				let layout = {
					title: 'Boxplot of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Value")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotBoxplot").data("loaded", "true");
			}
			
			function plotHeatmap() {
				if ($("#plotHeatmap").data("loaded") === "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col => {
					if (special_col_names.includes(col) || result_names.includes(col)) {
						return false;
					}
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.every(row => {
						let value = parseFloat(row[index]);
						return !isNaN(value) && isFinite(value);
					});
				});
			
				if (numericColumns.length < 2) {
					console.error("Not enough valid numeric columns for Heatmap");
					return;
				}
			
				var columnData = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.map(row => parseFloat(row[index]));
				});
			
				var dataMatrix = numericColumns.map((_, i) =>
					numericColumns.map((_, j) => {
						let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
						return values.reduce((a, b) => a + b, 0) / values.length;
					})
				);
			
				var trace = {
					z: dataMatrix,
					x: numericColumns,
					y: numericColumns,
					colorscale: 'Viridis',
					type: 'heatmap'
				};
			
				var layout = {
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				var plotDiv = document.getElementById("plotHeatmap");
				plotDiv.innerHTML = "";
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotHeatmap").data("loaded", "true");
			}
			
			function plotHistogram() {
				if ($("#plotHistogram").data("loaded") == "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Histogram");
					return;
				}
			
				var plotDiv = document.getElementById("plotHistogram");
				plotDiv.innerHTML = "";
			
				const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
			
				let traces = numericColumns.map((col, index) => {
					let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
			
					return {
						x: data,
						type: 'histogram',
						name: col,
						opacity: 0.7,
						marker: {
							color: colorPalette[index % colorPalette.length]
						},
						autobinx: true
					};
				});
			
				let layout = {
					title: 'Histogram of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Value")
					},
					yaxis: {
						title: get_axis_title_data("Frequency")
					},
					showlegend: true,
					barmode: 'overlay'
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotHistogram").data("loaded", "true");
			}
			
			function plotViolin() {
				if ($("#plotViolin").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Violin Plot");
					return;
				}
			
				var plotDiv = document.getElementById("plotViolin");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'violin',
						name: col,
						box: {
							visible: true
						},
						line: {
							color: 'rgb(0, 255, 0)'
						},
						marker: {
							color: 'rgb(0, 255, 0)'
						},
						meanline: {
							visible: true
						},
					};
				});
			
				let layout = {
					title: 'Violin Plot of Numerical Columns',
					yaxis: {
						title: get_axis_title_data("Value")
					},
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotViolin").data("loaded", "true");
			}
			
			function plotExitCodesPieChart() {
				if ($("#plotExitCodesPieChart").data("loaded") == "true") {
					return;
				}
			
				var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
			
				var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
					counts[exitCode] = (counts[exitCode] || 0) + 1;
					return counts;
				}, {});
			
				var labels = Object.keys(exitCodeCounts);
				var values = Object.values(exitCodeCounts);
			
				var plotDiv = document.getElementById("plotExitCodesPieChart");
				plotDiv.innerHTML = "";
			
				var trace = {
					labels: labels,
					values: values,
					type: 'pie',
					hoverinfo: 'label+percent',
					textinfo: 'label+value',
					marker: {
						colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
					}
				};
			
				var layout = {
					title: 'Exit Code Distribution',
					showlegend: true
				};
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotExitCodesPieChart").data("loaded", "true");
			}
			
			function plotResultEvolution() {
				if ($("#plotResultEvolution").data("loaded") == "true") {
					return;
				}
			
				result_names.forEach(resultName => {
					var relevantColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
					);
			
					var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
					var resultIndex = tab_results_headers_json.indexOf(resultName);
			
					let data = tab_results_csv_json.map(row => ({
						x: row[xColumnIndex],
						y: parseFloat(row[resultIndex])
					}));
			
					data.sort((a, b) => a.x - b.x);
			
					let xData = data.map(item => item.x);
					let yData = data.map(item => item.y);
			
					let trace = {
						x: xData,
						y: yData,
						mode: 'lines+markers',
						name: resultName,
						line: {
							shape: 'linear'
						},
						marker: {
							size: get_marker_size()
						}
					};
			
					let layout = {
						title: `Evolution of ${resultName} over time`,
						xaxis: {
							title: get_axis_title_data("Trial-Index")
						},
						yaxis: {
							title: get_axis_title_data(resultName)
						},
						showlegend: true
					};
			
					let subDiv = document.createElement("div");
					document.getElementById("plotResultEvolution").appendChild(subDiv);
			
					Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
				});
			
				$("#plotResultEvolution").data("loaded", "true");
			}
			
			function plotResultPairs() {
				if ($("#plotResultPairs").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotResultPairs");
				plotDiv.innerHTML = "";
			
				for (let i = 0; i < result_names.length; i++) {
					for (let j = i + 1; j < result_names.length; j++) {
						let xName = result_names[i];
						let yName = result_names[j];
			
						let xIndex = tab_results_headers_json.indexOf(xName);
						let yIndex = tab_results_headers_json.indexOf(yName);
			
						let data = tab_results_csv_json
							.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
							.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								status: row[tab_results_headers_json.indexOf("trial_status")]
							}));
			
						let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
			
						let trace = {
							x: data.map(d => d.x),
							y: data.map(d => d.y),
							mode: 'markers',
							marker: {
								size: get_marker_size(),
								color: colors
							},
							text: data.map(d => `Status: ${d.status}`),
							type: 'scatter',
							showlegend: false
						};
			
						let layout = {
							xaxis: {
								title: get_axis_title_data(xName)
							},
							yaxis: {
								title: get_axis_title_data(yName)
							},
							showlegend: false
						};
			
						let subDiv = document.createElement("div");
						plotDiv.appendChild(subDiv);
			
						Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
					}
				}
			
				$("#plotResultPairs").data("loaded", "true");
			}
			
			function add_up_down_arrows_for_scrolling () {
				const upArrow = document.createElement('div');
				const downArrow = document.createElement('div');
			
				const style = document.createElement('style');
				style.innerHTML = `
					.scroll-arrow {
						position: fixed;
						right: 10px;
						z-index: 100;
						cursor: pointer;
						font-size: 25px;
						display: none;
						background-color: green;
						color: white;
						padding: 5px;
						outline: 2px solid white;
						box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
						transition: background-color 0.3s, transform 0.3s;
					}
					.scroll-arrow:hover {
						background-color: darkgreen;
						transform: scale(1.1);
					}
					#up-arrow {
						top: 10px;
					}
					#down-arrow {
						bottom: 10px;
					}
				`;
				document.head.appendChild(style);
			
				upArrow.id = "up-arrow";
				upArrow.classList.add("scroll-arrow");
				upArrow.classList.add("invert_in_dark_mode");
				upArrow.innerHTML = "&#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();
			}
			
			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();;
				plotResultEvolution();;
				plotExitCodesPieChart();

				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> Max. nr. evaluations</td><td>50372 </td></tr><tr><td> Max. nr. evaluations (from arguments)</td><td>50000 </td></tr><tr><td> Number random steps</td><td>20    </td></tr><tr><td> Nr. of workers (parameter)</td><td>20    </td></tr><tr><td> Main process memory (GB)</td><td>8     </td></tr><tr><td> Worker memory (GB)</td><td>32    </td></tr><tr><td> Nr. imported jobs</td><td>372   </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>int</td><td>1</td><td>5000</td><td></td><td>int</td><td>No         </td></tr><tr><td> n_samples</td><td>int</td><td>1</td><td>5000</td><td></td><td>int</td><td>No         </td></tr><tr><td> confidence</td><td>choice</td><td></td><td></td><td>0.25, 0.1, 0.05, 0.025, 0.01, 0.005, 0.001</td><td></td><td></td></tr><tr><td> feature_proportion</td><td>float</td><td>0.001</td><td>0.999</td><td></td><td>float</td><td>No         </td></tr><tr><td> n_clusters</td><td>int</td><td>1</td><td>50</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>22</td>
			<td>550</td>
			<td>2</td>
			<td>574</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>
			<br>
			<h2>Git-Version</h2>
			<tt>Commit: 2223ae6553abdd3e288f4b391080b763a7a48477
			</tt>
			<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,feature_proportion,n_clusters,confidence
0,0_0,COMPLETED,Sobol,SOBOL,0.700000000000000066613381477509,18.000000000000000000000000000000,1961,2441,0.799423568129539519055981600104,11,0.25
1,1_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,14.000000000000000000000000000000,4268,2635,0.099351818336173888801532427806,28,0.01
2,2_0,COMPLETED,Sobol,SOBOL,0.739999999999999991118215802999,218.000000000000000000000000000000,3464,54,0.445492655232548717680884919901,40,0.25
3,3_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,9.000000000000000000000000000000,303,4857,0.655701311219483606862468150211,23,0.1
4,4_0,COMPLETED,Sobol,SOBOL,0.690000000000000057731597280508,13.000000000000000000000000000000,714,1150,0.168379319963976731289179156192,47,0.025
5,5_0,COMPLETED,Sobol,SOBOL,0.700000000000000066613381477509,10.000000000000000000000000000000,3021,3765,0.995520461725071026393152351375,17,0.005
6,6_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,22.000000000000000000000000000000,4718,1340,0.578542165432125332635848735663,5,0.001
7,7_0,COMPLETED,Sobol,SOBOL,0.700000000000000066613381477509,11.000000000000000000000000000000,1556,3721,0.257588700890541089094654125802,34,0.01
8,8_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,36.000000000000000000000000000000,1638,358,0.727335241559892931029196461168,35,0.025
9,9_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,10.000000000000000000000000000000,4648,4538,0.436233947962522516927919014051,3,0.05
10,10_0,COMPLETED,Sobol,SOBOL,0.700000000000000066613381477509,11.000000000000000000000000000000,2635,2111,0.018946686176583172472875915560,15,0.001
11,11_0,COMPLETED,Sobol,SOBOL,0.640000000000000013322676295502,14.000000000000000000000000000000,1114,2931,0.817453918712213623010143237479,49,0.01
12,12_0,COMPLETED,Sobol,SOBOL,0.660000000000000031086244689504,11.000000000000000000000000000000,372,1649,0.361384517535567273416319267199,22,0.001
13,13_0,COMPLETED,Sobol,SOBOL,0.680000000000000048849813083507,11.000000000000000000000000000000,3383,3408,0.537121125716715996922800968605,42,0.01
14,14_0,COMPLETED,Sobol,SOBOL,0.719999999999999973354647408996,22.000000000000000000000000000000,3869,824,0.884902215870097319516673906037,30,0.1
15,15_0,COMPLETED,Sobol,SOBOL,0.719999999999999973354647408996,9.000000000000000000000000000000,2348,4067,0.216622342748567453529773274568,9,0.025
16,16_0,COMPLETED,Sobol,SOBOL,0.690000000000000057731597280508,11.000000000000000000000000000000,2219,650,0.947823183421045545316019342863,8,0.01
17,17_0,COMPLETED,Sobol,SOBOL,0.660000000000000031086244689504,30.000000000000000000000000000000,4057,4281,0.153701626151800163633254214801,30,0.005
18,18_0,COMPLETED,Sobol,SOBOL,0.680000000000000048849813083507,13.000000000000000000000000000000,3215,1865,0.297611027030274255533726091016,44,0.025
19,19_0,COMPLETED,Sobol,SOBOL,0.700000000000000066613381477509,9.000000000000000000000000000000,522,3231,0.600894870894029708985328852577,20,0.05
20,20_0,COMPLETED,Sobol,SOBOL,0.690000000000000057731597280508,12.000000000000000000000000000000,962,2014,0.082842002853751178781394060024,49,0.05
21,21_0,COMPLETED,Sobol,SOBOL,0.670000000000000039968028886506,10.000000000000000000000000000000,2801,3067,0.753558347363024960685606856714,14,0.001
22,22_0,COMPLETED,Sobol,SOBOL,0.739999999999999991118215802999,32.000000000000000000000000000000,4459,496,0.664536100180819655491859521135,1,0.01
23,23_0,COMPLETED,Sobol,SOBOL,0.700000000000000066613381477509,11.000000000000000000000000000000,1766,4439,0.499032838387414801051278345767,38,0.25
24,24_0,COMPLETED,Sobol,SOBOL,0.700000000000000066613381477509,13.000000000000000000000000000000,1369,1554,0.516591078164055916843722116027,33,0.1
25,25_0,COMPLETED,Sobol,SOBOL,0.690000000000000057731597280508,15.000000000000000000000000000000,4848,3547,0.319539596689864979772721653717,5,0.25
26,26_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,15.000000000000000000000000000000,2873,973,0.229233770921826368605067614226,19,0.01
27,27_0,COMPLETED,Sobol,SOBOL,0.660000000000000031086244689504,9.000000000000000000000000000000,883,3982,0.934665815580636216708398933406,44,0.005
28,28_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,14.000000000000000000000000000000,133,190,0.384516378102824074591836733816,24,0.01
29,29_0,COMPLETED,Sobol,SOBOL,0.700000000000000066613381477509,10.000000000000000000000000000000,3612,4760,0.716677783535793477831532527489,39,0.005
30,30_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,88.000000000000000000000000000000,4069,162,0.538223286297583758397422570852,23,0.001
31,31_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2566,1,0.503693180046458355292315900442,27,0.01
32,32_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,37.000000000000000000000000000000,4659,425,0.561416585041630611385699012317,13,0.025
33,33_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,29.000000000000000000000000000000,4877,582,0.552066001848336762591884507856,9,0.01
34,34_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,31.000000000000000000000000000000,4532,2982,0.001000000000000000020816681712,1,0.01
35,35_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2098,1,0.505224733627193667295784962334,32,0.025
36,36_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,10.000000000000000000000000000000,4459,4695,0.998999999999999999111821580300,1,0.025
37,37_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,35.000000000000000000000000000000,3614,359,0.542030654705255132697061526414,6,0.1
38,38_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,30.000000000000000000000000000000,4509,3014,0.001000000000000000020816681712,1,0.025
39,39_0,FAILED,BoTorch,BOTORCH_MODULAR,,,392,1,0.543658526093445670923642865091,50,0.1
40,40_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,71.000000000000000000000000000000,1959,196,0.548820551417048219100536243786,50,0.005
41,41_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,27.000000000000000000000000000000,4942,677,0.607626705324803451091497663583,1,0.1
42,42_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,10.000000000000000000000000000000,3215,4645,0.998999999999999999111821580300,1,0.05
43,43_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2992,10,0.523892318223105446506338012114,12,0.025
44,44_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,37.000000000000000000000000000000,4625,424,0.613014316787836577304915408604,47,0.01
45,45_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,321.000000000000000000000000000000,1046,54,0.569711657394216586247637224005,50,0.01
46,46_0,FAILED,BoTorch,BOTORCH_MODULAR,,,195,1,0.495680528866587977177005086560,47,0.025
47,47_0,FAILED,BoTorch,BOTORCH_MODULAR,,,410,1,0.453734042657828906541794822260,6,0.1
48,48_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,37.000000000000000000000000000000,4135,399,0.539547800135057209480748952046,17,0.005
49,49_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,21.000000000000000000000000000000,4994,1097,0.590676907180823218546095176862,1,0.01
50,50_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2953,1,0.551781342289027643133181300072,50,0.001
51,51_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,40.000000000000000000000000000000,4669,355,0.538150130150292671515899201040,16,0.001
52,52_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,11.000000000000000000000000000000,5000,4686,0.998999999999999999111821580300,1,0.001
53,53_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,10.000000000000000000000000000000,3851,4249,0.998999999999999999111821580300,1,0.1
54,54_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,9.000000000000000000000000000000,2417,4562,0.998999999999999999111821580300,1,0.1
55,55_0,FAILED,BoTorch,BOTORCH_MODULAR,,,9,1,0.422521717767514293218056309343,1,0.025
56,56_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.640000000000000013322676295502,10.000000000000000000000000000000,1,4465,0.998999999999999999111821580300,1,0.05
57,57_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,14.000000000000000000000000000000,117,129,0.559133380101802357486917571805,1,0.05
58,58_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,40.000000000000000000000000000000,3917,335,0.580565125057908471006840045447,1,0.001
59,59_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,74.000000000000000000000000000000,3212,109,0.545409544366371790147240972146,27,0.05
60,60_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,16.000000000000000000000000000000,4940,1328,0.681713894770362149877485080651,44,0.001
61,61_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,10.000000000000000000000000000000,1,290,0.555332843073492288255010862486,12,0.25
62,62_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,3209,1458,0.742919309298469499047712361062,30,0.05
63,63_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,13.000000000000000000000000000000,3831,2485,0.169603797764097036226971226824,50,0.05
64,64_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,17.000000000000000000000000000000,4240,1509,0.725846780598479401902523022727,38,0.05
65,65_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,21.000000000000000000000000000000,556,269,0.580104222396906843428610045521,14,0.05
66,66_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,11.000000000000000000000000000000,39,318,0.589025469896375386547049401997,12,0.005
67,67_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,31.000000000000000000000000000000,5000,1103,0.001000000000000000020816681712,45,0.01
68,68_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,5000,1131,0.679060320131461669923567114893,44,0.1
69,69_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,4955,1124,0.690686271102797899601455355878,47,0.05
70,70_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,18.000000000000000000000000000000,162,118,0.450940656660552774415151588983,12,0.25
71,71_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,18.000000000000000000000000000000,5000,1439,0.753513834909802060479933061288,46,0.001
72,72_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,13.000000000000000000000000000000,4768,2428,0.135366994336186097225294133750,50,0.01
73,73_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,13.000000000000000000000000000000,4822,2562,0.180511600122179260985433302267,50,0.05
74,74_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,9.000000000000000000000000000000,2037,2476,0.164102345898254231570945194107,29,0.05
75,75_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,13.000000000000000000000000000000,3863,2466,0.244379382268582773241050176694,39,0.05
76,76_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,17.000000000000000000000000000000,4762,1381,0.728573315773403562545240674808,37,0.05
77,77_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,3929,1552,0.850815667766839700547620850557,44,0.1
78,78_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,17.000000000000000000000000000000,5000,1356,0.847187838334022225161845653929,50,0.001
79,79_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,100,316,0.594784303711658912661164322344,13,0.025
80,80_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,446,493,0.648689149772894935175315822562,16,0.05
81,81_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,16.000000000000000000000000000000,1735,1414,0.724667326176439252627403675433,24,0.05
82,82_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,19.000000000000000000000000000000,4614,1265,0.599654099729392986439791002340,38,0.05
83,83_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,3679,2082,0.998999999999999999111821580300,50,0.005
84,84_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,18.000000000000000000000000000000,5000,1404,0.680486859622911999245786773827,50,0.05
85,85_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,11.000000000000000000000000000000,4596,5000,0.879677283554964217771043877292,26,0.1
86,86_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,11.000000000000000000000000000000,630,2532,0.236742764215118084436539902526,36,0.05
87,87_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,13.000000000000000000000000000000,4947,2551,0.125313060426798156266059436348,50,0.005
88,88_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,3881,1493,0.697170571070443556571660792542,39,0.005
89,89_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,345,472,0.646606759029021649354262990528,14,0.001
90,90_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,3880,1101,0.599569733653343761581311355258,1,0.1
91,91_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,23.000000000000000000000000000000,3897,1025,0.599649982784605350971673942695,1,0.05
92,92_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,2186,361,0.528557177471257633172285750334,1,0.25
93,93_0,FAILED,BoTorch,BOTORCH_MODULAR,,,663,1,0.432759830255158295386763711576,11,0.05
94,94_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,20.000000000000000000000000000000,4682,1350,0.359614845395848370390723403034,19,0.005
95,95_0,FAILED,BoTorch,BOTORCH_MODULAR,,,745,1,0.434551233303763773108130408218,11,0.05
96,96_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,1,157,0.420496044724178563711802780745,30,0.05
97,97_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,1071,805,0.626071908611396721333619552752,1,0.1
98,98_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,45.000000000000000000000000000000,5000,532,0.481253720749925062438734357784,49,0.005
99,99_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,134.000000000000000000000000000000,611,44,0.432670785422708203959984984976,10,0.05
100,100_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,17.000000000000000000000000000000,4976,1349,0.644820173629898962808226769994,18,0.05
101,101_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,48.000000000000000000000000000000,1142,165,0.538413236463440414425463131920,1,0.25
102,102_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,29.000000000000000000000000000000,2594,179,0.572902582285771733161539032153,1,0.05
103,103_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,63.000000000000000000000000000000,1792,334,0.616966329498460308577989508194,38,0.05
104,104_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,231,323,0.597774470838517202331274802418,1,0.05
105,105_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,32.000000000000000000000000000000,4491,525,0.602742586133737545672772739636,35,0.25
106,106_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,28.000000000000000000000000000000,4538,643,0.594715888761703226350618933793,50,0.001
107,107_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,14.000000000000000000000000000000,4892,2600,0.998999999999999999111821580300,1,0.005
108,108_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,10.000000000000000000000000000000,1,235,0.405428310552846005165861242858,1,0.1
109,109_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,40.000000000000000000000000000000,1328,288,0.564963059159581004742278764752,1,0.01
110,110_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,34.000000000000000000000000000000,4679,546,0.663789685343377566795197708416,1,0.25
111,111_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,21.000000000000000000000000000000,4636,1268,0.529588915974931739860664947628,47,0.25
112,112_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,2464,443,0.579053285940142714594003336970,1,0.25
113,113_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,13.000000000000000000000000000000,2746,1802,0.998999999999999999111821580300,50,0.005
114,114_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,18.000000000000000000000000000000,4944,2968,0.090768076340531453349846913170,31,0.01
115,115_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,1,163,0.503264557211447471907206363539,9,0.05
116,116_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,9.000000000000000000000000000000,99,5000,0.308203446313885853768965716881,1,0.1
117,117_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,17.000000000000000000000000000000,5000,1571,0.594848833822561928741379233543,7,0.25
118,118_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,20.000000000000000000000000000000,2606,643,0.539877646336125782688952767785,18,0.05
119,119_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,5000,724,0.569803357698843160328294743522,50,0.05
120,120_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,23.000000000000000000000000000000,5000,913,0.487569951519168265363646241894,1,0.25
121,121_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,34.000000000000000000000000000000,2670,195,0.565826982129539635124615415407,20,0.25
122,122_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,16.000000000000000000000000000000,5000,1752,0.579868431100353687313031514350,50,0.005
123,123_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,688,911,0.297530679419881260994173999279,50,0.005
124,124_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,23.000000000000000000000000000000,5000,972,0.601653735281446500060553717049,50,0.05
125,125_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,38.000000000000000000000000000000,875,210,0.519312319928798449275575421780,17,0.25
126,126_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,28.000000000000000000000000000000,1920,694,0.222937592931043732091822562325,23,0.005
127,127_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,20.000000000000000000000000000000,5000,1256,0.398248783096766867384275201402,50,0.005
128,128_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,30.000000000000000000000000000000,4666,843,0.265758613605496074594469746444,50,0.005
129,129_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,25.000000000000000000000000000000,3424,590,0.282384555109895019064936150244,34,0.01
130,130_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,1291,474,0.572505752279888091571535824187,1,0.005
131,131_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,1199,450,0.672337961336425649072623400571,1,0.1
132,132_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,9.000000000000000000000000000000,1,4134,0.001000000000000000020816681712,5,0.025
133,133_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,12.000000000000000000000000000000,2341,851,0.306721429488016006370543209414,1,0.005
134,134_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,10.000000000000000000000000000000,2934,4948,0.460029175741152263068300953819,1,0.025
135,135_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,3071,665,0.564338459079970711229634616757,50,0.25
136,136_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,44.000000000000000000000000000000,855,197,0.667148940816745472659476945410,15,0.05
137,137_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,17.000000000000000000000000000000,5000,1926,0.632917984415367840256294584833,50,0.005
138,138_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,31.000000000000000000000000000000,5000,786,0.497007294806093069539087991870,1,0.01
139,139_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,17.000000000000000000000000000000,5000,2779,0.629940272572822923535795780481,39,0.1
140,140_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,5000,1733,0.404247287668403010307827116776,16,0.005
141,141_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,3133,0.598109698697520286714279791340,50,0.1
142,142_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,5000,1970,0.632990170344266300794799917639,1,0.25
143,143_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,12.000000000000000000000000000000,4297,4593,0.453164293307243837283238008240,50,0.1
144,144_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,21.000000000000000000000000000000,399,212,0.001000000000000000020816681712,9,0.005
145,145_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,10.000000000000000000000000000000,1,4364,0.299315812365486266788394686955,1,0.05
146,146_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,15.000000000000000000000000000000,3254,2209,0.998999999999999999111821580300,48,0.005
147,147_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,29.000000000000000000000000000000,2862,408,0.001000000000000000020816681712,8,0.005
148,148_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,16.000000000000000000000000000000,5000,2266,0.703220446302847079778075567447,1,0.05
149,149_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,17.000000000000000000000000000000,5000,2026,0.001000000000000000020816681712,28,0.01
150,150_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,88.000000000000000000000000000000,4996,182,0.315816181749681013091191061903,1,0.005
151,151_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,56.000000000000000000000000000000,715,726,0.001000000000000000020816681712,50,0.01
152,152_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,14.000000000000000000000000000000,1,214,0.001000000000000000020816681712,10,0.01
153,153_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,14.000000000000000000000000000000,5000,3352,0.659771562176048043113496532897,1,0.1
154,154_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,22.000000000000000000000000000000,4313,1678,0.001000000000000000020816681712,50,0.01
155,155_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,37.000000000000000000000000000000,5000,570,0.230428326117830872821201637635,50,0.005
156,156_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,26.000000000000000000000000000000,2997,992,0.001000000000000000020816681712,50,0.005
157,157_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,38.000000000000000000000000000000,494,1818,0.001000000000000000020816681712,50,0.01
158,158_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,9.000000000000000000000000000000,246,3909,0.413229478264495686534729657069,7,0.25
159,159_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,13.000000000000000000000000000000,1484,3845,0.487552094430944160663443653903,50,0.025
160,160_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,4709,2159,0.001000000000000000020816681712,27,0.01
161,161_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,5000,3551,0.001000000000000000020816681712,50,0.1
162,162_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,3187,0.001000000000000000020816681712,2,0.01
163,163_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,10.000000000000000000000000000000,1,4942,0.001000000000000000020816681712,50,0.1
164,164_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,12.000000000000000000000000000000,5000,4912,0.001000000000000000020816681712,50,0.25
165,165_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,13.000000000000000000000000000000,1981,4659,0.001000000000000000020816681712,33,0.01
166,166_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,20.000000000000000000000000000000,5000,1451,0.008013663507275343989011417989,1,0.01
167,167_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,25.000000000000000000000000000000,5000,912,0.001000000000000000020816681712,18,0.05
168,168_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,21.000000000000000000000000000000,5000,1197,0.001000000000000000020816681712,1,0.05
169,169_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,13.000000000000000000000000000000,5000,4399,0.001000000000000000020816681712,1,0.1
170,170_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,88.000000000000000000000000000000,4995,233,0.575634483501170191033224909916,50,0.05
171,171_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,12.000000000000000000000000000000,5000,3998,0.001000000000000000020816681712,15,0.01
172,172_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,16.000000000000000000000000000000,5000,2297,0.001000000000000000020816681712,50,0.25
173,173_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,99.000000000000000000000000000000,3950,163,0.391804240057184172130178012594,8,0.1
174,174_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,5000,3265,0.001000000000000000020816681712,50,0.1
175,175_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,18.000000000000000000000000000000,5000,2508,0.001000000000000000020816681712,1,0.01
176,176_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,39.000000000000000000000000000000,5000,1314,0.001000000000000000020816681712,50,0.01
177,177_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,10.000000000000000000000000000000,2139,2759,0.001000000000000000020816681712,50,0.01
178,178_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,10.000000000000000000000000000000,1,4747,0.001000000000000000020816681712,50,0.1
179,179_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,5000,2679,0.001000000000000000020816681712,50,0.001
180,180_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,28.000000000000000000000000000000,5000,2685,0.001000000000000000020816681712,1,0.1
181,181_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,18.000000000000000000000000000000,5000,1645,0.001000000000000000020816681712,50,0.05
182,182_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,10.000000000000000000000000000000,1,4620,0.001000000000000000020816681712,50,0.001
183,183_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,12.000000000000000000000000000000,5000,3965,0.001000000000000000020816681712,50,0.25
184,184_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,32.000000000000000000000000000000,4245,615,0.001000000000000000020816681712,50,0.1
185,185_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,10.000000000000000000000000000000,1,188,0.705796968494713294894893351739,1,0.05
186,186_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,10.000000000000000000000000000000,1,3652,0.001000000000000000020816681712,50,0.1
187,187_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,74.000000000000000000000000000000,3451,161,0.330598568625793620689989893435,8,0.25
188,188_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,39.000000000000000000000000000000,2686,158,0.001000000000000000020816681712,10,0.05
189,189_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,2261,171,0.001000000000000000020816681712,9,0.25
190,190_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,40.000000000000000000000000000000,2627,159,0.001000000000000000020816681712,4,0.1
191,191_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,83.000000000000000000000000000000,3710,151,0.001000000000000000020816681712,4,0.005
192,192_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,24.000000000000000000000000000000,2307,162,0.127152691774409964953207463623,1,0.05
193,193_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,82.000000000000000000000000000000,4219,197,0.001000000000000000020816681712,50,0.05
194,194_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,16.000000000000000000000000000000,2128,173,0.001000000000000000020816681712,1,0.05
195,195_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,51.000000000000000000000000000000,1091,179,0.001000000000000000020816681712,1,0.05
196,196_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,21.000000000000000000000000000000,2331,170,0.001000000000000000020816681712,1,0.001
197,197_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,25.000000000000000000000000000000,3411,562,0.001000000000000000020816681712,50,0.05
198,198_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,41.000000000000000000000000000000,2730,161,0.001000000000000000020816681712,1,0.25
199,199_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,60.000000000000000000000000000000,4482,255,0.422604854457793055466652276664,1,0.01
200,200_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,39.000000000000000000000000000000,2646,156,0.001000000000000000020816681712,1,0.001
201,201_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,30.000000000000000000000000000000,2483,159,0.001000000000000000020816681712,1,0.025
202,202_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,2282,166,0.001000000000000000020816681712,1,0.005
203,203_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,39.000000000000000000000000000000,2835,160,0.001000000000000000020816681712,1,0.05
204,204_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,20.000000000000000000000000000000,2147,166,0.001000000000000000020816681712,1,0.25
205,205_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,31.000000000000000000000000000000,1246,406,0.001000000000000000020816681712,24,0.25
206,206_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,29.000000000000000000000000000000,2404,160,0.001000000000000000020816681712,1,0.05
207,207_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,140.000000000000000000000000000000,5000,1472,0.001000000000000000020816681712,50,0.001
208,208_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,14.000000000000000000000000000000,178,184,0.001000000000000000020816681712,1,0.001
209,209_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,30.000000000000000000000000000000,1632,417,0.001000000000000000020816681712,1,0.05
210,210_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,47.000000000000000000000000000000,2571,145,0.001000000000000000020816681712,1,0.005
211,211_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,45.000000000000000000000000000000,2662,130,0.001000000000000000020816681712,1,0.25
212,212_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,3003,608,0.001000000000000000020816681712,1,0.25
213,213_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,22.000000000000000000000000000000,4350,904,0.001000000000000000020816681712,1,0.1
214,214_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,17.000000000000000000000000000000,248,177,0.001000000000000000020816681712,4,0.25
215,215_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,24.000000000000000000000000000000,2860,465,0.001000000000000000020816681712,1,0.001
216,216_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,21.000000000000000000000000000000,3266,573,0.998999999999999999111821580300,50,0.25
217,217_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,30.000000000000000000000000000000,701,181,0.001000000000000000020816681712,5,0.25
218,218_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,276,184,0.001000000000000000020816681712,5,0.05
219,219_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,5000,1472,0.998999999999999999111821580300,5,0.01
220,220_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,23.000000000000000000000000000000,5000,1414,0.998999999999999999111821580300,27,0.01
221,221_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,5000,1636,0.998999999999999999111821580300,36,0.1
222,222_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,1927,1585,0.998999999999999999111821580300,31,0.05
223,223_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,13.000000000000000000000000000000,5000,2558,0.998999999999999999111821580300,50,0.005
224,224_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,11.000000000000000000000000000000,277,4862,0.001000000000000000020816681712,1,0.01
225,225_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,11.000000000000000000000000000000,2002,645,0.998999999999999999111821580300,24,0.05
226,226_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,18.000000000000000000000000000000,646,4528,0.001000000000000000020816681712,1,0.01
227,227_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,9.000000000000000000000000000000,1,5000,0.001000000000000000020816681712,30,0.01
228,228_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,10.000000000000000000000000000000,703,5000,0.001000000000000000020816681712,19,0.025
229,229_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,5000,1869,0.625012658428213629946412766003,32,0.05
230,230_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,9.000000000000000000000000000000,1,5000,0.998999999999999999111821580300,41,0.25
231,231_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,22.000000000000000000000000000000,4449,1236,0.998999999999999999111821580300,27,0.005
232,232_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,10.000000000000000000000000000000,1428,4724,0.001000000000000000020816681712,13,0.025
233,233_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,17.000000000000000000000000000000,5000,1747,0.001000000000000000020816681712,33,0.05
234,234_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,5000,1642,0.001000000000000000020816681712,1,0.1
235,235_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,19.000000000000000000000000000000,5000,2357,0.001000000000000000020816681712,7,0.001
236,236_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,5000,2286,0.998999999999999999111821580300,50,0.1
237,237_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,5000,2133,0.762626266504902172727042852785,1,0.1
238,238_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,23.000000000000000000000000000000,650,955,0.001000000000000000020816681712,1,0.05
239,239_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,70.000000000000000000000000000000,2082,925,0.001000000000000000020816681712,35,0.001
240,240_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,5000,1696,0.998999999999999999111821580300,50,0.01
241,241_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,25.000000000000000000000000000000,372,190,0.236248037442890190806821237857,7,0.05
242,242_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,3121,1,0.001000000000000000020816681712,1,0.05
243,243_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,2811,1,0.001000000000000000020816681712,1,0.001
244,244_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,62.000000000000000000000000000000,5000,1928,0.001000000000000000020816681712,1,0.001
245,245_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,22.000000000000000000000000000000,2523,1,0.001000000000000000020816681712,1,0.25
246,246_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,19.000000000000000000000000000000,3221,1,0.001000000000000000020816681712,1,0.1
247,247_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,5000,1762,0.998999999999999999111821580300,1,0.05
248,248_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,42.000000000000000000000000000000,1402,631,0.154464910013801387522747177172,1,0.005
249,249_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,4256,2059,0.998999999999999999111821580300,20,0.1
250,250_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,21.000000000000000000000000000000,361,181,0.258242954875280650295366058344,6,0.001
251,251_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,24.000000000000000000000000000000,598,216,0.245490286731261109220980642931,15,0.001
252,252_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,16.000000000000000000000000000000,4998,2060,0.485434355752865309696630902181,50,0.01
253,253_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,16.000000000000000000000000000000,3475,1108,0.001000000000000000020816681712,1,0.25
254,254_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,11.000000000000000000000000000000,1,210,0.001000000000000000020816681712,26,0.25
255,255_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,461,741,0.998999999999999999111821580300,50,0.005
256,256_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,14.000000000000000000000000000000,491,722,0.973194004702465864653504468151,21,0.25
257,257_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,12.000000000000000000000000000000,2286,602,0.001000000000000000020816681712,50,0.05
258,258_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,22.000000000000000000000000000000,873,370,0.929674126995439831233625227469,1,0.25
259,259_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,25.000000000000000000000000000000,862,352,0.998999999999999999111821580300,1,0.025
260,260_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,14.000000000000000000000000000000,4103,2725,0.998999999999999999111821580300,50,0.01
261,261_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,16.000000000000000000000000000000,5000,2416,0.001000000000000000020816681712,27,0.001
262,262_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,35.000000000000000000000000000000,3935,495,0.001000000000000000020816681712,1,0.25
263,263_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,28.000000000000000000000000000000,325,113,0.001000000000000000020816681712,1,0.005
264,264_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,11.000000000000000000000000000000,2820,4046,0.998999999999999999111821580300,3,0.001
265,265_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,19.000000000000000000000000000000,3628,994,0.998999999999999999111821580300,50,0.01
266,266_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,23.000000000000000000000000000000,5000,923,0.998999999999999999111821580300,50,0.005
267,267_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,24.000000000000000000000000000000,3338,598,0.350653956870258021183417440625,1,0.005
268,268_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,13.000000000000000000000000000000,1915,4141,0.001000000000000000020816681712,1,0.05
269,269_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,2656,1410,0.998999999999999999111821580300,50,0.1
270,270_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,36.000000000000000000000000000000,1061,284,0.001000000000000000020816681712,8,0.001
271,271_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,19.000000000000000000000000000000,1094,707,0.998999999999999999111821580300,23,0.005
272,272_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,12.000000000000000000000000000000,5000,3721,0.001000000000000000020816681712,50,0.1
273,273_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,5000,1947,0.998999999999999999111821580300,19,0.05
274,274_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,47.000000000000000000000000000000,3090,202,0.998999999999999999111821580300,1,0.001
275,275_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,2131,0.337213543749004862615237243517,50,0.1
276,276_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,26.000000000000000000000000000000,5000,700,0.998999999999999999111821580300,1,0.025
277,277_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,19.000000000000000000000000000000,5000,2218,0.001000000000000000020816681712,1,0.005
278,278_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,11.000000000000000000000000000000,1,4895,0.001000000000000000020816681712,22,0.025
279,279_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,10.000000000000000000000000000000,1,5000,0.998999999999999999111821580300,1,0.01
280,280_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,13.000000000000000000000000000000,5000,3338,0.001000000000000000020816681712,10,0.05
281,281_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,52.000000000000000000000000000000,4042,1341,0.001000000000000000020816681712,1,0.05
282,282_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,12.000000000000000000000000000000,1,571,0.998999999999999999111821580300,30,0.25
283,283_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,22.000000000000000000000000000000,5000,1,0.001000000000000000020816681712,1,0.025
284,284_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,24.000000000000000000000000000000,5000,1,0.001000000000000000020816681712,1,0.005
285,285_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,68.000000000000000000000000000000,5000,245,0.998999999999999999111821580300,50,0.25
286,286_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2736,1,0.998999999999999999111821580300,1,0.005
287,287_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,27.000000000000000000000000000000,5000,863,0.998999999999999999111821580300,1,0.1
288,288_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,2421,0.998999999999999999111821580300,50,0.001
289,289_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,5000,1411,0.998999999999999999111821580300,38,0.1
290,290_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,2881,1106,0.998999999999999999111821580300,18,0.05
291,291_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,15.000000000000000000000000000000,5000,2848,0.001000000000000000020816681712,50,0.1
292,292_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,2830,0.998999999999999999111821580300,1,0.05
293,293_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,20.000000000000000000000000000000,5000,1262,0.998999999999999999111821580300,50,0.25
294,294_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,20.000000000000000000000000000000,220,3669,0.001000000000000000020816681712,1,0.025
295,295_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,5000,2338,0.998999999999999999111821580300,22,0.001
296,296_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,166.000000000000000000000000000000,1,501,0.001000000000000000020816681712,50,0.005
297,297_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,14.000000000000000000000000000000,1,948,0.998999999999999999111821580300,21,0.005
298,298_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,5000,2002,0.998999999999999999111821580300,50,0.05
299,299_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,31.000000000000000000000000000000,5000,620,0.001000000000000000020816681712,37,0.05
300,300_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,12.000000000000000000000000000000,5000,3179,0.998999999999999999111821580300,7,0.1
301,301_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,34.000000000000000000000000000000,4761,588,0.998999999999999999111821580300,50,0.05
302,302_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,5000,2079,0.998999999999999999111821580300,1,0.05
303,303_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,1771,1489,0.862143022771336808851572186541,1,0.005
304,304_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,12.000000000000000000000000000000,5000,3878,0.689881966804960167216620448016,26,0.05
305,305_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,15.000000000000000000000000000000,5000,2912,0.998999999999999999111821580300,44,0.05
306,306_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,1,575,0.998999999999999999111821580300,4,0.001
307,307_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,17.000000000000000000000000000000,5000,1859,0.998999999999999999111821580300,27,0.01
308,308_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,19.000000000000000000000000000000,2461,1183,0.998999999999999999111821580300,50,0.05
309,309_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,18.000000000000000000000000000000,5000,1963,0.001000000000000000020816681712,50,0.1
310,310_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,5000,1821,0.998999999999999999111821580300,50,0.05
311,311_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,24.000000000000000000000000000000,5000,1013,0.998999999999999999111821580300,30,0.005
312,312_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,24.000000000000000000000000000000,5000,1427,0.998999999999999999111821580300,50,0.005
313,313_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,59.000000000000000000000000000000,5000,1045,0.001000000000000000020816681712,1,0.005
314,314_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,21.000000000000000000000000000000,5000,1194,0.001000000000000000020816681712,22,0.1
315,315_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,12.000000000000000000000000000000,634,1169,0.576419989927566711607198612910,1,0.25
316,316_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,19.000000000000000000000000000000,5000,1624,0.736015017693554929323340729752,20,0.005
317,317_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,5000,775,0.001000000000000000020816681712,1,0.05
318,318_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,14.000000000000000000000000000000,5000,2200,0.998999999999999999111821580300,27,0.05
319,319_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,5000,2535,0.998999999999999999111821580300,21,0.05
320,320_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,14.000000000000000000000000000000,1,792,0.998999999999999999111821580300,1,0.05
321,321_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,23.000000000000000000000000000000,5000,1195,0.998999999999999999111821580300,1,0.001
322,322_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,21.000000000000000000000000000000,5000,1638,0.998999999999999999111821580300,50,0.05
323,323_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,15.000000000000000000000000000000,5000,2085,0.497227837929121785354169560378,26,0.005
324,324_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,9.000000000000000000000000000000,1,1031,0.998999999999999999111821580300,1,0.05
325,325_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,11.000000000000000000000000000000,1,225,0.998999999999999999111821580300,15,0.05
326,326_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,22.000000000000000000000000000000,5000,1568,0.001000000000000000020816681712,1,0.05
327,327_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,34.000000000000000000000000000000,3857,532,0.998999999999999999111821580300,25,0.05
328,328_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,2442,0.998999999999999999111821580300,1,0.1
329,329_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,8.000000000000000000000000000000,2094,5000,0.552895553834521713554295274662,17,0.005
330,330_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,13.000000000000000000000000000000,211,440,0.998999999999999999111821580300,2,0.1
331,331_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,79.000000000000000000000000000000,2274,306,0.001000000000000000020816681712,1,0.005
332,332_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,42.000000000000000000000000000000,5000,3812,0.001000000000000000020816681712,1,0.001
333,333_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,9.000000000000000000000000000000,1,4785,0.998999999999999999111821580300,1,0.01
334,334_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,12.000000000000000000000000000000,5000,3449,0.001000000000000000020816681712,1,0.25
335,335_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,13.000000000000000000000000000000,1,240,0.998999999999999999111821580300,19,0.005
336,336_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,9.000000000000000000000000000000,1,4881,0.998999999999999999111821580300,50,0.01
337,337_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,12.000000000000000000000000000000,5000,3670,0.998999999999999999111821580300,50,0.25
338,338_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,22.000000000000000000000000000000,3719,930,0.998999999999999999111821580300,1,0.05
339,339_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,5000,1334,0.998999999999999999111821580300,31,0.001
340,340_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,5000,3008,0.001000000000000000020816681712,50,0.005
341,341_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,5000,3263,0.998999999999999999111821580300,47,0.05
342,342_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,17.000000000000000000000000000000,541,1016,0.998999999999999999111821580300,50,0.005
343,343_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,13.000000000000000000000000000000,5000,3176,0.001000000000000000020816681712,32,0.1
344,344_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,28.000000000000000000000000000000,5000,4104,0.001000000000000000020816681712,50,0.001
345,345_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,84.000000000000000000000000000000,1085,134,0.001000000000000000020816681712,1,0.025
346,346_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,5000,1747,0.001000000000000000020816681712,50,0.1
347,347_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,36.000000000000000000000000000000,5000,629,0.998999999999999999111821580300,50,0.01
348,348_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,20.000000000000000000000000000000,5000,1543,0.998999999999999999111821580300,1,0.001
349,349_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,18.000000000000000000000000000000,5000,1731,0.998999999999999999111821580300,30,0.001
350,350_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,478,607,0.998999999999999999111821580300,6,0.25
351,351_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,19.000000000000000000000000000000,5000,1,0.001000000000000000020816681712,1,0.25
352,352_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,5000,3470,0.001000000000000000020816681712,33,0.1
353,353_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,17.000000000000000000000000000000,5000,2086,0.001000000000000000020816681712,50,0.25
354,354_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,51.000000000000000000000000000000,799,5000,0.001000000000000000020816681712,1,0.005
355,355_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,5000,1312,0.998999999999999999111821580300,47,0.05
356,356_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,25.000000000000000000000000000000,5000,1061,0.998999999999999999111821580300,50,0.005
357,357_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,17.000000000000000000000000000000,5000,2632,0.998999999999999999111821580300,50,0.025
358,358_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,5000,1422,0.998999999999999999111821580300,27,0.005
359,359_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,33.000000000000000000000000000000,5000,3278,0.001000000000000000020816681712,1,0.005
360,360_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,1970,0.998999999999999999111821580300,34,0.025
361,361_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,12.000000000000000000000000000000,5000,3614,0.001000000000000000020816681712,34,0.25
362,362_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,747,1215,0.001000000000000000020816681712,33,0.05
363,363_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,14.000000000000000000000000000000,5000,2698,0.001000000000000000020816681712,30,0.005
364,364_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,5000,3054,0.998999999999999999111821580300,32,0.25
365,365_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,10.000000000000000000000000000000,1,241,0.998999999999999999111821580300,14,0.25
366,366_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,9.000000000000000000000000000000,1,427,0.998999999999999999111821580300,1,0.25
367,367_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,13.000000000000000000000000000000,5000,2722,0.998999999999999999111821580300,1,0.05
368,368_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,5000,2367,0.736866465560709449356124878250,39,0.005
369,369_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,18.000000000000000000000000000000,1769,1318,0.998999999999999999111821580300,37,0.25
370,370_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,14.000000000000000000000000000000,5000,2757,0.998999999999999999111821580300,15,0.005
371,371_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,9.000000000000000000000000000000,1,4945,0.998999999999999999111821580300,27,0.01
372,372_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,10.000000000000000000000000000000,5000,4832,0.158321749748600776319662486458,1,0.25
373,373_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.640000000000000013322676295502,9.000000000000000000000000000000,1,4744,0.001000000000000000020816681712,1,0.01
374,374_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,4351,1509,0.998999999999999999111821580300,24,0.005
375,375_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,17.000000000000000000000000000000,1397,869,0.998999999999999999111821580300,1,0.025
376,376_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,16.000000000000000000000000000000,4853,3903,0.998999999999999999111821580300,50,0.1
377,377_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,14.000000000000000000000000000000,4372,2439,0.001000000000000000020816681712,1,0.1
378,378_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,24.000000000000000000000000000000,4087,824,0.998999999999999999111821580300,17,0.005
379,379_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.700000000000000066613381477509,14.000000000000000000000000000000,5000,3479,0.998999999999999999111821580300,26,0.001
380,380_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,20.000000000000000000000000000000,1272,1617,0.015685954577708650231615550297,1,0.25
381,381_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,1292,1600,0.998999999999999999111821580300,1,0.001
382,382_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.690000000000000057731597280508,17.000000000000000000000000000000,5000,3394,0.001000000000000000020816681712,50,0.005
383,383_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1363,1784,0.998999999999999999111821580300,15,0.25
384,384_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,1374,1759,0.998999999999999999111821580300,1,0.25
385,385_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,28.000000000000000000000000000000,1209,455,0.998999999999999999111821580300,1,0.001
386,386_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,14.000000000000000000000000000000,5000,5000,0.998999999999999999111821580300,50,0.025
387,387_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,5000,3338,0.998999999999999999111821580300,25,0.025
388,388_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,1251,1096,0.998999999999999999111821580300,1,0.1
389,389_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,17.000000000000000000000000000000,1279,2110,0.883249585386290059219049908279,1,0.05
390,390_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,5000,2333,0.998999999999999999111821580300,1,0.005
391,391_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,909,1475,0.998999999999999999111821580300,40,0.05
392,392_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,15.000000000000000000000000000000,945,1090,0.998999999999999999111821580300,16,0.25
393,393_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,1236,1353,0.998999999999999999111821580300,1,0.25
394,394_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,12.000000000000000000000000000000,956,5000,0.433803740558727501941405080288,50,0.25
395,395_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,1013,1120,0.001000000000000000020816681712,1,0.01
396,396_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,5000,2840,0.555863730524958410406100028922,50,0.005
397,397_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,26.000000000000000000000000000000,1205,621,0.998999999999999999111821580300,18,0.25
398,398_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,23.000000000000000000000000000000,4472,1191,0.001000000000000000020816681712,50,0.005
399,399_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,13.000000000000000000000000000000,1320,1902,0.998999999999999999111821580300,21,0.25
400,400_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,19.000000000000000000000000000000,1221,874,0.998999999999999999111821580300,50,0.05
401,401_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,14.000000000000000000000000000000,4658,2205,0.998999999999999999111821580300,1,0.025
402,402_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,1325,1162,0.617384226457470597360099873185,15,0.005
403,403_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,1165,723,0.998999999999999999111821580300,1,0.05
404,404_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,9.000000000000000000000000000000,966,4700,0.998999999999999999111821580300,35,0.1
405,405_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,12.000000000000000000000000000000,5000,3639,0.998999999999999999111821580300,37,0.025
406,406_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,10.000000000000000000000000000000,1071,4899,0.998999999999999999111821580300,1,0.25
407,407_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,1221,827,0.998999999999999999111821580300,1,0.25
408,408_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,1269,1262,0.998999999999999999111821580300,1,0.25
409,409_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,57.000000000000000000000000000000,1025,150,0.001000000000000000020816681712,1,0.25
410,410_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,25.000000000000000000000000000000,5000,2440,0.001000000000000000020816681712,50,0.005
411,411_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,4889,2166,0.998999999999999999111821580300,50,0.001
412,412_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,12.000000000000000000000000000000,1607,4408,0.998999999999999999111821580300,1,0.25
413,413_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,13.000000000000000000000000000000,5000,3114,0.001000000000000000020816681712,31,0.005
414,414_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,13.000000000000000000000000000000,1312,1715,0.998999999999999999111821580300,23,0.005
415,415_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,25.000000000000000000000000000000,1127,466,0.998999999999999999111821580300,1,0.25
416,416_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,19.000000000000000000000000000000,4456,1227,0.998999999999999999111821580300,34,0.001
417,417_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2139,1,0.001000000000000000020816681712,6,0.25
418,418_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,11.000000000000000000000000000000,1090,4924,0.998999999999999999111821580300,19,0.001
419,419_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,15.000000000000000000000000000000,4869,1858,0.998999999999999999111821580300,1,0.005
420,420_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,12.000000000000000000000000000000,5000,3777,0.998999999999999999111821580300,50,0.005
421,421_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,1206,651,0.998999999999999999111821580300,1,0.001
422,422_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,5000,3041,0.001000000000000000020816681712,50,0.05
423,423_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,17.000000000000000000000000000000,5000,1648,0.998999999999999999111821580300,1,0.05
424,424_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,15.000000000000000000000000000000,1246,1896,0.998999999999999999111821580300,1,0.01
425,425_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,12.000000000000000000000000000000,5000,3179,0.998999999999999999111821580300,50,0.005
426,426_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,12.000000000000000000000000000000,5000,2985,0.998999999999999999111821580300,14,0.1
427,427_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,13.000000000000000000000000000000,5000,4566,0.998999999999999999111821580300,24,0.025
428,428_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,31.000000000000000000000000000000,752,214,0.001000000000000000020816681712,16,0.05
429,429_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,1011,2105,0.001000000000000000020816681712,35,0.25
430,430_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,12.000000000000000000000000000000,5000,3075,0.998999999999999999111821580300,50,0.005
431,431_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,114.000000000000000000000000000000,5000,2189,0.001000000000000000020816681712,50,0.001
432,432_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,30.000000000000000000000000000000,826,243,0.998999999999999999111821580300,10,0.25
433,433_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,23.000000000000000000000000000000,4325,1113,0.998999999999999999111821580300,50,0.001
434,434_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,32.000000000000000000000000000000,935,340,0.870837937391372718387572149368,25,0.25
435,435_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,25.000000000000000000000000000000,4126,749,0.598717670853933014996073325165,8,0.001
436,436_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,44.000000000000000000000000000000,4204,414,0.998999999999999999111821580300,50,0.005
437,437_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,20.000000000000000000000000000000,1248,779,0.998999999999999999111821580300,1,0.005
438,438_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,21.000000000000000000000000000000,1393,828,0.971523261610774957475200608314,39,0.25
439,439_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,15.000000000000000000000000000000,5000,2851,0.607397544614624029790661552397,45,0.001
440,440_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,11.000000000000000000000000000000,2075,426,0.957181207638987174846079142299,1,0.25
441,441_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,5000,2367,0.308105087667469856871349520588,18,0.05
442,442_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,16.000000000000000000000000000000,4946,1572,0.998999999999999999111821580300,40,0.001
443,443_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,34.000000000000000000000000000000,4502,743,0.998999999999999999111821580300,24,0.001
444,444_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,22.000000000000000000000000000000,789,355,0.493948669742253765324591086028,22,0.25
445,445_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,62.000000000000000000000000000000,965,140,0.001000000000000000020816681712,23,0.005
446,446_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,22.000000000000000000000000000000,4363,1638,0.001000000000000000020816681712,30,0.005
447,447_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,12.000000000000000000000000000000,5000,2456,0.316141750630278939304673713195,16,0.005
448,448_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,15.000000000000000000000000000000,5000,1919,0.998999999999999999111821580300,37,0.005
449,449_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,13.000000000000000000000000000000,1220,1744,0.097617930160954255458527484279,18,0.1
450,450_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,22.000000000000000000000000000000,947,564,0.665366499225461538458148424979,9,0.05
451,451_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,1027,596,0.616443373987489273702067293925,10,0.25
452,452_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,19.000000000000000000000000000000,996,1,0.001000000000000000020816681712,1,0.005
453,453_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,40.000000000000000000000000000000,2157,61,0.001000000000000000020816681712,19,0.005
454,454_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,13.000000000000000000000000000000,1149,1967,0.499404537942137405348574930031,23,0.05
455,455_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,19.000000000000000000000000000000,992,1,0.001000000000000000020816681712,1,0.001
456,456_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,1206,2117,0.001000000000000000020816681712,1,0.25
457,457_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,16.000000000000000000000000000000,939,1107,0.470836799213616208348298641795,39,0.05
458,458_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,16.000000000000000000000000000000,1189,1228,0.001000000000000000020816681712,17,0.25
459,459_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,14.000000000000000000000000000000,5000,3391,0.719810596665692026263627667504,28,0.1
460,460_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,24.000000000000000000000000000000,832,359,0.998999999999999999111821580300,12,0.005
461,461_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,15.000000000000000000000000000000,1211,1925,0.001000000000000000020816681712,14,0.25
462,462_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,2022,0.696208027900989567804401758622,21,0.001
463,463_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,22.000000000000000000000000000000,4513,1792,0.001000000000000000020816681712,1,0.005
464,464_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,20.000000000000000000000000000000,5000,2916,0.001000000000000000020816681712,50,0.001
465,465_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,19.000000000000000000000000000000,3881,1134,0.228331464743312534393382406961,33,0.005
466,466_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,22.000000000000000000000000000000,1231,842,0.998999999999999999111821580300,42,0.005
467,467_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,2289,0.338937483132411676578499282186,18,0.025
468,468_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,22.000000000000000000000000000000,4510,1609,0.998999999999999999111821580300,17,0.001
469,469_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,14.000000000000000000000000000000,5000,2625,0.288802517819061299597649394855,19,0.001
470,470_0,FAILED,BoTorch,BOTORCH_MODULAR,,,81,1,0.706665344028530650888342279359,1,0.001
471,471_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,13.000000000000000000000000000000,1315,3010,0.998999999999999999111821580300,1,0.25
472,472_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,65.000000000000000000000000000000,5000,1067,0.001000000000000000020816681712,1,0.001
473,473_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,47.000000000000000000000000000000,3112,219,0.496120247557045757069715818943,1,0.25
474,474_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,23.000000000000000000000000000000,4472,1083,0.703728524949239542252144019585,26,0.25
475,475_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,15.000000000000000000000000000000,5000,4892,0.209795755640989262102991119718,33,0.1
476,476_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,11.000000000000000000000000000000,5000,4016,0.998999999999999999111821580300,16,0.05
477,477_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,20.000000000000000000000000000000,3663,1,0.001000000000000000020816681712,1,0.001
478,478_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,11.000000000000000000000000000000,1913,3945,0.998999999999999999111821580300,1,0.25
479,479_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,12.000000000000000000000000000000,1124,1732,0.566411052719541396882618755626,12,0.05
480,480_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,12.000000000000000000000000000000,1253,2271,0.001000000000000000020816681712,19,0.25
481,481_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,10.000000000000000000000000000000,530,5000,0.594682933026292603884144227777,32,0.25
482,482_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,11.000000000000000000000000000000,1350,4367,0.616196791523190290362776977418,15,0.25
483,483_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,10.000000000000000000000000000000,1179,4709,0.643950021181341702991574038606,1,0.25
484,484_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,13.000000000000000000000000000000,1085,2089,0.552854347752510411062587536435,21,0.25
485,485_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,1211,1583,0.599769071741309334200309422158,1,0.01
486,486_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,26.000000000000000000000000000000,3967,676,0.741252808628656612022211902513,10,0.005
487,487_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,5000,1375,0.998999999999999999111821580300,46,0.001
488,488_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,18.000000000000000000000000000000,4543,1354,0.998999999999999999111821580300,40,0.001
489,489_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,23.000000000000000000000000000000,983,585,0.716816730732545592807980483485,9,0.005
490,490_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,13.000000000000000000000000000000,1493,2809,0.451392078340024760496618227990,1,0.25
491,491_0,FAILED,BoTorch,BOTORCH_MODULAR,,,976,1,0.998999999999999999111821580300,1,0.005
492,492_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,16.000000000000000000000000000000,5000,3501,0.998999999999999999111821580300,1,0.005
493,493_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,1093,911,0.578334235374765359694038124871,1,0.05
494,494_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,18.000000000000000000000000000000,1040,2379,0.186765508281399539303180290517,1,0.25
495,495_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,13.000000000000000000000000000000,1564,2098,0.787102400818278202798694564990,38,0.1
496,496_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,11.000000000000000000000000000000,5000,4389,0.998999999999999999111821580300,50,0.1
497,497_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,1539,3020,0.998999999999999999111821580300,21,0.25
498,498_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,1124,931,0.998999999999999999111821580300,1,0.001
499,499_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,13.000000000000000000000000000000,5000,2489,0.001000000000000000020816681712,21,0.05
500,500_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,25.000000000000000000000000000000,1046,432,0.998999999999999999111821580300,21,0.005
501,501_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,15.000000000000000000000000000000,1407,3556,0.998999999999999999111821580300,14,0.25
502,502_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,5000,2645,0.998999999999999999111821580300,32,0.025
503,503_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1019,1,0.998999999999999999111821580300,1,0.005
504,504_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,13.000000000000000000000000000000,1270,4901,0.453501489125514745115452797108,10,0.25
505,505_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,28.000000000000000000000000000000,4237,720,0.001000000000000000020816681712,18,0.005
506,506_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,11.000000000000000000000000000000,5000,4793,0.797978763478446428969448334101,40,0.025
507,507_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,23.000000000000000000000000000000,4160,749,0.987219971018176933874599399132,9,0.001
508,508_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,11.000000000000000000000000000000,5000,4331,0.914341552477951902133668227179,15,0.1
509,509_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,11.000000000000000000000000000000,1467,3871,0.998999999999999999111821580300,33,0.1
510,510_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,10.000000000000000000000000000000,1144,4013,0.998999999999999999111821580300,24,0.1
511,511_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,2314,0.001000000000000000020816681712,18,0.001
512,512_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,1656,3146,0.998999999999999999111821580300,16,0.25
513,513_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,13.000000000000000000000000000000,5000,3212,0.952164192579220358680913705030,27,0.05
514,514_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,1340,904,0.630547454819600061703965820925,10,0.025
515,515_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,13.000000000000000000000000000000,5000,2737,0.252208302783172688688040352645,31,0.001
516,516_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,16.000000000000000000000000000000,5000,2664,0.998999999999999999111821580300,50,0.001
517,517_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,11.000000000000000000000000000000,5000,4764,0.001000000000000000020816681712,43,0.1
518,518_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,30.000000000000000000000000000000,4181,608,0.998999999999999999111821580300,50,0.025
519,519_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,14.000000000000000000000000000000,1296,1900,0.577429254285928283962903151405,38,0.05
520,520_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,4385,1897,0.990136387460684219696815944189,29,0.001
521,521_0,FAILED,BoTorch,BOTORCH_MODULAR,,,960,1,0.998999999999999999111821580300,1,0.005
522,522_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,17.000000000000000000000000000000,342,388,0.784883862613219518422624787490,21,0.05
523,523_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,13.000000000000000000000000000000,1704,3412,0.654952741100691127051902640233,37,0.25
524,524_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,14.000000000000000000000000000000,5000,3539,0.827535631668396054472225387144,29,0.005
525,525_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,16.000000000000000000000000000000,1428,1602,0.998999999999999999111821580300,11,0.25
526,526_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2270,1,0.790078134615081806302328004676,1,0.025
527,527_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,11.000000000000000000000000000000,5000,4653,0.634810865101015364686531938787,13,0.025
528,528_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,3192,3084,0.583329963201470125255809762166,24,0.025
529,529_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,11.000000000000000000000000000000,5000,4887,0.990313243834416323529978853912,17,0.05
530,530_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,1692,1520,0.665169349761729034042900821078,24,0.025
531,531_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,12.000000000000000000000000000000,1675,3800,0.340589607579669495507346255181,23,0.025
532,532_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1066,1,0.998999999999999999111821580300,1,0.005
533,533_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,11.000000000000000000000000000000,5000,4043,0.998999999999999999111821580300,37,0.1
534,534_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,14.000000000000000000000000000000,1647,2674,0.998999999999999999111821580300,1,0.25
535,535_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3851,1,0.998999999999999999111821580300,27,0.005
536,536_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,10.000000000000000000000000000000,1120,4822,0.368903649122475196797665830672,3,0.001
537,537_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,15.000000000000000000000000000000,1170,1152,0.465493178077840297213185749570,50,0.1
538,538_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1137,1,0.998999999999999999111821580300,1,0.005
539,539_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,10.000000000000000000000000000000,5000,4647,0.998999999999999999111821580300,50,0.1
540,540_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,16.000000000000000000000000000000,4442,1683,0.503145068717129273494492736063,40,0.05
541,541_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,11.000000000000000000000000000000,1875,4876,0.998999999999999999111821580300,21,0.25
542,542_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,12.000000000000000000000000000000,5000,3672,0.244776267673663311397191932883,17,0.025
543,543_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1049,1,0.998999999999999999111821580300,1,0.005
544,544_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,26.000000000000000000000000000000,958,432,0.613010075674615628749108964257,9,0.001
545,545_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,2598,874,0.998999999999999999111821580300,1,0.005
546,546_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1082,1,0.998999999999999999111821580300,1,0.005
547,547_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,33.000000000000000000000000000000,4647,637,0.001000000000000000020816681712,50,0.025
548,548_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,18.000000000000000000000000000000,1255,2186,0.543908476122150319476133972785,1,0.1
549,549_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,14.000000000000000000000000000000,1788,2888,0.528245698580714351244580484490,16,0.05
550,550_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,15.000000000000000000000000000000,1783,2372,0.998999999999999999111821580300,45,0.05
551,551_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,20.000000000000000000000000000000,1888,1912,0.708161695598597762391079868394,13,0.025
552,552_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,16.000000000000000000000000000000,5000,3559,0.585242384669634518878922335716,50,0.05
553,553_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,33.000000000000000000000000000000,1560,2268,0.001000000000000000020816681712,1,0.005
554,554_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,4550,1068,0.900549167007930639883284129610,40,0.001
555,555_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,33.000000000000000000000000000000,1096,341,0.001000000000000000020816681712,22,0.25
556,556_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,13.000000000000000000000000000000,5000,3894,0.998999999999999999111821580300,28,0.01
557,557_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,22.000000000000000000000000000000,1143,1,0.001000000000000000020816681712,1,0.25
558,558_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,4398,1271,0.998999999999999999111821580300,18,0.001
559,559_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,18.000000000000000000000000000000,4841,1769,0.998999999999999999111821580300,36,0.005
560,560_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,13.000000000000000000000000000000,5000,2990,0.755565509925002376512281898613,29,0.05
561,561_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,22.000000000000000000000000000000,4027,952,0.998999999999999999111821580300,9,0.005
562,562_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,4449,1085,0.587561646133048620654903970717,18,0.25
563,563_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,14.000000000000000000000000000000,5000,4139,0.998999999999999999111821580300,20,0.001
564,564_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,27.000000000000000000000000000000,1110,385,0.998999999999999999111821580300,1,0.05
565,565_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,19.000000000000000000000000000000,3955,999,0.770467821798460739124436713610,50,0.005
566,566_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,12.000000000000000000000000000000,1744,3349,0.001000000000000000020816681712,24,0.25
567,567_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,11.000000000000000000000000000000,5000,3755,0.280694410259357651771239261507,40,0.025
568,568_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,11.000000000000000000000000000000,1548,4123,0.794270977753196216752940017614,1,0.25
569,569_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,11.000000000000000000000000000000,2117,786,0.851577535553902031928430460539,1,0.025
570,570_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,1712,2957,0.998999999999999999111821580300,1,0.1
571,571_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,1657,2729,0.736081228401502696279123938439,36,0.1
572,572_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,17.000000000000000000000000000000,963,1185,0.482226181719227542377126383144,42,0.025
573,573_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3745,1,0.001000000000000000020816681712,1,0.05
</pre>
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				createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
			
			<h1>&nbsp;Errors</h1>
			<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'> Download &raquo;oo_errors.txt&laquo; as file</button>
<pre id='simple_pre_tab_tab_errors'><span style="background-color: black; color: white">
/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4915725/4915725_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4944253/4944253_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4956957/4956957_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4964054/4964054_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4975984/4975984_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4979001/4979001_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4983103/4983103_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4985169/4985169_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4987346/4987346_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4993918/4993918_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4996186/4996186_0_log.err not found

</span></pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'> Download &raquo;oo_errors.txt&laquo; as file</button>
			
			<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>50000                                                                                                                            </td></tr><tr><td> run_program</td><td>None                                                                                                                             </td></tr><tr><td> experiment_name</td><td>None                                                                                                                             </td></tr><tr><td> mem_gb</td><td>32                                                                                                                               </td></tr><tr><td> parameter</td><td>None                                                                                                                             </td></tr><tr><td> continue_previous_job</td><td>/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/ </td></tr><tr><td> experiment_constraints</td><td>None                                                                                                                             </td></tr><tr><td> run_dir</td><td>runs                                                                                                                             </td></tr><tr><td> seed</td><td>None                                                                                                                             </td></tr><tr><td> decimalrounding</td><td>4                                                                                                                                </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>None                                                                                                                             </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>False                                                                                                                            </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>10                                                                                                                               </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> occ_type</td><td>euclid                                                                                                                           </td></tr><tr><td> result_names</td><td>[&#039;RESULT=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>False                                                                                                                            </td></tr><tr><td> revert_to_random_when_seemingly_exhausted</td><td>True                                                                                                                             </td></tr><tr><td> load_data_from_existing_jobs</td><td>[]                                                                                                                               </td></tr><tr><td> n_estimators_randomforest</td><td>100                                                                                                                              </td></tr><tr><td> external_generator</td><td>None                                                                                                                             </td></tr><tr><td> username</td><td>None                                                                                                                             </td></tr><tr><td> max_failed_jobs</td><td>None                                                                                                                             </td></tr><tr><td> num_parallel_jobs</td><td>20                                                                                                                               </td></tr><tr><td> worker_timeout</td><td>120                                                                                                                              </td></tr><tr><td> slurm_use_srun</td><td>False                                                                                                                            </td></tr><tr><td> time</td><td></td></tr><tr><td> partition</td><td></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>
			<div class='invert_in_dark_mode' id='workerUsagePlot'></div><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>
<pre id="pre_tab_worker_usage">1746192465.8102438,20,0,0
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1746512245.9427915,20,1,5
1746515582.0945656,20,1,5
1746515582.5140603,20,1,5
1746515584.1889498,20,2,10
1746515584.9818573,20,2,10
1746515594.1629043,20,1,5
1746515594.2324154,20,1,5
1746519768.2213986,20,1,5
1746519768.6531162,20,1,5
1746519770.2909544,20,2,10
1746519771.1544025,20,2,10
1746519780.5363555,20,1,5
1746519780.6239035,20,1,5
1746523922.4155202,20,1,5
1746523923.0635438,20,1,5
1746523924.6644974,20,2,10
1746523925.425143,20,2,10
1746523934.493617,20,1,5
1746523934.564872,20,1,5
1746527374.9311678,20,1,5
1746527375.2183287,20,1,5
1746527376.9201856,20,2,10
1746527377.6939027,20,2,10
1746527387.0918043,20,1,5
1746527387.16082,20,1,5
1746531562.0529566,20,1,5
1746531562.4067209,20,1,5
1746531564.1972477,20,2,10
1746531565.0265117,20,2,10
1746531574.0573492,20,1,5
1746531574.353934,20,1,5
1746534614.4113328,20,1,5
1746534614.9292965,20,1,5
1746534616.3474941,20,2,10
1746534616.803036,20,2,10
1746534624.9085295,20,1,5
1746534624.9759374,20,1,5
1746539149.3458073,20,1,5
1746539150.0816836,20,1,5
1746539152.0333977,20,2,10
1746539152.9040167,20,2,10
1746539162.7776577,20,1,5
1746539162.8770583,20,1,5
1746543633.3721035,20,1,5
1746543634.0605073,20,1,5
1746543635.9907746,20,2,10
1746543636.5447025,20,2,10
1746543646.209352,20,1,5
1746543646.2670622,20,1,5
1746547339.8635316,20,1,5
1746547340.2508917,20,1,5
1746547342.0711825,20,2,10
1746547342.8570883,20,2,10
1746547351.8996854,20,1,5
1746547351.979461,20,1,5
1746551186.276112,20,1,5
1746551186.7364514,20,1,5
1746551188.4204118,20,2,10
1746551188.8769965,20,2,10
1746551198.5775743,20,1,5
1746551198.6589954,20,1,5
1746555387.7892692,20,1,5
1746555388.2714853,20,1,5
1746555390.2221127,20,2,10
1746555391.0119956,20,2,10
1746555400.1799307,20,1,5
1746555400.3569615,20,1,5
1746555407.5255208,20,1,5
1746555418.9446833,20,1,5
1746555430.1928167,20,1,5
1746555441.1337428,20,1,5
1746555446.8352575,20,1,5
1746555454.1081662,20,0,0
1746555464.9266698,20,0,0
1746560104.9333854,20,0,0
1746560105.2401624,20,0,0
1746560107.1832128,20,1,5
1746560113.1193898,20,1,5
1746564708.8441713,20,1,5
1746564709.2883031,20,1,5
1746564711.2708485,20,2,10
1746564712.069794,20,2,10
1746564721.3890724,20,1,5
1746564721.476196,20,1,5
1746566295.918618,20,1,5
1746566296.2047093,20,1,5
1746566297.6343057,20,2,10
1746566298.1922827,20,2,10
1746566306.7721286,20,1,5
1746566306.8319376,20,1,5
1746570290.169903,20,1,5
1746570290.6001804,20,1,5
1746570292.1972206,20,2,10
1746570292.6491976,20,2,10
1746570302.6162298,20,1,5
1746570302.6883333,20,1,5
1746573501.996616,20,1,5
1746573502.3752596,20,1,5
1746573505.2453136,20,2,10
1746573506.1456866,20,2,10
1746573515.0729506,20,1,5
1746573515.1460142,20,1,5
1746578189.9821026,20,1,5
1746578190.4478145,20,1,5
1746578192.2213223,20,2,10
1746578193.1091475,20,2,10
1746578203.620815,20,1,5
1746578203.6965082,20,1,5
1746583119.467085,20,1,5
1746583119.9934475,20,1,5
1746583121.5517612,20,2,10
1746583122.1214678,20,2,10
1746583131.1506052,20,1,5
1746583131.2146277,20,1,5
1746587817.7863662,20,1,5
1746587818.2500544,20,1,5
1746587820.149888,20,2,10
1746587820.9743733,20,2,10
1746587831.092777,20,1,5
1746587831.1757085,20,1,5
1746592437.823858,20,1,5
1746592438.2279067,20,1,5
1746592440.0776916,20,2,10
1746592440.8838189,20,2,10
1746592449.7408721,20,1,5
1746592449.8057902,20,1,5
1746596755.2433736,20,1,5
1746596755.6767828,20,1,5
1746596757.2873564,20,2,10
1746596757.7431304,20,2,10
1746596768.2037785,20,1,5
1746596768.2749164,20,1,5
1746601326.15501,20,1,5
1746601326.613294,20,1,5
1746601328.2047348,20,2,10
1746601329.0411735,20,2,10
1746601338.422289,20,1,5
1746601338.4971356,20,1,5
1746605477.9541993,20,1,5
1746605478.2448094,20,1,5
1746605480.0825393,20,2,10
1746605480.5901089,20,2,10
1746605489.9298472,20,1,5
1746605489.988339,20,1,5
1746610218.5750873,20,1,5
1746610219.0663254,20,1,5
1746610220.9721856,20,2,10
1746610221.4194124,20,2,10
1746610232.375301,20,1,5
1746610232.4557533,20,1,5
1746614536.0306528,20,1,5
1746614536.4736557,20,1,5
1746614538.26797,20,2,10
1746614539.0998964,20,2,10
1746614548.7506287,20,1,5
1746614548.823629,20,1,5
1746618976.7558093,20,1,5
1746618977.0976183,20,1,5
1746618978.9487846,20,2,10
1746618979.602543,20,2,10
1746618989.3698885,20,1,5
1746618989.452135,20,1,5
1746622323.9878886,20,1,5
1746622324.3044364,20,1,5
1746622326.1852987,20,2,10
1746622326.4890466,20,2,10
1746622336.6469946,20,1,5
1746622336.7099776,20,1,5
1746626910.5368204,20,1,5
1746626911.281941,20,1,5
1746626913.3365993,20,2,10
1746626914.376245,20,2,10
1746626926.3324878,20,1,5
1746626926.4414074,20,1,5
1746632748.1952758,20,1,5
1746632748.7233639,20,1,5
1746632750.3805816,20,2,10
1746632751.3459067,20,2,10
1746632763.927812,20,1,5
1746632764.069912,20,1,5
1746638497.736578,20,1,5
1746638498.424777,20,1,5
1746638500.4585538,20,2,10
1746638501.9330266,20,2,10
1746638515.4368944,20,1,5
1746638515.559691,20,1,5
1746645914.747814,20,1,5
1746645915.243792,20,1,5
1746645917.2262936,20,2,10
1746645918.0506744,20,2,10
1746645929.247414,20,1,5
1746645929.4013646,20,1,5
1746645938.1890187,20,1,5
1746645943.602413,20,1,5
1746645951.864549,20,0,0
1746645963.0894663,20,0,0
1746651383.9208035,20,0,0
1746651384.2907846,20,0,0
1746651386.2619019,20,1,5
1746651392.8779545,20,1,5
1746656009.2169204,20,1,5
1746656009.6427572,20,1,5
1746656011.2362747,20,2,10
1746656012.114007,20,2,10
1746656022.2828956,20,1,5
1746656022.5467947,20,1,5
1746661262.4648676,20,1,5
1746661263.1509426,20,1,5
1746661265.0225585,20,2,10
1746661265.8620245,20,2,10
1746661276.531405,20,1,5
1746661276.622796,20,1,5
1746665067.3107731,20,1,5
1746665067.9721467,20,1,5
1746665069.576054,20,2,10
1746665070.049074,20,2,10
1746665082.197096,20,1,5
1746665082.3144238,20,1,5
1746668238.8447618,20,1,5
1746668239.2868907,20,1,5
1746668241.166873,20,2,10
1746668242.0884929,20,2,10
1746668253.0145693,20,1,5
1746668253.1019807,20,1,5
1746673839.8797097,20,1,5
1746673840.3588645,20,1,5
1746673842.2378142,20,2,10
1746673843.0522282,20,2,10
1746673854.1210048,20,1,5
1746673854.2226746,20,1,5
1746679035.5064218,20,1,5
1746679036.1272464,20,1,5
1746679038.0349877,20,2,10
1746679038.884158,20,2,10
1746679048.926372,20,1,5
1746679049.0100052,20,1,5
1746684710.971431,20,1,5
1746684711.3129776,20,1,5
1746684713.1162858,20,2,10
1746684713.6581752,20,2,10
1746684723.5970187,20,1,5
1746684723.665582,20,1,5
1746690326.6024342,20,1,5
1746690327.143585,20,1,5
1746690328.98143,20,2,10
1746690329.8491688,20,2,10
1746690341.2821925,20,1,5
1746690341.3741827,20,1,5
1746695472.8310795,20,1,5
1746695473.3202949,20,1,5
1746695475.184712,20,2,10
1746695476.076693,20,2,10
1746695487.2479792,20,1,5
1746695487.3350642,20,1,5
1746700676.2002869,20,1,5
1746700676.6588266,20,1,5
1746700678.2323632,20,2,10
1746700679.0792506,20,2,10
1746700690.0060384,20,1,5
1746700690.0950978,20,1,5
1746705713.8628864,20,1,5
1746705714.329795,20,1,5
1746705716.1951718,20,2,10
1746705717.0632298,20,2,10
1746705726.9142978,20,1,5
1746705727.1061118,20,1,5
1746711313.0248275,20,1,5
1746711313.462273,20,1,5
1746711315.1758947,20,2,10
1746711315.9417589,20,2,10
1746711326.9631722,20,1,5
1746711327.0423913,20,1,5
1746715948.7305589,20,1,5
1746715949.0865471,20,1,5
1746715950.5456812,20,2,10
1746715951.1573248,20,2,10
1746715960.8207364,20,1,5
1746715961.0055473,20,1,5
1746721648.4438875,20,1,5
1746721649.1725566,20,1,5
1746721651.060816,20,2,10
1746721651.9447515,20,2,10
1746721662.1728795,20,1,5
1746721662.2523296,20,1,5
1746726396.7595491,20,1,5
1746726397.2152736,20,1,5
1746726399.0540276,20,2,10
1746726399.9119785,20,2,10
1746726409.379312,20,1,5
1746726409.4518352,20,1,5
1746730772.720787,20,1,5
1746730773.153216,20,1,5
1746730775.0485675,20,2,10
1746730775.9462705,20,2,10
1746730785.649178,20,1,5
1746730785.720854,20,1,5
1746736265.9258335,20,1,5
1746736266.3591776,20,1,5
1746736268.1304145,20,2,10
1746736268.9528208,20,2,10
1746736280.122871,20,1,5
1746736280.2025342,20,1,5
1746741350.268273,20,1,5
1746741350.65405,20,1,5
1746741352.123716,20,2,10
1746741352.670183,20,2,10
1746741362.3998134,20,1,5
1746741362.846611,20,1,5
1746747182.2831273,20,1,5
1746747182.7991295,20,1,5
1746747184.3996642,20,2,10
1746747185.1707056,20,2,10
1746747195.695508,20,1,5
1746747195.8598654,20,1,5
</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
1746192465,632.30859375,34.3
1746192465,629.62109375,34.4
1746192465,629.6875,33.6
1746192465,629.6875,41.0
1746192465,629.6875,29.0
1746192465,629.6875,35.5
1746192465,629.6875,36.8
1746227798,896.39453125,28.5
1746227798,896.39453125,14.9
1746227798,896.39453125,14.6
1746227798,896.39453125,17.1
1746260995,862.7265625,15.7
1746260995,862.7265625,13.9
1746260995,862.7265625,13.8
1746260995,862.7265625,19.4
1746302382,918.7421875,15.8
1746302382,918.7421875,15.3
1746302382,918.7421875,15.4
1746302382,918.7421875,18.4
1746356629,944.59375,15.7
1746356629,944.59375,16.7
1746356630,944.59375,16.7
1746356630,944.59375,17.1
1746415961,975.6875,18.4
1746415961,975.6875,12.8
1746415961,975.6875,13.0
1746415961,975.6875,16.3
1746478536,1003.18359375,16.1
1746478536,1003.18359375,13.7
1746478536,1003.18359375,13.0
1746478536,1003.18359375,17.5
1746555407,1005.59375,15.9
1746555407,1005.59375,14.9
1746555407,1005.59375,15.0
1746555407,1005.59375,15.0
1746645937,1133.29296875,18.2
1746645937,1133.29296875,22.2
1746645937,1133.29296875,21.0
1746645938,1133.29296875,27.3
1746747202,1046.046875,20.0
1746747203,1046.046875,16.5
1746747203,1046.046875,16.4
1746747203,1046.046875,17.6
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download &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>
			<div class="invert_in_dark_mode" id="plotResultPairs"></div>
			<h1>&nbsp;Evolution</h1>
			<div class="invert_in_dark_mode" id="plotResultEvolution"></div>
			<h1>&nbsp;Exit-Codes</h1>
			<div class="invert_in_dark_mode" id="plotExitCodesPieChart"></div>
	</body>
</html>