OmniOpt2

Setting	Value
Max. nr. evaluations	50337
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	337
Generation Node	Total	COMPLETED	FAILED	RUNNING
SOBOL	30	30	0	0
BOTORCH_MODULAR	564	513	50	1
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
50	543	1	594
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_TMDBalanced5s_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
Parameter	Min	Max	Mean	Std Dev	Count
generation_method	No numerical statistics available
ACCURACY	0.81	0.85	0.8414	0.0043	543
RUNTIME	0	529	5.8545	30.1325	543
recent_samples_size	1	5000	2832.1616	1649.5404	594
n_samples	1	5000	2382.3603	1561.7867	594
feature_proportion	0.001	0.999	0.5	0.3319	594
n_clusters	1	50	26.4091	20.162	594
confidence	0.001	0.25	0.0583	0.0808	594
Skipped tabs:
Job-Infos
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		<title>Exported &raquo;s4122485/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/12&laquo; from OmniOpt2-Share</title>
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}

.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;
	border: solid 1px;
}

#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: sans-serif;
}

.gridjs-th-content {
	overflow: visible !important;
}

.error_text {
	color: red;
}

h1, h2, h3, h4, h5, h6 {
	margin-top: 1em;
	font-weight: bold;
	color: #333;
	border-left: 5px solid #ccc;
	padding-left: 0.5em;
}

.no_cursive {
	font-style: normal;	
}

.caveat {
	background-color: #fff8b3;
	border: 1px solid #f2d600;
	padding: 1em 1em 1em 70px;
	position: relative;
	font-family: sans-serif;
	color: #665500;
	margin: 1em 0;
	border-radius: 4px;
}

.caveat h1, .caveat h2, .caveat h3, .caveat h4 {
	margin-top: 0;
	margin-bottom: 0.5em;
	font-weight: bold;
}

.caveat::before {
	content: "⚠️";
	font-size: 50px;
	line-height: 1;
	position: absolute;
	left: 10px;
	top: 50%;
	transform: translateY(-50%);
	pointer-events: none;
	user-select: none;
}

.caveat.warning::before { content: "⚠️"; }
.caveat.stop::before { content: "🛑"; }
.caveat.exclamation::before { content: "❗"; }
.caveat.alarm::before { content: "🚨"; }
.caveat.tip::before { content: "💡"; }

.tutorial_icon {
	display: inline-block;
	font-size: 1.3em;
	line-height: 1;
	vertical-align: middle;
	transform: translateY(-10%);
	padding: 0.2em 0;
}

.highlight {
	background-color: yellow;
	font-weight: bold;
}

#searchResults li {
	opacity: 0;
	transform: translateY(8px);
	animation: fadeInUp 0.3s ease-out forwards;
	animation-delay: 0.05s;
	list-style: none;
	margin-bottom: 5px;
}

@keyframes fadeInUp {
	to {
		opacity: 1;
		transform: translateY(0);
	}
}

.search_headline {
	font-weight: bold;
	margin-top: 1em;
	margin-bottom: 0.3em;
	color: #444;
}

.search_share_path {
	color: black;
	display: block ruby;
	margin-top: 20px;
}

@media print {
	#scads_bar {
		display: none !important;
	}
}

/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
	color: #222
}

.surface{
	background: #ece9d8
}

u{
	text-decoration: none;
	border-bottom: .5px solid #222
}

a{
	color: #00f
}

a: focus{
	outline: 1px dotted #00f
}

code,code *{
	font-family: monospace
}

pre{
	display: block;
	padding: 12px 8px;
	background-color: #000;
	color: silver;
	font-size: 1rem;
	margin: 0;
	overflow: scroll;
}

summary: focus{
	outline: 1px dotted #000
}

: :-webkit-scrollbar{
	width: 16px
}

: :-webkit-scrollbar: horizontal{
	height: 17px
}

: :-webkit-scrollbar-track{
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}

: :-webkit-scrollbar-thumb{
	background-color: #dfdfdf;
	box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}

: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
	display: block
}

: :-webkit-scrollbar-button: vertical: start{
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}

: :-webkit-scrollbar-button: vertical: end{
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}

: :-webkit-scrollbar-button: horizontal: start{
	width: 16px;
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}

: :-webkit-scrollbar-button: horizontal: end{
	width: 16px;
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}

button{
	border: none;
	background: #ece9d8;
	box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
	border-radius: 0;
	min-width: 75px;
	min-height: 23px;
	padding: 0 12px
}

button: not(: disabled).active,button: not(: disabled): active{
	box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}

button.focused,button: focus{
	outline: 1px dotted #000;
	outline-offset: -4px
}

label{
	display: inline-flex;
	align-items: center
}

textarea{
	padding: 3px 4px;
	border: none;
	background-color: #fff;
	box-sizing: border-box;
	-webkit-appearance: none;
	-moz-appearance: none;
	appearance: none;
	border-radius: 0
}

textarea: focus{
	outline: none
}

select: focus option{
	color: #000;
	background-color: #fff
}

.vertical-bar{
	width: 4px;
	height: 20px;
	background: silver;
	box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}

&: disabled,&: disabled+label{
	color: grey;
	text-shadow: 1px 1px 0 #fff
}

input[type=radio]+label{
	line-height: 13px;
	position: relative;
	margin-left: 19px
}

input[type=radio]+label: before{
	content: "";
	position: absolute;
	top: 0;
	left: -19px;
	display: inline-block;
	width: 13px;
	height: 13px;
	margin-right: 6px;
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}

input[type=radio]: active+label: before{
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}

input[type=radio]: checked+label: after{
	content: "";
	display: block;
	width: 5px;
	height: 5px;
	top: 5px;
	left: -14px;
	position: absolute;
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}

input[type=radio][disabled]+label: before{
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}

input[type=radio][disabled]: checked+label: after{
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}

input[type=email],input[type=password]{
	padding: 3px 4px;
	border: 1px solid #7f9db9;
	background-color: #fff;
	box-sizing: border-box;
	-webkit-appearance: none;
	-moz-appearance: none;
	appearance: none;
	border-radius: 0;
	height: 21px;
	line-height: 2
}

input[type=email]: focus,input[type=password]: focus{
	outline: none
}

input[type=range]{
	-webkit-appearance: none;
	width: 100%;
	background: transparent
}

input[type=range]: focus{
	outline: none
}

input[type=range]: :-webkit-slider-thumb{
	-webkit-appearance: none;
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}

input[type=range]: :-moz-range-thumb{
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}

input[type=range]: :-webkit-slider-runnable-track{
	background: #000;
	border-right: 1px solid grey;
	border-bottom: 1px solid grey;
	box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}

input[type=range]: :-moz-range-track{
	background: #000;
	border-right: 1px solid grey;
	border-bottom: 1px solid grey;
	box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}

input[type=range].has-box-indicator: :-webkit-slider-thumb{
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}

input[type=range].has-box-indicator: :-moz-range-thumb{
	background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}

.is-vertical{
	display: inline-block;
	width: 4px;
	height: 150px;
	transform: translateY(50%)
}

.is-vertical>input[type=range]{
	width: 150px;
	height: 4px;
	margin: 0 16px 0 10px;
	transform-origin: left;
	transform: rotate(270deg) translateX(calc(-50% + 8px))
}

.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
	border-left: 1px solid grey;
	border-bottom: 1px solid grey;
	box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}

.is-vertical>input[type=range]: :-moz-range-track{
	border-left: 1px solid grey;
	border-bottom: 1px solid grey;
	box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}

.is-vertical>input[type=range]: :-webkit-slider-thumb{
	transform: translateY(-8px) scaleX(-1)
}

.is-vertical>input[type=range]: :-moz-range-thumb{
	transform: translateY(2px) scaleX(-1)
}

.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
	transform: translateY(-10px) scaleX(-1)
}

.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
	transform: translateY(0) scaleX(-1)
}

.window{
	font-size: 11px;
	box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
	background: #ece9d8;
	padding: 3px
}

.window fieldset{
	margin-bottom: 9px
}

.title-bar{
	background: #000;
	padding: 3px 2px 3px 3px;
	display: flex;
	justify-content: space-between;
	align-items: center
}

.title-bar-text{
	font-weight: 700;
	color: #fff;
	letter-spacing: 0;
	margin-right: 24px
}

.title-bar-controls button{
	padding: 0;
	display: block;
	min-width: 16px;
	min-height: 14px
}

.title-bar-controls button: focus{
	outline: none
}

.window-body{
	margin: 8px
}

.window-body pre{
	margin: -8px
}

.status-bar{
	margin: 0 1px;
	display: flex;
	gap: 1px
}

.status-bar-field{
	box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
	flex-grow: 1;
	padding: 2px 3px;
	margin: 0
}

ul.tree-view{
	display: block;
	background: #fff;
	padding: 6px;
	margin: 0
}

ul.tree-view li{
	list-style-type: none;
	margin-top: 3px
}

ul.tree-view a{
	text-decoration: none;
	color: #000
}

ul.tree-view a: focus{
	background-color: #2267cb;
	color: #fff
}

ul.tree-view ul{
	margin-top: 3px;
	margin-left: 16px;
	padding-left: 16px;
	border-left: 1px dotted grey
}

ul.tree-view ul>li{
	position: relative
}

ul.tree-view ul>li: before{
	content: "";
	display: block;
	position: absolute;
	left: -16px;
	top: 6px;
	width: 12px;
	border-bottom: 1px dotted grey
}

ul.tree-view ul>li: last-child: after{
	content: "";
	display: block;
	position: absolute;
	left: -20px;
	top: 7px;
	bottom: 0;
	width: 8px;
	background: #fff
}

ul.tree-view ul details>summary: before{
	margin-left: -22px;
	position: relative;
	z-index: 1
}

ul.tree-view details{
	margin-top: 0
}

ul.tree-view details>summary: before{
	text-align: center;
	display: block;
	float: left;
	content: "+";
	border: 1px solid grey;
	width: 8px;
	height: 9px;
	line-height: 9px;
	margin-right: 5px;
	padding-left: 1px;
	background-color: #fff
}

ul.tree-view details[open] summary{
	margin-bottom: 0
}

ul.tree-view details[open]>summary: before{
	content: "-"
}

fieldset{
	border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
	padding: 10px;
	padding-block-start: 8px;
	margin: 0
}

legend{
	background: #ece9d8
}

menu[role=tablist]{
	position: relative;
	margin: 0 0 -2px;
	text-indent: 0;
	list-style-type: none;
	display: flex;
	padding-left: 3px
}

menu[role=tablist] button{
	z-index: 1;
	display: block;
	color: #222;
	text-decoration: none;
	min-width: unset
}

menu[role=tablist] button[aria-selected=true]{
	padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}

menu[role=tablist] button: focus{
	outline: 1px dotted #222;outline-offset: -4px
}

menu[role=tablist].justified button{
	flex-grow: 1;text-align: center
}

[role=tabpanel]{
	padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}

: :-webkit-scrollbar{
	width: 17px
}

: :-webkit-scrollbar-corner{
	background: #dfdfdf
}

: :-webkit-scrollbar-track: vertical{
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}

: :-webkit-scrollbar-track: horizontal{
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}

: :-webkit-scrollbar-thumb{
	background-position: 50%;
	background-repeat: no-repeat;
	background-color: #c8d6fb;
	background-size: 7px;
	border: 1px solid #fff;
	border-radius: 2px;
	box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}

: :-webkit-scrollbar-thumb: vertical{
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}

: :-webkit-scrollbar-thumb: horizontal{
	background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}

: :-webkit-scrollbar-button: vertical: start{
	height: 17px;
	background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 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: :-webkit-scrollbar-button: vertical: end{
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}

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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: "";
	position: absolute;
	top: 1px;
	left: 2px;
	width: 100%;
	height: calc(100% - 2px);
	padding: 1px 2px;
	border-radius: 2px;
	background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}

progress: not([value]): after,progress: not([value]): after: not([value]){
	animation: sliding 2s linear 0s infinite
}

progress: not([value]): after: not([value]){
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        15.1
    ],
    [
        1746728409,
        1063.4921875,
        23.6
    ],
    [
        1746728410,
        1063.4921875,
        24
    ],
    [
        1746728410,
        1063.4921875,
        19.6
    ]
];
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 = [], reload = false) {
	try {
		if ($("#parallel-plot").data("loaded") === "true" && !reload) {
			return;
		}

		// Filter rows ohne leere Werte (wie in deinem Originalcode)
		dataArray = filterNonEmptyRows(dataArray);

		const ignoreSet = new Set(ignoreColumns);
		const numericalCols = [];
		const categoricalCols = [];
		const categoryMappings = {};
		const enable_slurm_id_if_exists = $("#enable_slurm_id_if_exists").is(":checked");

		// Spalten einteilen in numerisch oder kategorisch + category mappings aufbauen
		headers.forEach((header, colIndex) => {
			if (ignoreSet.has(header)) return;
			if (!enable_slurm_id_if_exists && header === "OO_Info_SLURM_JOB_ID") 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]));
			}
		});

		// Erzeuge UI für Checkboxen und Min/Max Inputs für numerische Spalten
		const controlContainerId = "parallel-plot-controls";
		let controlContainer = $("#" + controlContainerId);
		if (controlContainer.length === 0) {
			controlContainer = $('<div id="' + controlContainerId + '" style="margin-bottom:10px; display: flex;"></div>');
			$("#parallel-plot").before(controlContainer);
		} else {
			controlContainer.empty();
		}

		// Map um Checkbox-Zustände und Min/Max-Werte zu speichern
		const columnVisibility = {};
		const minMaxLimits = {};

		// Checkboxen + Min/Max Felder generieren mit Boxen, max-Breite, Umbruch und Zeilenumbruch nach jeder Box
		headers.forEach((header) => {
			try {
				if (ignoreSet.has(header)) return;
				if (!enable_slurm_id_if_exists && header === "OO_Info_SLURM_JOB_ID") return;

				const isNumerical = numericalCols.some(col => col.name === header);

				const checkboxId = `chk_${header}`;
				const minInputId = `min_${header}`;
				const maxInputId = `max_${header}`;

				columnVisibility[header] = true;
				minMaxLimits[header] = { min: null, max: null };

				// Wrapper Box mit max-Breite, Umbruch, Block-Level-Element für newline nach jeder Box
				const boxWrapper = $('<div></div>').css({
					border: "1px solid #ddd",
					borderRadius: "8px",
					padding: "12px 16px",
					marginBottom: "12px",
					boxShadow: "0 2px 6px rgba(0,0,0,0.1)",
					backgroundColor: "#fff",
					display: "flex",
					flexWrap: "wrap",
					alignItems: "center",
					gap: "15px",
					maxWidth: "350px",
					width: "100%", // damit bei kleinen Screens die Box maximal voll breit ist
					boxSizing: "border-box"
				});

				// Innerer Container mit Flexbox für Ausrichtung der Elemente, flex-grow damit Inputs genug Platz bekommen
				const container = $('<div></div>').css({
					display: "flex",
					alignItems: "center",
					gap: "10px",
					flexWrap: "wrap",
					flexGrow: 1,
					minWidth: "0" // wichtig für flexbox Overflow Handling
				});

				// Checkbox mit Label
				const checkbox = $(`<input type="checkbox" id="${checkboxId}" checked />`);
				const label = $(`<label for="${checkboxId}" style="font-weight: 600; min-width: 140px; cursor: pointer; white-space: nowrap;">${header}</label>`);

				container.append(checkbox).append(label);

				if (isNumerical) {
					// Werte ermitteln (nur gültige Zahlen)
					const numericValues = dataArray
						.map(row => parseFloat(row[headers.indexOf(header)]))
						.filter(val => !isNaN(val));

					const minVal = numericValues.length > 0 ? Math.min(...numericValues) : 0;
					const maxVal = numericValues.length > 0 ? Math.max(...numericValues) : 100;

					// Min Input mit Label
					const minWrapper = $('<div></div>').css({
						display: "flex",
						flexDirection: "column",
						alignItems: "flex-start",
						minWidth: "90px"
					});
					const minLabel = $('<label></label>').attr("for", minInputId).text("Min").css({
						fontSize: "0.75rem",
						color: "#555",
						marginBottom: "2px"
					});
					const minInput = $(`<input type="number" id="${minInputId}" placeholder="min" />`).css({
						width: "80px",
						padding: "5px 8px",
						borderRadius: "5px",
						border: "1px solid #ccc",
						boxShadow: "inset 0 1px 3px rgba(0,0,0,0.1)",
						transition: "border-color 0.3s ease"
					});
					minInput.attr("min", minVal);
					minInput.attr("max", maxVal);

					minInput.on("focus", function () {
						$(this).css("border-color", "#007BFF");
					});
					minInput.on("blur", function () {
						$(this).css("border-color", "#ccc");
					});

					minWrapper.append(minLabel).append(minInput);

					// Max Input mit Label
					const maxWrapper = $('<div></div>').css({
						display: "flex",
						flexDirection: "column",
						alignItems: "flex-start",
						minWidth: "90px"
					});
					const maxLabel = $('<label></label>').attr("for", maxInputId).text("Max").css({
						fontSize: "0.75rem",
						color: "#555",
						marginBottom: "2px"
					});
					const maxInput = $(`<input type="number" id="${maxInputId}" placeholder="max" />`).css({
						width: "80px",
						padding: "5px 8px",
						borderRadius: "5px",
						border: "1px solid #ccc",
						boxShadow: "inset 0 1px 3px rgba(0,0,0,0.1)",
						transition: "border-color 0.3s ease"
					});
					maxInput.attr("min", minVal);
					maxInput.attr("max", maxVal);

					maxInput.on("focus", function () {
						$(this).css("border-color", "#007BFF");
					});
					maxInput.on("blur", function () {
						$(this).css("border-color", "#ccc");
					});

					maxWrapper.append(maxLabel).append(maxInput);

					// Events für min/max Eingaben
					minInput.on("input", function () {
						const val = parseFloat($(this).val());
						minMaxLimits[header].min = isNaN(val) ? null : val;
						updatePlot();
					});

					maxInput.on("input", function () {
						const val = parseFloat($(this).val());
						minMaxLimits[header].max = isNaN(val) ? null : val;
						updatePlot();
					});

					container.append(minWrapper).append(maxWrapper);
				}

				// Checkbox Change Event
				checkbox.on("change", function () {
					columnVisibility[header] = $(this).is(":checked");
					updatePlot();
				});

				boxWrapper.append(container);

				// Jede Box bekommt ihren eigenen Block (also newline)
				controlContainer.append(boxWrapper);
			} catch (error) {
				console.error(`Fehler bei Header '${header}':`, error);
			}
		});



		// Erzeuge Ergebnis-Auswahl für Farbskala (color by result)
		const resultSelectId = "result-select";
		let resultSelect = $(`#${resultSelectId}`);
		if (resultSelect.length === 0) {
			resultSelect = $(`<select id="${resultSelectId}"></select>`);
			controlContainer.before(resultSelect);
		} else {
			resultSelect.empty();
		}

		resultSelect.append('<option value="none">No color</option>');

		for (let i = 0; i < resultNames.length; i++) {
			let minMaxInfo = "min [auto]";
			if (typeof result_min_max !== "undefined" && result_min_max[i] !== undefined) {
				minMaxInfo = result_min_max[i];
			}
			resultSelect.append(`<option value="${resultNames[i]}">${resultNames[i]} (${minMaxInfo})</option>`);
		}

		let colorValues = null;
		let colorScale = null;

		resultSelect.off("change").on("change", function () {
			const selectedResult = $(this).val();
			if (selectedResult === "none") {
				colorValues = null;
				colorScale = null;
			} else {
				const col = numericalCols.find(c => c.name.toLowerCase() === selectedResult.toLowerCase());
				if (!col) {
					colorValues = null;
					colorScale = null;
					updatePlot();
					return;
				}
				colorValues = dataArray.map(row => parseFloat(row[col.index]));

				let invertColor = false;
				if (typeof result_min_max !== "undefined") {
					const idx = resultNames.indexOf(selectedResult);
					if (idx !== -1) {
						invertColor = result_min_max[idx] === "max";
					}
				}

				colorScale = invertColor
					? [[0, 'red'], [1, 'green']]
					: [[0, 'green'], [1, 'red']];
			}
			updatePlot();
		});

		// Initial Auswahl: kein Farbwert, oder erstes Ergebnis falls nur eins
		if (resultNames.length === 1) {
			resultSelect.val(resultNames[0]).trigger("change");
		} else {
			resultSelect.val("none").trigger("change");
		}

		function updatePlot() {
			try {
				// Filter Spalten nach Checkboxen
				const filteredNumericalCols = numericalCols.filter(col => columnVisibility[col.name]);
				const filteredCategoricalCols = categoricalCols.filter(col => columnVisibility[col.name]);

				// Filtere die Datenzeilen, um nur die zu behalten, die innerhalb aller gesetzten Min/Max Limits liegen
				const filteredData = dataArray.filter(row => {
					for (let col of filteredNumericalCols) {
						const val = parseFloat(row[col.index]);
						if (isNaN(val)) return false; // ungültiger Wert raus

						const limits = minMaxLimits[col.name];
						if (limits.min !== null && val < limits.min) return false;
						if (limits.max !== null && val > limits.max) return false;
					}
					// Kategorische Werte ignorieren Filter (könntest hier evtl. erweitern)
					return true;
				});

				const dimensions = [];

				// Füge numerische Dimensionen hinzu mit Min/Max Limits (Range anhand gefilterter Daten)
				filteredNumericalCols.forEach(col => {
					let vals = filteredData.map(row => parseFloat(row[col.index]));

					// Fallback falls alle Werte NaN (sollte eigentlich nicht vorkommen)
					const realMin = vals.length > 0 ? Math.min(...vals) : 0;
					const realMax = vals.length > 0 ? Math.max(...vals) : 100;

					dimensions.push({
						label: col.name,
						values: vals,
						range: [realMin, realMax]
					});
				});

				// Kategorische Dimensionen (aus gefilterten Daten)
				filteredCategoricalCols.forEach(col => {
					const vals = filteredData.map(row => categoryMappings[col.name][row[col.index]]);
					dimensions.push({
						label: col.name,
						values: vals,
						tickvals: Object.values(categoryMappings[col.name]),
						ticktext: Object.keys(categoryMappings[col.name])
					});
				});

				// Linienfarbe bestimmen, falls Farbskala gesetzt ist
				let filteredColorValues = null;
				if (colorValues) {
					// Da colorValues für alle Daten sind, filtere sie auch entsprechend
					filteredColorValues = filteredData.map(row => {
						const col = numericalCols.find(c => c.name.toLowerCase() === resultSelect.val().toLowerCase());
						return col ? parseFloat(row[col.index]) : null;
					});
				}

				const trace = {
					type: 'parcoords',
					dimensions: dimensions,
					line: filteredColorValues ? { color: filteredColorValues, 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();
			} catch (error) {
				console.error("Fehler in updatePlot():", error);
			}
		}


		updatePlot();

		$("#parallel-plot").data("loaded", "true");

		make_text_in_parallel_plot_nicer();

	} catch (err) {
		console.error("Error in createParallelPlot:", err);
	}
}

function plotWorkerUsage() {
	if($("#workerUsagePlot").data("loaded") == "true") {
		return;
	}
	var data = tab_worker_usage_csv_json;
	if (!Array.isArray(data) || data.length === 0) {
		console.error("Invalid or empty data provided.");
		return;
	}

	let timestamps = [];
	let desiredWorkers = [];
	let realWorkers = [];

	for (let i = 0; i < data.length; i++) {
		let entry = data[i];

		if (!Array.isArray(entry) || entry.length < 3) {
			console.warn("Skipping invalid entry:", entry);
			continue;
		}

		let unixTime = parseFloat(entry[0]);
		let desired = parseInt(entry[1], 10);
		let real = parseInt(entry[2], 10);

		if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
			console.warn("Skipping invalid numerical values:", entry);
			continue;
		}

		timestamps.push(new Date(unixTime * 1000).toISOString());
		desiredWorkers.push(desired);
		realWorkers.push(real);
	}

	let trace1 = {
		x: timestamps,
		y: desiredWorkers,
		mode: 'lines+markers',
		name: 'Desired Workers',
		line: {
			color: 'blue'
		}
	};

	let trace2 = {
		x: timestamps,
		y: realWorkers,
		mode: 'lines+markers',
		name: 'Real Workers',
		line: {
			color: 'red'
		}
	};

	let layout = {
		title: "Worker Usage Over Time",
		xaxis: {
			title: get_axis_title_data("Time", "date")
		},
		yaxis: {
			title: get_axis_title_data("Number of Workers")
		},
		legend: {
			x: 0,
			y: 1
		}
	};

	Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
	$("#workerUsagePlot").data("loaded", "true");
}

function plotCPUAndRAMUsage() {
	if($("#mainWorkerCPURAM").data("loaded") == "true") {
		return;
	}
	var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
	var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
	var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);

	var trace1 = {
		x: timestamps,
		y: cpuUsage,
		mode: 'lines+markers',
		marker: {
			size: get_marker_size(),
		},
		name: 'CPU Usage (%)',
		type: 'scatter',
		yaxis: 'y1'
	};

	var trace2 = {
		x: timestamps,
		y: ramUsage,
		mode: 'lines+markers',
		marker: {
			size: get_marker_size(),
		},
		name: 'RAM Usage (MB)',
		type: 'scatter',
		yaxis: 'y2'
	};

	var layout = {
		title: 'CPU and RAM Usage Over Time',
		xaxis: {
			title: get_axis_title_data("Timestamp", "date"),
			tickmode: 'array',
			tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
			ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
			tickangle: -45
		},
		yaxis: {
			title: get_axis_title_data("CPU Usage (%)"),
			rangemode: 'tozero'
		},
		yaxis2: {
			title: get_axis_title_data("RAM Usage (MB)"),
			overlaying: 'y',
			side: 'right',
			rangemode: 'tozero'
		},
		legend: {
			x: 0.1,
			y: 0.9
		}
	};

	var data = [trace1, trace2];
	Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
	$("#mainWorkerCPURAM").data("loaded", "true");
}

function plotScatter2d() {
	if ($("#plotScatter2d").data("loaded") == "true") {
		return;
	}

	var plotDiv = document.getElementById("plotScatter2d");

	var minInput = document.getElementById("minValue");
	var maxInput = document.getElementById("maxValue");

	if (!minInput || !maxInput) {
		minInput = document.createElement("input");
		minInput.id = "minValue";
		minInput.type = "number";
		minInput.placeholder = "Min Value";
		minInput.step = "any";

		maxInput = document.createElement("input");
		maxInput.id = "maxValue";
		maxInput.type = "number";
		maxInput.placeholder = "Max Value";
		maxInput.step = "any";

		var inputContainer = document.createElement("div");
		inputContainer.style.marginBottom = "10px";
		inputContainer.appendChild(minInput);
		inputContainer.appendChild(maxInput);
		plotDiv.appendChild(inputContainer);
	}

	var resultSelect = document.getElementById("resultSelect");
	if (result_names.length > 1 && !resultSelect) {
		resultSelect = document.createElement("select");
		resultSelect.id = "resultSelect";
		resultSelect.style.marginBottom = "10px";

		var sortedResults = [...result_names].sort();
		sortedResults.forEach(result => {
			var option = document.createElement("option");
			option.value = result;
			option.textContent = result;
			resultSelect.appendChild(option);
		});

		var selectContainer = document.createElement("div");
		selectContainer.style.marginBottom = "10px";
		selectContainer.appendChild(resultSelect);
		plotDiv.appendChild(selectContainer);
	}

	minInput.addEventListener("input", updatePlots);
	maxInput.addEventListener("input", updatePlots);
	if (resultSelect) {
		resultSelect.addEventListener("change", updatePlots);
	}

	updatePlots();

	async function updatePlots() {
		var minValue = parseFloat(minInput.value);
		var maxValue = parseFloat(maxInput.value);
		if (isNaN(minValue)) minValue = -Infinity;
		if (isNaN(maxValue)) maxValue = Infinity;

		while (plotDiv.children.length > 2) {
			plotDiv.removeChild(plotDiv.lastChild);
		}

		var selectedResult = resultSelect ? resultSelect.value : result_names[0];

		var resultIndex = tab_results_headers_json.findIndex(header =>
			header.toLowerCase() === selectedResult.toLowerCase()
		);
		var resultValues = tab_results_csv_json.map(row => row[resultIndex]);

		var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
		var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));

		if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
		if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);

		var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";

		var numericColumns = tab_results_headers_json.filter(col =>
			!special_col_names.includes(col) && !result_names.includes(col) &&
			!col.startsWith("OO_Info") &&
			tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
		);

		if (numericColumns.length < 2) {
			console.error("Not enough columns for Scatter-Plots");
			return;
		}

		for (let i = 0; i < numericColumns.length; i++) {
			for (let j = i + 1; j < numericColumns.length; j++) {
				let xCol = numericColumns[i];
				let yCol = numericColumns[j];

				let xIndex = tab_results_headers_json.indexOf(xCol);
				let yIndex = tab_results_headers_json.indexOf(yCol);

				let data = tab_results_csv_json.map(row => ({
					x: parseFloat(row[xIndex]),
					y: parseFloat(row[yIndex]),
					result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
				}));

				data = data.filter(d => d.result >= minResult && d.result <= maxResult);

				let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
				let layout = {
					title: layoutTitle,
					xaxis: {
						title: get_axis_title_data(xCol)
					},
					yaxis: {
						title: get_axis_title_data(yCol)
					},
					showlegend: false
				};
				let subDiv = document.createElement("div");

				let spinnerContainer = document.createElement("div");
				spinnerContainer.style.display = "flex";
				spinnerContainer.style.alignItems = "center";
				spinnerContainer.style.justifyContent = "center";
				spinnerContainer.style.width = layout.width + "px";
				spinnerContainer.style.height = layout.height + "px";
				spinnerContainer.style.position = "relative";

				let spinner = document.createElement("div");
				spinner.className = "spinner";
				spinner.style.width = "40px";
				spinner.style.height = "40px";

				let loadingText = document.createElement("span");
				loadingText.innerText = `Loading ${layoutTitle}`;
				loadingText.style.marginLeft = "10px";

				spinnerContainer.appendChild(spinner);
				spinnerContainer.appendChild(loadingText);

				plotDiv.appendChild(spinnerContainer);

				await new Promise(resolve => setTimeout(resolve, 50));

				let colors = data.map(d => {
					if (d.result === null) {
						return 'rgb(0, 0, 0)';
					} else {
						let norm = (d.result - minResult) / (maxResult - minResult);
						if (invertColor) {
							norm = 1 - norm;
						}
						return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
					}
				});

				let trace = {
					x: data.map(d => d.x),
					y: data.map(d => d.y),
					mode: 'markers',
					marker: {
						size: get_marker_size(),
						color: data.map(d => d.result !== null ? d.result : null),
						colorscale: invertColor ? [
							[0, 'red'],
							[1, 'green']
						] : [
							[0, 'green'],
							[1, 'red']
						],
						colorbar: {
							title: 'Result',
							tickvals: [minResult, maxResult],
							ticktext: [`${minResult}`, `${maxResult}`]
						},
						symbol: data.map(d => d.result === null ? 'x' : 'circle'),
					},
					text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
					type: 'scatter',
					showlegend: false
				};

				try {
					plotDiv.replaceChild(subDiv, spinnerContainer);
				} catch (err) {
					//
				}
				Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
			}
		}
	}

	$("#plotScatter2d").data("loaded", "true");
}

function plotScatter3d() {
	if ($("#plotScatter3d").data("loaded") == "true") {
		return;
	}

	var plotDiv = document.getElementById("plotScatter3d");
	if (!plotDiv) {
		console.error("Div element with id 'plotScatter3d' not found");
		return;
	}
	plotDiv.innerHTML = "";

	var minInput3d = document.getElementById("minValue3d");
	var maxInput3d = document.getElementById("maxValue3d");

	if (!minInput3d || !maxInput3d) {
		minInput3d = document.createElement("input");
		minInput3d.id = "minValue3d";
		minInput3d.type = "number";
		minInput3d.placeholder = "Min Value";
		minInput3d.step = "any";

		maxInput3d = document.createElement("input");
		maxInput3d.id = "maxValue3d";
		maxInput3d.type = "number";
		maxInput3d.placeholder = "Max Value";
		maxInput3d.step = "any";

		var inputContainer3d = document.createElement("div");
		inputContainer3d.style.marginBottom = "10px";
		inputContainer3d.appendChild(minInput3d);
		inputContainer3d.appendChild(maxInput3d);
		plotDiv.appendChild(inputContainer3d);
	}

	var select3d = document.getElementById("select3dScatter");
	if (result_names.length > 1 && !select3d) {
		if (!select3d) {
			select3d = document.createElement("select");
			select3d.id = "select3dScatter";
			select3d.style.marginBottom = "10px";
			select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");

			select3d.addEventListener("change", updatePlots3d);

			plotDiv.appendChild(select3d);
		}
	}

	minInput3d.addEventListener("input", updatePlots3d);
	maxInput3d.addEventListener("input", updatePlots3d);

	updatePlots3d();

	async function updatePlots3d() {
		var selectedResult = select3d ? select3d.value : result_names[0];
		var minValue3d = parseFloat(minInput3d.value);
		var maxValue3d = parseFloat(maxInput3d.value);

		if (isNaN(minValue3d)) minValue3d = -Infinity;
		if (isNaN(maxValue3d)) maxValue3d = Infinity;

		while (plotDiv.children.length > 2) {
			plotDiv.removeChild(plotDiv.lastChild);
		}

		var resultIndex = tab_results_headers_json.findIndex(header =>
			header.toLowerCase() === selectedResult.toLowerCase()
		);
		var resultValues = tab_results_csv_json.map(row => row[resultIndex]);

		var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
		var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));

		if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
		if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);

		var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";

		var numericColumns = tab_results_headers_json.filter(col =>
			!special_col_names.includes(col) && !result_names.includes(col) &&
			!col.startsWith("OO_Info") &&
			tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
		);

		if (numericColumns.length < 3) {
			console.error("Not enough columns for 3D scatter plots");
			return;
		}

		for (let i = 0; i < numericColumns.length; i++) {
			for (let j = i + 1; j < numericColumns.length; j++) {
				for (let k = j + 1; k < numericColumns.length; k++) {
					let xCol = numericColumns[i];
					let yCol = numericColumns[j];
					let zCol = numericColumns[k];

					let xIndex = tab_results_headers_json.indexOf(xCol);
					let yIndex = tab_results_headers_json.indexOf(yCol);
					let zIndex = tab_results_headers_json.indexOf(zCol);

					let data = tab_results_csv_json.map(row => ({
						x: parseFloat(row[xIndex]),
						y: parseFloat(row[yIndex]),
						z: parseFloat(row[zIndex]),
						result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
					}));

					data = data.filter(d => d.result >= minResult && d.result <= maxResult);

					let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
					let layout = {
						title: layoutTitle,
						scene: {
							xaxis: {
								title: get_axis_title_data(xCol)
							},
							yaxis: {
								title: get_axis_title_data(yCol)
							},
							zaxis: {
								title: get_axis_title_data(zCol)
							}
						},
						showlegend: false
					};

					let spinnerContainer = document.createElement("div");
					spinnerContainer.style.display = "flex";
					spinnerContainer.style.alignItems = "center";
					spinnerContainer.style.justifyContent = "center";
					spinnerContainer.style.width = layout.width + "px";
					spinnerContainer.style.height = layout.height + "px";
					spinnerContainer.style.position = "relative";

					let spinner = document.createElement("div");
					spinner.className = "spinner";
					spinner.style.width = "40px";
					spinner.style.height = "40px";

					let loadingText = document.createElement("span");
					loadingText.innerText = `Loading ${layoutTitle}`;
					loadingText.style.marginLeft = "10px";

					spinnerContainer.appendChild(spinner);
					spinnerContainer.appendChild(loadingText);

					plotDiv.appendChild(spinnerContainer);

					await new Promise(resolve => setTimeout(resolve, 50));

					let colors = data.map(d => {
						if (d.result === null) {
							return 'rgb(0, 0, 0)';
						} else {
							let norm = (d.result - minResult) / (maxResult - minResult);
							if (invertColor) {
								norm = 1 - norm;
							}
							return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
						}
					});

					let trace = {
						x: data.map(d => d.x),
						y: data.map(d => d.y),
						z: data.map(d => d.z),
						mode: 'markers',
						marker: {
							size: get_marker_size(),
							color: data.map(d => d.result !== null ? d.result : null),
							colorscale: invertColor ? [
								[0, 'red'],
								[1, 'green']
							] : [
								[0, 'green'],
								[1, 'red']
							],
							colorbar: {
								title: 'Result',
								tickvals: [minResult, maxResult],
								ticktext: [`${minResult}`, `${maxResult}`]
							},
						},
						text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
						type: 'scatter3d',
						showlegend: false
					};

					let subDiv = document.createElement("div");
					try {
						plotDiv.replaceChild(subDiv, spinnerContainer);
					} catch (err) {
						//
					}
					Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
				}
			}
		}
	}

	$("#plotScatter3d").data("loaded", "true");
}

async function plot_worker_cpu_ram() {
	if($("#worker_cpu_ram_pre").data("loaded") == "true") {
		return;
	}

	const logData = $("#worker_cpu_ram_pre").text();
	const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;

	const hostData = {};

	logData.split("\n").forEach(line => {
		line = line.trim();
		const match = line.match(regex);
		if (match) {
			const timestamp = new Date(parseInt(match[1]) * 1000);
			const hostname = match[2];
			const cpu = parseFloat(match[3]);
			const ram = parseFloat(match[4]);

			if (!hostData[hostname]) {
				hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
			}

			hostData[hostname].timestamps.push(timestamp);
			hostData[hostname].cpuUsage.push(cpu);
			hostData[hostname].ramUsage.push(ram);
		}
	});

	if (!Object.keys(hostData).length) {
		console.log("No valid data found");
		return;
	}

	const container = document.getElementById("cpuRamWorkerChartContainer");
	container.innerHTML = "";

	var i = 1;

	Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
		const chartId = `workerChart_${index}`;
		const chartDiv = document.createElement("div");
		chartDiv.id = chartId;
		chartDiv.style.marginBottom = "40px";
		container.appendChild(chartDiv);

		const cpuTrace = {
			x: timestamps,
			y: cpuUsage,
			mode: "lines+markers",
			name: "CPU Usage (%)",
			yaxis: "y1",
			line: {
				color: "red"
			}
		};

		const ramTrace = {
			x: timestamps,
			y: ramUsage,
			mode: "lines+markers",
			name: "RAM Usage (MB)",
			yaxis: "y2",
			line: {
				color: "blue"
			}
		};

		const layout = {
			title: `Worker CPU and RAM Usage - ${hostname}`,
			xaxis: {
				title: get_axis_title_data("Timestamp", "date")
			},
			yaxis: {
				title: get_axis_title_data("CPU Usage (%)"),
				side: "left",
				color: "red"
			},
			yaxis2: {
				title: get_axis_title_data("RAM Usage (MB)"),
				side: "right",
				overlaying: "y",
				color: "blue"
			},
			showlegend: true
		};

		Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
		i++;
	});

	$("#plot_worker_cpu_ram_button").remove();
	$("#worker_cpu_ram_pre").data("loaded", "true");
}

function load_log_file(log_nr, filename) {
	var pre_id = `single_run_${log_nr}_pre`;

	if (!$("#" + pre_id).data("loaded")) {
		const params = new URLSearchParams(window.location.search);

		const user_id = params.get('user_id');
		const experiment_name = params.get('experiment_name');
		const run_nr = params.get('run_nr');

		var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;

		fetch(url)
			.then(response => response.json())
			.then(data => {
				if (data.data) {
					$("#" + pre_id).html(data.data);
					$("#" + pre_id).data("loaded", true);
				} else {
					log(`No 'data' key found in response.`);
				}

				$("#spinner_log_" + log_nr).remove();
			})
			.catch(error => {
				log(`Error loading log: ${error}`);
				$("#spinner_log_" + log_nr).remove();
			});
	}
}

function load_debug_log () {
	var pre_id = `here_debuglogs_go`;

	if (!$("#" + pre_id).data("loaded")) {
		const params = new URLSearchParams(window.location.search);

		const user_id = params.get('user_id');
		const experiment_name = params.get('experiment_name');
		const run_nr = params.get('run_nr');

		var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;

		fetch(url)
			.then(response => response.json())
			.then(data => {
				$("#debug_log_spinner").remove();

				if (data.data) {
					try {
						$("#" + pre_id).html(data.data);
					} catch (err) {
						$("#" + pre_id).text(`Error loading data: ${err}`);
					}

					$("#" + pre_id).data("loaded", true);

					if (typeof apply_theme_based_on_system_preferences === 'function') {
						apply_theme_based_on_system_preferences();
					}
				} else {
					log(`No 'data' key found in response.`);
				}
			})
			.catch(error => {
				log(`Error loading log: ${error}`);
				$("#debug_log_spinner").remove();
			});
	}
}

function plotBoxplot() {
	if ($("#plotBoxplot").data("loaded") == "true") {
		return;
	}
	var numericColumns = tab_results_headers_json.filter(col =>
		!special_col_names.includes(col) && !result_names.includes(col) &&
		!col.startsWith("OO_Info") &&
		tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
	);

	if (numericColumns.length < 1) {
		console.error("Not enough numeric columns for Boxplot");
		return;
	}

	var resultIndex = tab_results_headers_json.findIndex(function(header) {
		return result_names.includes(header.toLowerCase());
	});
	var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
	var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
	var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));

	var plotDiv = document.getElementById("plotBoxplot");
	plotDiv.innerHTML = "";

	let traces = numericColumns.map(col => {
		let index = tab_results_headers_json.indexOf(col);
		let data = tab_results_csv_json.map(row => parseFloat(row[index]));

		return {
			y: data,
			type: 'box',
			name: col,
			boxmean: 'sd',
			marker: {
				color: 'rgb(0, 255, 0)'
			},
		};
	});

	let layout = {
		title: 'Boxplot of Numerical Columns',
		xaxis: {
			title: get_axis_title_data("Columns")
		},
		yaxis: {
			title: get_axis_title_data("Value")
		},
		showlegend: false
	};

	Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
	$("#plotBoxplot").data("loaded", "true");
}

function plotHeatmap() {
	if ($("#plotHeatmap").data("loaded") === "true") {
		return;
	}

	var numericColumns = tab_results_headers_json.filter(col => {
		if (special_col_names.includes(col) || result_names.includes(col)) {
			return false;
		}
		if (!col.startsWith("OO_Info")) {
			return true;
		}
		let index = tab_results_headers_json.indexOf(col);
		return tab_results_csv_json.every(row => {
			let value = parseFloat(row[index]);
			return !isNaN(value) && isFinite(value);
		});
	});

	if (numericColumns.length < 2) {
		console.error("Not enough valid numeric columns for Heatmap");
		return;
	}

	var columnData = numericColumns.map(col => {
		let index = tab_results_headers_json.indexOf(col);
		return tab_results_csv_json.map(row => parseFloat(row[index]));
	});

	var dataMatrix = numericColumns.map((_, i) =>
		numericColumns.map((_, j) => {
			let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
			return values.reduce((a, b) => a + b, 0) / values.length;
		})
	);

	var trace = {
		z: dataMatrix,
		x: numericColumns,
		y: numericColumns,
		colorscale: 'Viridis',
		type: 'heatmap'
	};

	var layout = {
		xaxis: {
			title: get_axis_title_data("Columns")
		},
		yaxis: {
			title: get_axis_title_data("Columns")
		},
		showlegend: false
	};

	var plotDiv = document.getElementById("plotHeatmap");
	plotDiv.innerHTML = "";

	Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
	$("#plotHeatmap").data("loaded", "true");
}

function plotHistogram() {
	if ($("#plotHistogram").data("loaded") == "true") {
		return;
	}

	var numericColumns = tab_results_headers_json.filter(col =>
		!special_col_names.includes(col) && !result_names.includes(col) &&
		!col.startsWith("OO_Info") &&
		tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
	);

	if (numericColumns.length < 1) {
		console.error("Not enough columns for Histogram");
		return;
	}

	var plotDiv = document.getElementById("plotHistogram");
	plotDiv.innerHTML = "";

	const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];

	let traces = numericColumns.map((col, index) => {
		let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));

		return {
			x: data,
			type: 'histogram',
			name: col,
			opacity: 0.7,
			marker: {
				color: colorPalette[index % colorPalette.length]
			},
			autobinx: true
		};
	});

	let layout = {
		title: 'Histogram of Numerical Columns',
		xaxis: {
			title: get_axis_title_data("Value")
		},
		yaxis: {
			title: get_axis_title_data("Frequency")
		},
		showlegend: true,
		barmode: 'overlay'
	};

	Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
	$("#plotHistogram").data("loaded", "true");
}

function plotViolin() {
	if ($("#plotViolin").data("loaded") == "true") {
		return;
	}
	var numericColumns = tab_results_headers_json.filter(col =>
		!special_col_names.includes(col) && !result_names.includes(col) &&
		!col.startsWith("OO_Info") &&
		tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
	);

	if (numericColumns.length < 1) {
		console.error("Not enough columns for Violin Plot");
		return;
	}

	var plotDiv = document.getElementById("plotViolin");
	plotDiv.innerHTML = "";

	let traces = numericColumns.map(col => {
		let index = tab_results_headers_json.indexOf(col);
		let data = tab_results_csv_json.map(row => parseFloat(row[index]));

		return {
			y: data,
			type: 'violin',
			name: col,
			box: {
				visible: true
			},
			line: {
				color: 'rgb(0, 255, 0)'
			},
			marker: {
				color: 'rgb(0, 255, 0)'
			},
			meanline: {
				visible: true
			},
		};
	});

	let layout = {
		title: 'Violin Plot of Numerical Columns',
		yaxis: {
			title: get_axis_title_data("Value")
		},
		xaxis: {
			title: get_axis_title_data("Columns")
		},
		showlegend: false
	};

	Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
	$("#plotViolin").data("loaded", "true");
}

function plotExitCodesPieChart() {
	if ($("#plotExitCodesPieChart").data("loaded") == "true") {
		return;
	}

	var exitCodes = tab_results_csv_json.map(row => row[tab_results_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("CANDIDATE") ? "lightblue" :
			el.textContent.includes("ABANDONED") ? "yellow" : "";
		if (color) el.style.backgroundColor = color;
		el.classList.add("invert_in_dark_mode");
	});
}

function _colorize_table_entries_by_queue_time() {
	let cells = [...document.querySelectorAll('[data-column-id="queue_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_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_queue_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();
	if (typeof apply_theme_based_on_system_preferences === 'function') {
		apply_theme_based_on_system_preferences();
	}
}

function plotTimelineFromGlobals() {
	if (
		typeof tab_results_headers_json === "undefined" ||
		typeof tab_results_csv_json === "undefined" ||
		!Array.isArray(tab_results_headers_json) ||
		!Array.isArray(tab_results_csv_json)
	) {
		console.warn("Global variables 'tab_results_headers_json' or 'tab_results_csv_json' missing or invalid.");
		return null;
	}

	const headers = tab_results_headers_json;
	const data = tab_results_csv_json;

	const col = name => headers.indexOf(name);
	const ix_trial_index = col("trial_index");
	const ix_start_time = col("start_time");
	const ix_end_time = col("end_time");
	const ix_status = col("trial_status");

	if ([ix_trial_index, ix_start_time, ix_end_time, ix_status].some(ix => ix === -1)) {
		console.warn("One or more needed columns missing");
		return null;
	}

	const traces = [];

	// Add dummy traces for legend
	traces.push({
		type: "scatter",
		mode: "lines",
		x: [null, null],
		y: [null, null],
		line: { color: "green", width: 4 },
		name: "COMPLETED",
		showlegend: true,
		hoverinfo: "none"
	});
	traces.push({
		type: "scatter",
		mode: "lines",
		x: [null, null],
		y: [null, null],
		line: { color: "yellow", width: 4 },
		name: "RUNNING",
		showlegend: true,
		hoverinfo: "none"
	});
	traces.push({
		type: "scatter",
		mode: "lines",
		x: [null, null],
		y: [null, null],
		line: { color: "red", width: 4 },
		name: "FAILED/OTHER",
		showlegend: true,
		hoverinfo: "none"
	});

	for (const row of data) {
		const trial_index = row[ix_trial_index];
		const start = row[ix_start_time];
		const end = row[ix_end_time];
		const status = row[ix_status];

		if (
			trial_index === "" || start === "" || end === "" ||
			isNaN(start) || isNaN(end)
		) continue;

		let color = "red"; // default
		if (status === "COMPLETED") color = "green";
		else if (status === "RUNNING") color = "yellow";

		traces.push({
			type: "scatter",
			mode: "lines",
			x: [new Date(start * 1000), new Date(end * 1000)],
			y: [trial_index, trial_index],
			line: { color: color, width: 4 },
			name: `Trial ${trial_index} (${status})`,
			showlegend: false,
			hoverinfo: "x+y+name"
		});
	}

	if (traces.length <= 3) { // only dummy traces added
		console.warn("No valid data for plotting found.");
		return null;
	}

	const layout = {
		title: "Trial Timeline",
		xaxis: {
			title: "Time",
			type: "date"
		},
		yaxis: {
			title: "Trial Index",
			autorange: "reversed"
		},
		margin: { t: 50 }
	};

	Plotly.newPlot('plot_timeline', traces, add_default_layout_data(layout));
	return true;
}

function createResultParameterCanvases(this_res_name) {
	if (
		typeof special_col_names === "undefined" ||
		typeof result_names === "undefined" ||
		typeof result_min_max === "undefined" ||
		typeof tab_results_headers_json === "undefined" ||
		typeof tab_results_csv_json === "undefined"
	) {
		console.error("Missing one or more required global variables.");
		return null;
	}

	if (
		!Array.isArray(special_col_names) ||
		!Array.isArray(result_names) ||
		!Array.isArray(result_min_max) ||
		!Array.isArray(tab_results_headers_json) ||
		!Array.isArray(tab_results_csv_json)
	) {
		console.error("All inputs must be arrays.");
		return null;
	}

	function getColumnIndexMap(headers) {
		var map = {};
		for (var i = 0; i < headers.length; i++) {
			map[headers[i]] = i;
		}
		return map;
	}

	function getColumnData(data, index) {
		var result = [];
		for (var i = 0; i < data.length; i++) {
			result.push(data[i][index]);
		}
		return result;
	}

	function normalize(value, min, max) {
		if (max === min) {
			return 0.5;
		}
		return (value - min) / (max - min);
	}

	function interpolateColor(ratio, reverse) {
		var r = reverse ? ratio : 1 - ratio;
		var g = reverse ? 1 - ratio : ratio;
		var b = 0;
		r = Math.floor(r * 255);
		g = Math.floor(g * 255);
		return "rgb(" + r + "," + g + "," + b + ")";
	}

	function createCanvas(width, height) {
		var canvas = document.createElement("canvas");
		canvas.width = width;
		canvas.height = height;
		return canvas;
	}

	function isNumericArray(arr) {
		for (var i = 0; i < arr.length; i++) {
			var val = arr[i];
			if (typeof val !== "number" || isNaN(val)) {
				return false;
			}
		}
		return true;
	}

	function findBestRowIndex() {
		var bestIndex = 0;

		for (var i = 1; i < tab_results_csv_json.length; i++) {
			var better = false;

			for (var r = 0; r < result_names.length; r++) {
				var col = result_names[r];
				var colIdx = header_map[col];
				var goal = result_min_max[r]; // "min" or "max"

				var valCurrent = tab_results_csv_json[i][colIdx];
				var valBest = tab_results_csv_json[bestIndex][colIdx];

				if (goal === "min" && valCurrent < valBest) {
					better = true;
					break;
				}

				if (goal === "max" && valCurrent > valBest) {
					better = true;
					break;
				}
			}

			if (better) {
				bestIndex = i;
			}
		}

		return bestIndex;
	}

	var canvas_width = 1000;
	var canvas_height = 100;

	var header_map = getColumnIndexMap(tab_results_headers_json);

	var parameter_columns = tab_results_headers_json.filter(function (name) {
		return (
			!special_col_names.includes(name) &&
			!result_names.includes(name) &&
			!name.startsWith("OO_Info_")
		);
	});

	var container = document.createElement("div");

	for (var r = 0; r < result_names.length; r++) {
		var result_name = result_names[r];
		if (this_res_name == result_name) {
			var result_index = header_map[result_name];
			var result_goal = result_min_max[r]; // "min" or "max"

			var result_values = getColumnData(tab_results_csv_json, result_index);
			var result_min = Math.min.apply(null, result_values);
			var result_max = Math.max.apply(null, result_values);

			var heading = document.createElement("h2");
			heading.textContent = "Interpretation for result: " + result_name + " (goal: " + result_goal + ")";
			heading.style.fontFamily = "sans-serif";
			heading.style.marginTop = "24px";
			heading.style.marginBottom = "12px";
			container.appendChild(heading);

			var table = document.createElement("table");
			table.style.borderCollapse = "collapse";
			table.style.marginBottom = "32px";

			var thead = document.createElement("thead");
			var headRow = document.createElement("tr");

			var th1 = document.createElement("th");
			th1.textContent = "Parameter";
			th1.style.textAlign = "left";
			th1.style.padding = "6px 12px";
			var th2 = document.createElement("th");
			th2.textContent = "Distribution of result";
			th2.style.textAlign = "left";
			th2.style.padding = "6px 12px";

			headRow.appendChild(th1);
			headRow.appendChild(th2);
			thead.appendChild(headRow);
			table.appendChild(thead);

			var tbody = document.createElement("tbody");

			for (var p = 0; p < parameter_columns.length; p++) {
				var param_name = parameter_columns[p];
				var param_index = header_map[param_name];
				var param_values = getColumnData(tab_results_csv_json, param_index);

				if (!isNumericArray(param_values)) {
					continue;
				}

				var param_min = Math.min.apply(null, param_values);
				var param_max = Math.max.apply(null, param_values);

				var canvas = createCanvas(canvas_width, canvas_height);

				canvas.classList.add("invert_in_dark_mode");

				var ctx = canvas.getContext("2d");

				ctx.fillStyle = "white";
				ctx.fillRect(0, 0, canvas.width, canvas.height);

				var x_groups = {};

				for (var i = 0; i < tab_results_csv_json.length; i++) {
					var raw_param = tab_results_csv_json[i][param_index];
					var raw_result = tab_results_csv_json[i][result_index];

					var x_ratio = normalize(raw_param, param_min, param_max);
					var x = Math.floor(x_ratio * (canvas_width - 1));

					if (!x_groups[x]) {
						x_groups[x] = [];
					}

					x_groups[x].push(raw_result);
				}

				for (var x in x_groups) {
					var values = x_groups[x];
					values.sort(function (a, b) {
						return a - b;
					});

					var stripe_height = canvas_height / values.length;
					for (var i = 0; i < values.length; i++) {
						var y_start = i * stripe_height;
						var y_end = (i + 1) * stripe_height;

						var value = values[i];
						var result_ratio = normalize(value, result_min, result_max);
						var color = interpolateColor(result_ratio, result_goal === "min");

						ctx.beginPath();
						ctx.strokeStyle = color;
						ctx.lineWidth = 1;
						ctx.moveTo(Number(x) + 0.5, y_start);
						ctx.lineTo(Number(x) + 0.5, y_end);
						ctx.stroke();
					}
				}

				var row = document.createElement("tr");

				var cell_param = document.createElement("td");
				cell_param.textContent = param_name;
				cell_param.style.padding = "4px 12px";
				cell_param.style.verticalAlign = "top";
				cell_param.style.fontFamily = "monospace";
				cell_param.style.whiteSpace = "nowrap";

				var cell_canvas = document.createElement("td");
				cell_canvas.appendChild(canvas);
				cell_canvas.style.padding = "4px 12px";

				row.appendChild(cell_param);
				row.appendChild(cell_canvas);
				tbody.appendChild(row);
			}

			table.appendChild(tbody);
			container.appendChild(table);
		}
	}

	// === Summary: Best result ===
	var bestIndex = findBestRowIndex();
	var bestRow = tab_results_csv_json[bestIndex];

	var ul = document.createElement("ul");
	ul.style.margin = "0";
	ul.style.paddingLeft = "24px";

	// Alle Result-Spalten
	for (var i = 0; i < result_names.length; i++) {
		var name = result_names[i];
		var val = bestRow[header_map[name]];
		var li = document.createElement("li");
		li.textContent = name + " = " + val;
		ul.appendChild(li);
	}

	// Alle Parameter-Spalten (außer special_col_names)
	for (var i = 0; i < tab_results_headers_json.length; i++) {
		var name = tab_results_headers_json[i];
		if (special_col_names.includes(name) || name.startsWith("OO_Info_") || result_names.includes(name)) {
			continue;
		}

		var val = bestRow[header_map[name]];
		var li = document.createElement("li");
		li.textContent = name + " = " + val;
		ul.appendChild(li);
	}

	return container;
}

function initializeResultParameterVisualizations() {
        try {
                var elements = $('.result_parameter_visualization');

                if (!elements || elements.length === 0) {
                        console.warn('No .result_parameter_visualization elements found.');
                        return;
                }

                elements.each(function () {
                        var element = $(this);

                        if (element.data('initialized')) {
                                return; // Already initialized, skip
                        }

                        var resname = element.attr('data-resname');

                        if (!resname) {
                                console.error('Missing data-resname attribute for element:', this);
                                return;
                        }

                        try {
                                var html = createResultParameterCanvases(resname);

                                element.html(html);
                                element.data('initialized', true);

                        } catch (err) {
                                console.error('Error while calling createResultParameterCanvases for resname:', resname, err);
                        }
                });
        } catch (outerErr) {
                console.error('Failed to initialize result parameter visualizations:', outerErr);
        }
}

function plotParameterDistributionsByStatus() {
	const container = document.getElementById('parameter_by_status_distribution');
	if (!container) {
		console.error("Kein Container mit id 'parameter_by_status_distribution' gefunden.");
		return null;
	}

	if ($(container).data("loaded") === "true") {
		return;
	}

	if (
		typeof special_col_names === "undefined" ||
		typeof result_names === "undefined" ||
		typeof result_min_max === "undefined" ||
		typeof tab_results_headers_json === "undefined" ||
		typeof tab_results_csv_json === "undefined"
	) {
		console.error("Missing one or more required global variables.");
		return null;
	}

	if (
		!Array.isArray(special_col_names) ||
		!Array.isArray(result_names) ||
		!Array.isArray(result_min_max) ||
		!Array.isArray(tab_results_headers_json) ||
		!Array.isArray(tab_results_csv_json)
	) {
		console.error("All inputs must be arrays.");
		return null;
	}

	container.innerHTML = "";

	const statusIndex = tab_results_headers_json.indexOf("trial_status");
	if (statusIndex < 0) {
		container.textContent = "Kein 'trial_status' in den Daten gefunden.";
		return null;
	}

	const trialStatuses = [...new Set(tab_results_csv_json.map(row => row[statusIndex]))].filter(s => s != null);
	const paramCols = tab_results_headers_json.filter(col =>
		!special_col_names.includes(col) &&
		!result_names.includes(col)
	);

	for (const param of paramCols) {
		const paramIndex = tab_results_headers_json.indexOf(param);
		if (paramIndex < 0) continue;

		const traces = [];

		trialStatuses.forEach((status) => {
			const filteredValues = tab_results_csv_json
				.filter(row => row[statusIndex] === status)
				.map(row => row[paramIndex])
				.filter(val => val !== "" && val != null && !isNaN(val))
				.map(Number);

			if (filteredValues.length > 1) {
				// Histogramm-Bins automatisch mit Plotly bestimmen lassen oder eigene
				// Hier: bins in 20 Stück
				const nbins = 20;
				traces.push({
					type: 'histogram',
					x: filteredValues,
					name: status,
					opacity: 0.6,
					xbingroup: 0,
					marker: {color: getColorForStatus(status)},
					nbinsx: nbins,
					// für Overlay-Stil:
					// histfunc: 'count', // default
					// autobinx: false,
					// xbins: {start: Math.min(...filteredValues), end: Math.max(...filteredValues), size: (Math.max(...filteredValues) - Math.min(...filteredValues)) / nbins}
				});
			}
		});

		if (traces.length > 0) {
			const h2 = document.createElement('h2');
			if(!param.startsWith("OO_Info_")) {
				h2.textContent = `Histogram: ${param}`;
				container.appendChild(h2);

				const plotDiv = document.createElement('div');
				plotDiv.style.marginBottom = '30px';
				container.appendChild(plotDiv);

				Plotly.newPlot(plotDiv, traces, {
					barmode: 'overlay',  // 'stack' oder 'overlay'
					xaxis: {
						title: { text: String(param) },  // Sicherstellen, dass es ein Textobjekt ist
						automargin: true,
						tickangle: -45,                  // Optional: bessere Lesbarkeit
						titlefont: { size: 16 }          // Optional: größerer Titel
					},
					yaxis: {
						title: { text: 'Count' },        // Titel explizit als Objekt angeben
						automargin: true,
						titlefont: { size: 16 }          // Optional: größerer Titel
					},
					margin: {
						l: 60,
						r: 30,
						t: 30,
						b: 80                            // genug Platz für x-Achsentitel
					},
					legend: {
						orientation: "h"
					}
				}, {
					responsive: true
				});

			}
		}
	}

	$(container).data("loaded", "true");

	// Color mapping (falls nicht global)
	function getColorForStatus(status) {
		const baseAlpha = 0.5;
		switch(status.toUpperCase()) {
			case 'FAILED':      return `rgba(214, 39, 40, ${baseAlpha})`;
			case 'COMPLETED':   return `rgba(44, 160, 44, ${baseAlpha})`;
			case 'ABANDONED':   return `rgba(255, 215, 0, ${baseAlpha})`;
			case 'RUNNING':     return `rgba(50, 50, 44, ${baseAlpha})`;
			default:
				const otherColors = [
					`rgba(31, 119, 180, ${baseAlpha})`,
					`rgba(255, 127, 14, ${baseAlpha})`,
					`rgba(148, 103, 189, ${baseAlpha})`,
					`rgba(140, 86, 75, ${baseAlpha})`,
					`rgba(227, 119, 194, ${baseAlpha})`,
					`rgba(127, 127, 127, ${baseAlpha})`,
					`rgba(188, 189, 34, ${baseAlpha})`,
					`rgba(23, 190, 207, ${baseAlpha})`
				];
				let hash = 0;
				for (let i = 0; i < status.length; i++) {
					hash = status.charCodeAt(i) + ((hash << 5) - hash);
				}
				const index = Math.abs(hash) % otherColors.length;
				return otherColors[index];
		}
	}

	resizePlotlyCharts();
}

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);

	if (typeof apply_theme_based_on_system_preferences === 'function') {
		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();;
plotParameterDistributionsByStatus();;
initializeResultParameterVisualizations();
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotScatter2d();;
plotScatter3d();
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();;
plotResultPairs();;
plotResultEvolution();

				colorize_table_entries();
			});
		</script>

		<h1><img class='invert_icon' src='i/overview.svg' style='height: 1em' />&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>50337 </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>337   </td></tr></tbody></table><h2>Job Summary per Generation Node</h2>
<table border='1' cellpadding='5' cellspacing='0'>
<thead><tr><th>Generation Node</th><th>Total</th><th>COMPLETED</th><th>FAILED</th><th>RUNNING</th></tr></thead>
<tbody>
<tr><td>SOBOL</td><td>30</td><td>30</td><td>0</td><td>0</td></tr>
<tr><td>BOTORCH_MODULAR</td><td>564</td><td>513</td><td>50</td><td>1</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>50</td>
<td>543</td>
<td>1</td>
<td>594</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><img class='invert_icon' src='i/csv.svg' style='height: 1em' />&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")'><img src='i/clipboard.svg' style='height: 1em'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'><img src='i/download.svg' style='height: 1em'> 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,0.839999999999999968913755310496,32,3891,212,0.103716966480016714591272375401,15,0.005000000000000000104083408559
1,1_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,2277,4468,0.634386613594368098389963961381,37,0.025000000000000001387778780781
2,2_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,1235,2316,0.965115121774375417373903474072,39,0.050000000000000002775557561563
3,3_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,2745,2988,0.310670194640755681358967876804,11,0.001000000000000000020816681712
4,4_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,3460,1845,0.596518199305981378088858946285,50,0.100000000000000005551115123126
5,5_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,52,3438,0.129221954811364420079300430189,3,0.001000000000000000020816681712
6,6_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,11,1429,668,0.460375034710392372350185041796,23,0.005000000000000000104083408559
7,7_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,4895,3990,0.803895777044817849699143152975,26,0.25
8,8_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,4652,1930,0.371448704075068247831836742989,30,0.050000000000000002775557561563
9,9_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,1829,2749,0.902141097862273477758776607516,20,0.25
10,10_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,4,434,598,0.695189909866079647215997283638,6,0.025000000000000001387778780781
11,11_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,3234,4707,0.040722013918682936628457014194,46,0.010000000000000000208166817117
12,12_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,2,2973,1064,0.864950343513861352739979793114,7,0.050000000000000002775557561563
13,13_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,851,4220,0.397616125004366049999759979983,42,0.010000000000000000208166817117
14,14_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,3,1880,1449,0.190236498735845083185580506324,34,0.025000000000000001387778780781
15,15_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,4131,3209,0.533795438155531876134318736149,19,0.001000000000000000020816681712
16,16_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,3,4264,1301,0.177475877372547996557727856271,17,0.100000000000000005551115123126
17,17_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,2070,3379,0.58341064958833155351669574884,33,0.050000000000000002775557561563
18,18_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,17,670,1228,0.750768450696021272250391120906,43,0.005000000000000000104083408559
19,19_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,2851,4077,0.470558161448687339856178368791,9,0.025000000000000001387778780781
20,20_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,6,3367,433,0.686630237326026016120295025758,45,0.005000000000000000104083408559
21,21_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,624,4849,0.094538412116467948620446293262,4,0.25
22,22_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,1648,2079,0.261349804179742906118377732128,22,0.050000000000000002775557561563
23,23_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,4529,2579,0.979166365170851316079847492801,31,0.025000000000000001387778780781
24,24_0,COMPLETED,Sobol,SOBOL,0.849999999999999977795539507497,6,4753,832,0.406863200083374954019177494047,28,0.005000000000000000104083408559
25,25_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,1269,3848,0.812760013155639171600341796875,24,0.001000000000000000020816681712
26,26_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,222,1696,0.519675673114135872054930587183,2,0.010000000000000000208166817117
27,27_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,3612,3609,0.239503595823422066324326351605,48,0.25
28,28_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,2603,2465,0.915195346770808049186030075361,12,0.001000000000000000020816681712
29,29_0,COMPLETED,Sobol,SOBOL,0.839999999999999968913755310496,1,1075,2818,0.323126252433285132692475372096,41,0.100000000000000005551115123126
30,30_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,2688,0.573162990296968599679416911385,46,0.005000000000000000104083408559
31,31_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2870,4110,0.556073404765987655551384705177,50,0.010000000000000000208166817117
32,32_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2845,4935,0.906541815405174200570570519631,50,0.010000000000000000208166817117
33,33_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,4995,0.57073838523848641202107501158,47,0.010000000000000000208166817117
34,34_0,FAILED,BoTorch,BOTORCH_MODULAR,,,5000,1,0.57464936505285502121864738001,50,0.010000000000000000208166817117
35,35_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,5000,0.001000000000000000020816681712,50,0.050000000000000002775557561563
36,36_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,209,3838,0.9989999999999999991118215803,50,0.010000000000000000208166817117
37,37_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2872,5000,0.972054561861824262258835460671,50,0.010000000000000000208166817117
38,38_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,3149,0.001000000000000000020816681712,50,0.010000000000000000208166817117
39,39_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3599,2742,0.537083834720633035431092139333,1,0.010000000000000000208166817117
40,40_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2281,5000,0.947756063327697906295554730605,1,0.25
41,41_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,3741,0.466445384064472945429002948003,1,0.25
42,42_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2417,4189,0.894571433990780762179895191366,50,0.025000000000000001387778780781
43,43_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1127,4972,0.917311251639741143115713839506,50,0.010000000000000000208166817117
44,44_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,5000,0.58238934057127145216270491801,1,0.100000000000000005551115123126
45,45_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,1,331,0.001000000000000000020816681712,50,0.050000000000000002775557561563
46,46_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,2728,331,0.530689831751712359775297045417,50,0.010000000000000000208166817117
47,47_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,4566,0.570605335825607018485072785552,44,0.100000000000000005551115123126
48,48_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,4251,0.571789811272821801857446644135,2,0.010000000000000000208166817117
49,49_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,1291,0.453964770663696892682992256596,50,0.010000000000000000208166817117
50,50_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,5000,0.9989999999999999991118215803,1,0.001000000000000000020816681712
51,51_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1962,3761,0.984695103050564846114411921008,50,0.100000000000000005551115123126
52,52_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2661,5000,0.885735343383033280062477388128,50,0.100000000000000005551115123126
53,53_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2880,5000,0.91883275534618258362229425984,50,0.25
54,54_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3096,4269,0.549818072276236247120095868013,36,0.25
55,55_0,FAILED,BoTorch,BOTORCH_MODULAR,,,5000,1,0.575400627976737744084800851851,1,0.100000000000000005551115123126
56,56_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2811,4925,0.568446080866958136645905597106,50,0.010000000000000000208166817117
57,57_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,622,0.454150450679343453685987697099,1,0.25
58,58_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2912,5000,0.969024897363672477190732479357,50,0.100000000000000005551115123126
59,59_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,4374,0.9989999999999999991118215803,50,0.010000000000000000208166817117
60,60_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4700,1724,0.614253475952709071528090589709,30,0.005000000000000000104083408559
61,61_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3494,1743,0.319999821744132051382081272095,29,0.005000000000000000104083408559
62,62_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1717,2925,0.613152142658230259186780131131,45,0.005000000000000000104083408559
63,63_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3646,2542,0.615075197233731496559983042971,10,0.005000000000000000104083408559
64,64_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2368,1423,0.9989999999999999991118215803,29,0.005000000000000000104083408559
65,65_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1596,2161,0.614177056400846854700148469419,50,0.005000000000000000104083408559
66,66_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3410,1891,0.614678851091536748718624494359,9,0.005000000000000000104083408559
67,67_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1817,2656,0.613990284457835211462395363924,30,0.001000000000000000020816681712
68,68_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,10,4506,628,0.414585999464086918386840352468,6,0.010000000000000000208166817117
69,69_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4409,2159,0.613365284704622149014596743655,1,0.005000000000000000104083408559
70,70_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,1777,2264,0.021030354295982975343370924293,1,0.001000000000000000020816681712
71,71_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2755,4371,0.32905667051413883816834982099,30,0.010000000000000000208166817117
72,72_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,63,1819,414,0.611911927423118950919445069303,11,0.001000000000000000020816681712
73,73_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3173,4291,0.614182318848568109537211512361,10,0.025000000000000001387778780781
74,74_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,39,2930,0.081592729743817926646443083882,10,0.010000000000000000208166817117
75,75_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.83000000000000007105427357601,5,888,426,0.614272439646334822427320432325,29,0.005000000000000000104083408559
76,76_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,582,4843,0.9989999999999999991118215803,30,0.005000000000000000104083408559
77,77_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3551,2696,0.615353334928089634736636526213,1,0.005000000000000000104083408559
78,78_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3446,3658,0.614052042602178160812798068946,1,0.005000000000000000104083408559
79,79_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3940,3625,0.295787181111993313642472003266,45,0.010000000000000000208166817117
80,80_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4717,1015,0.412379713415247872720925670365,1,0.100000000000000005551115123126
81,81_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1558,2906,0.613284270779349771629540555296,12,0.005000000000000000104083408559
82,82_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,959,1630,0.049941583288774092641570945261,4,0.001000000000000000020816681712
83,83_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2648,1624,0.614234149003148988121836282517,6,0.005000000000000000104083408559
84,84_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3516,2702,0.614692136146274825669877373002,1,0.010000000000000000208166817117
85,85_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,29,1704,105,0.986735105174975535291537198646,1,0.025000000000000001387778780781
86,86_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,18,3978,272,0.9989999999999999991118215803,12,0.005000000000000000104083408559
87,87_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.83000000000000007105427357601,4,873,536,0.613123137984936894540055618563,10,0.25
88,88_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4157,2473,0.303793815730602267954907347303,11,0.010000000000000000208166817117
89,89_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,11,3131,525,0.063311527499480324543412734783,10,0.010000000000000000208166817117
90,90_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4399,3509,0.651110780451143011937631399633,50,0.001000000000000000020816681712
91,91_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4822,947,0.001000000000000000020816681712,14,0.050000000000000002775557561563
92,92_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,106,4809,972,0.983066723734806524603868638224,11,0.001000000000000000020816681712
93,93_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,455,2989,0.9989999999999999991118215803,40,0.005000000000000000104083408559
94,94_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,9,4758,916,0.001000000000000000020816681712,10,0.001000000000000000020816681712
95,95_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3058,1700,0.445268952903557724276595308766,50,0.100000000000000005551115123126
96,96_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4775,1026,0.471112620674603077564057684867,35,0.100000000000000005551115123126
97,97_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4809,1125,0.466249438700039264826813223408,32,0.25
98,98_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3935,1935,0.43496717441032695106173378008,40,0.25
99,99_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,679,1945,0.86148334653088254331265716246,50,0.100000000000000005551115123126
100,100_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,606,1646,0.438777534960291148991018417291,50,0.25
101,101_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,4860,938,0.001000000000000000020816681712,1,0.100000000000000005551115123126
102,102_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,694,3452,0.654711675352049660148168186424,42,0.001000000000000000020816681712
103,103_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3883,4227,0.651802942685897779284687203472,26,0.001000000000000000020816681712
104,104_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2286,1,0.199259442182886548078357691338,45,0.001000000000000000020816681712
105,105_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4818,939,0.214036228614995993613234759323,34,0.100000000000000005551115123126
106,106_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,12,4868,935,0.9989999999999999991118215803,39,0.001000000000000000020816681712
107,107_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2241,1859,0.435207271034394715947257736843,50,0.25
108,108_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,496,2556,0.201406717248242883577802331274,42,0.100000000000000005551115123126
109,109_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2244,2665,0.9989999999999999991118215803,50,0.005000000000000000104083408559
110,110_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1633,4433,0.954707987558611304024225319154,20,0.001000000000000000020816681712
111,111_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,607,1594,0.001000000000000000020816681712,50,0.050000000000000002775557561563
112,112_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,4833,922,0.001000000000000000020816681712,1,0.100000000000000005551115123126
113,113_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4466,4986,0.778684533165632131179734187754,24,0.025000000000000001387778780781
114,114_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,6,4789,1193,0.464679343590766957827753458332,50,0.25
115,115_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3061,3392,0.200206783547276140344806094618,36,0.100000000000000005551115123126
116,116_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2438,3310,0.659083402297072074027539656527,1,0.001000000000000000020816681712
117,117_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3878,1585,0.001000000000000000020816681712,50,0.100000000000000005551115123126
118,118_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,660,1838,0.422650113139144245621992013184,41,0.25
119,119_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2975,2338,0.424976556946772943135215427901,11,0.25
120,120_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,201,4869,1011,0.885461881498733172080051190278,50,0.001000000000000000020816681712
121,121_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,67,4847,960,0.082359447569873220351688303253,46,0.001000000000000000020816681712
122,122_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4973,4286,0.9989999999999999991118215803,40,0.001000000000000000020816681712
123,123_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4787,2637,0.001000000000000000020816681712,3,0.050000000000000002775557561563
124,124_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,76,3571,846,0.9989999999999999991118215803,33,0.001000000000000000020816681712
125,125_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4737,2496,0.408627287430301355808381913448,1,0.100000000000000005551115123126
126,126_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,823,1077,0.001000000000000000020816681712,10,0.050000000000000002775557561563
127,127_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4954,3337,0.269072127009376715900401677573,1,0.010000000000000000208166817117
128,128_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,229,3703,0.9989999999999999991118215803,1,0.050000000000000002775557561563
129,129_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4292,1036,0.359804261461804997246360926511,37,0.010000000000000000208166817117
130,130_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,7,1947,1050,0.396218369450839102974271099811,1,0.010000000000000000208166817117
131,131_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,2828,1080,0.39772807188444514459035872278,1,0.25
132,132_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3336,3020,0.001000000000000000020816681712,1,0.025000000000000001387778780781
133,133_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.820000000000000062172489379009,6,1546,892,0.001000000000000000020816681712,17,0.050000000000000002775557561563
134,134_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,1900,1137,0.478402431740543587768144107031,1,0.100000000000000005551115123126
135,135_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,4,1882,1335,0.296168428881937229046883430783,1,0.25
136,136_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3488,3609,0.958575745317489125163490371051,1,0.001000000000000000020816681712
137,137_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4638,1075,0.348986267789871185129868536023,22,0.25
138,138_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3891,4051,0.772961004962520070726839094277,28,0.050000000000000002775557561563
139,139_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4440,4204,0.9989999999999999991118215803,1,0.010000000000000000208166817117
140,140_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,3294,0.576558445060058821773907311581,1,0.25
141,141_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1303,2151,0.9989999999999999991118215803,1,0.025000000000000001387778780781
142,142_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1329,2789,0.001000000000000000020816681712,1,0.005000000000000000104083408559
143,143_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,131,3413,0.750018308277681344797827023285,1,0.005000000000000000104083408559
144,144_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,5000,1460,0.001000000000000000020816681712,14,0.050000000000000002775557561563
145,145_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3745,4224,0.9989999999999999991118215803,33,0.025000000000000001387778780781
146,146_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,175,2137,0.478224247758499421134814610923,18,0.100000000000000005551115123126
147,147_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4298,2961,0.001000000000000000020816681712,1,0.25
148,148_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1111,4556,0.9989999999999999991118215803,1,0.005000000000000000104083408559
149,149_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2821,3023,0.001000000000000000020816681712,1,0.010000000000000000208166817117
150,150_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,811,2101,0.001000000000000000020816681712,1,0.25
151,151_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,960,3341,0.436222733504092019529707613401,1,0.050000000000000002775557561563
152,152_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1151,3702,0.9989999999999999991118215803,1,0.010000000000000000208166817117
153,153_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,2887,0.001000000000000000020816681712,1,0.001000000000000000020816681712
154,154_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4681,1030,0.340779010695826556354148806349,36,0.005000000000000000104083408559
155,155_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1466,2242,0.9989999999999999991118215803,50,0.025000000000000001387778780781
156,156_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4227,3259,0.750535116082405751036787933117,1,0.025000000000000001387778780781
157,157_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1071,4693,0.729583606547337670811259613401,16,0.025000000000000001387778780781
158,158_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,1721,1093,0.391056822499876888254988216431,1,0.100000000000000005551115123126
159,159_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3818,4865,0.561148429667006065635348477372,20,0.050000000000000002775557561563
160,160_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1972,3042,0.354539571238348449888633240334,1,0.050000000000000002775557561563
161,161_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4604,1086,0.419201411052727124229022592772,38,0.100000000000000005551115123126
162,162_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3321,4893,0.9989999999999999991118215803,33,0.025000000000000001387778780781
163,163_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4976,3728,0.9989999999999999991118215803,1,0.100000000000000005551115123126
164,164_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4298,2388,0.001000000000000000020816681712,50,0.005000000000000000104083408559
165,165_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3986,4598,0.860902261964439974306628755585,21,0.005000000000000000104083408559
166,166_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,120,4033,0.808324496271261350166525971872,18,0.025000000000000001387778780781
167,167_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,820,2558,0.001000000000000000020816681712,1,0.025000000000000001387778780781
168,168_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3598,4435,0.9989999999999999991118215803,15,0.050000000000000002775557561563
169,169_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,109,4564,0.14395861163877965438473438553,45,0.25
170,170_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3379,2729,0.001000000000000000020816681712,1,0.050000000000000002775557561563
171,171_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3724,3650,0.666841384787428381741847260855,1,0.050000000000000002775557561563
172,172_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4624,3860,0.429321375292948503865630982546,50,0.25
173,173_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4935,4081,0.9989999999999999991118215803,50,0.100000000000000005551115123126
174,174_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,767,3940,0.001000000000000000020816681712,1,0.100000000000000005551115123126
175,175_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3837,2746,0.001000000000000000020816681712,50,0.25
176,176_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1934,2340,0.001000000000000000020816681712,1,0.005000000000000000104083408559
177,177_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4353,3350,0.754929366046860894634562555439,50,0.100000000000000005551115123126
178,178_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4163,4232,0.001000000000000000020816681712,50,0.25
179,179_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3340,1882,0.78821355248728885989351056196,1,0.050000000000000002775557561563
180,180_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1553,4488,0.9989999999999999991118215803,50,0.25
181,181_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,6,4762,0.205567090040327193900537849913,50,0.025000000000000001387778780781
182,182_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,3073,0.9989999999999999991118215803,50,0.001000000000000000020816681712
183,183_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2789,3047,0.9989999999999999991118215803,1,0.025000000000000001387778780781
184,184_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,1918,0.9989999999999999991118215803,1,0.005000000000000000104083408559
185,185_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,1799,1089,0.473019709745339134165931227471,1,0.010000000000000000208166817117
186,186_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,159,4660,0.818400870741432862054409724806,1,0.050000000000000002775557561563
187,187_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,3425,1177,0.921217852536096781790320164873,1,0.100000000000000005551115123126
188,188_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,1938,1105,0.699197665464380779809516752721,1,0.25
189,189_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,5000,1391,0.001000000000000000020816681712,50,0.25
190,190_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4450,1087,0.001000000000000000020816681712,3,0.050000000000000002775557561563
191,191_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1983,3459,0.9989999999999999991118215803,18,0.001000000000000000020816681712
192,192_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4650,971,0.001000000000000000020816681712,25,0.25
193,193_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,3462,1273,0.9989999999999999991118215803,1,0.25
194,194_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4439,1088,0.325035086412122031784832643098,1,0.010000000000000000208166817117
195,195_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,454,4042,0.001000000000000000020816681712,50,0.050000000000000002775557561563
196,196_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4482,1101,0.632893412609880967600872736512,3,0.100000000000000005551115123126
197,197_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3433,4479,0.9989999999999999991118215803,50,0.010000000000000000208166817117
198,198_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,133,2190,0.9989999999999999991118215803,50,0.25
199,199_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,1890,1091,0.9989999999999999991118215803,1,0.050000000000000002775557561563
200,200_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,4973,0.344675158897774058797125462661,27,0.001000000000000000020816681712
201,201_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,3000,0.858264275760362793654678625899,1,0.050000000000000002775557561563
202,202_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4595,1061,0.001000000000000000020816681712,5,0.100000000000000005551115123126
203,203_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4736,1187,0.697297652262095724040591449011,45,0.010000000000000000208166817117
204,204_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,1414,1100,0.001000000000000000020816681712,1,0.025000000000000001387778780781
205,205_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3556,3717,0.749556374205271547239703977539,25,0.100000000000000005551115123126
206,206_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1949,3584,0.001000000000000000020816681712,1,0.25
207,207_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,4323,0.454405593205529478861848247107,1,0.025000000000000001387778780781
208,208_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4486,5000,0.396618314667364246783876069458,1,0.005000000000000000104083408559
209,209_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4870,3019,0.440867852038732577213409058459,27,0.005000000000000000104083408559
210,210_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,947,4715,0.853346872053539762248419719981,50,0.100000000000000005551115123126
211,211_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2953,2222,0.001000000000000000020816681712,1,0.025000000000000001387778780781
212,212_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2245,2523,0.001000000000000000020816681712,1,0.001000000000000000020816681712
213,213_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,663,3641,0.459854258051986708455416419383,50,0.050000000000000002775557561563
214,214_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4752,934,0.9989999999999999991118215803,31,0.100000000000000005551115123126
215,215_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,2369,0.001000000000000000020816681712,1,0.010000000000000000208166817117
216,216_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1113,3179,0.743784283181858629774296787218,50,0.100000000000000005551115123126
217,217_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4687,1179,0.001000000000000000020816681712,43,0.25
218,218_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,807,4488,0.346112203370358584297861170853,50,0.010000000000000000208166817117
219,219_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,123,2190,0.001000000000000000020816681712,1,0.100000000000000005551115123126
220,220_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1547,1985,0.001000000000000000020816681712,1,0.010000000000000000208166817117
221,221_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2511,2395,0.001000000000000000020816681712,50,0.100000000000000005551115123126
222,222_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,1939,1095,0.9989999999999999991118215803,1,0.010000000000000000208166817117
223,223_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,3941,0.9989999999999999991118215803,1,0.025000000000000001387778780781
224,224_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4600,2354,0.9989999999999999991118215803,25,0.050000000000000002775557561563
225,225_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,4595,1068,0.001000000000000000020816681712,1,0.010000000000000000208166817117
226,226_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1563,4721,0.591419283723845223832427109301,50,0.025000000000000001387778780781
227,227_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,4385,905,0.001000000000000000020816681712,50,0.050000000000000002775557561563
228,228_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,530,4081,0.001000000000000000020816681712,1,0.001000000000000000020816681712
229,229_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1854,4601,0.747255137670106872604947056971,1,0.100000000000000005551115123126
230,230_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4483,1070,0.001000000000000000020816681712,12,0.100000000000000005551115123126
231,231_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2364,3674,0.001000000000000000020816681712,50,0.025000000000000001387778780781
232,232_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,2553,1231,0.9989999999999999991118215803,1,0.100000000000000005551115123126
233,233_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4274,4507,0.308999754594777298333241333239,1,0.005000000000000000104083408559
234,234_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3096,3243,0.848270622454049827432243091607,1,0.005000000000000000104083408559
235,235_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,65,3735,0.246390088579051036443701150347,28,0.050000000000000002775557561563
236,236_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,662,4376,0.155290519072920768506307354073,18,0.050000000000000002775557561563
237,237_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,14,1778,1050,0.001000000000000000020816681712,50,0.010000000000000000208166817117
238,238_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,5000,1119,0.001000000000000000020816681712,29,0.25
239,239_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,919,3873,0.915685448307787641653021637467,50,0.100000000000000005551115123126
240,240_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,20,1880,1079,0.62492907991217860885768686785,1,0.005000000000000000104083408559
241,241_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1673,4230,0.001000000000000000020816681712,1,0.25
242,242_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4672,1154,0.265137983413151701039822683015,41,0.25
243,243_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4196,2960,0.858257394239804405344784754561,50,0.100000000000000005551115123126
244,244_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4404,2981,0.897050239812817729045946180122,50,0.001000000000000000020816681712
245,245_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,554,2293,0.001000000000000000020816681712,1,0.25
246,246_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3674,2938,0.784582056282294937332721929124,19,0.025000000000000001387778780781
247,247_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4447,989,0.400926087534836383952097094152,32,0.001000000000000000020816681712
248,248_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1100,1764,0.021890391225127463908917135882,50,0.005000000000000000104083408559
249,249_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4258,1019,0.374289315488983198587646938904,26,0.001000000000000000020816681712
250,250_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,843,3068,0.001000000000000000020816681712,36,0.025000000000000001387778780781
251,251_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,6,4857,940,0.341643426019852813890764764437,28,0.050000000000000002775557561563
252,252_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,19,4524,957,0.362059204558010938601597672459,31,0.001000000000000000020816681712
253,253_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,241,2456,0.049640669883275187479476642238,25,0.025000000000000001387778780781
254,254_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,38,4316,1095,0.42766174405262880053868457253,7,0.001000000000000000020816681712
255,255_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1634,2799,0.976836577240358883145177060214,50,0.001000000000000000020816681712
256,256_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4643,1029,0.327791893099856457283181043749,32,0.001000000000000000020816681712
257,257_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4275,2276,0.654289818176288395790152208065,50,0.100000000000000005551115123126
258,258_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,4309,1505,0.9989999999999999991118215803,36,0.010000000000000000208166817117
259,259_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,4212,1375,0.316499051201911629771501566211,2,0.050000000000000002775557561563
260,260_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,3635,889,0.325173064509554188283857456554,34,0.005000000000000000104083408559
261,261_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4584,3452,0.061188284991411941160510679083,16,0.025000000000000001387778780781
262,262_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4761,3961,0.08872704232534464607873303521,1,0.100000000000000005551115123126
263,263_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2824,2781,0.254280947108818433655130775151,35,0.25
264,264_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,489,2189,0.504355416307542880893777237361,50,0.050000000000000002775557561563
265,265_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4284,4900,0.402847657278540827174850846859,50,0.25
266,266_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,4635,0.224814461839248719288164579666,1,0.100000000000000005551115123126
267,267_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1148,4799,0.253454165071076376047898293109,38,0.010000000000000000208166817117
268,268_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,972,5000,0.417380214891751255557039712585,39,0.005000000000000000104083408559
269,269_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3224,1938,0.902299189938250201237224246142,20,0.025000000000000001387778780781
270,270_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3614,2753,0.182673446412618456502485742021,31,0.100000000000000005551115123126
271,271_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,23,4926,949,0.326670606419454112590017302864,31,0.001000000000000000020816681712
272,272_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4370,1,0.655915398357475587509668457642,50,0.050000000000000002775557561563
273,273_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3896,3846,0.046674064081439828322039886643,30,0.25
274,274_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1459,3964,0.662620912947591400588009946659,1,0.005000000000000000104083408559
275,275_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4538,4022,0.179558343817065702019419859425,15,0.005000000000000000104083408559
276,276_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4557,979,0.349387753221094277122915627842,32,0.25
277,277_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1893,1,0.674095264918694092592943434283,50,0.025000000000000001387778780781
278,278_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,5000,1021,0.659000204505809206168009950488,24,0.25
279,279_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1869,4434,0.23879024617940181052233583614,21,0.001000000000000000020816681712
280,280_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4838,1002,0.352262477403197382663790904189,29,0.005000000000000000104083408559
281,281_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3548,2375,0.609938961360957154766992971417,36,0.100000000000000005551115123126
282,282_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,19,4913,1090,0.001000000000000000020816681712,33,0.001000000000000000020816681712
283,283_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2389,3270,0.3627776935796669932621227872,50,0.25
284,284_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1654,1,0.706919285229275162762974105135,50,0.010000000000000000208166817117
285,285_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,13,1011,1112,0.352307681041260256638025794018,1,0.010000000000000000208166817117
286,286_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4902,4508,0.059154545166167880887453378591,17,0.100000000000000005551115123126
287,287_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3886,4745,0.972391288509308782117557257152,50,0.005000000000000000104083408559
288,288_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4652,1099,0.539881247852469492443105991697,38,0.25
289,289_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,7,5000,973,0.352083545242198425739843514748,30,0.25
290,290_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2655,1,0.304519386296128014013362417245,1,0.050000000000000002775557561563
291,291_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4236,1,0.677722668125796734450716485298,1,0.050000000000000002775557561563
292,292_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2972,4309,0.704261205458667771672764956747,50,0.100000000000000005551115123126
293,293_0,FAILED,BoTorch,BOTORCH_MODULAR,,,66,1,0.650645884999705659268443014298,50,0.050000000000000002775557561563
294,294_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4040,1093,0.467354222047048184496276235222,1,0.100000000000000005551115123126
295,295_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1597,2536,0.696697012113575153691158448055,50,0.025000000000000001387778780781
296,296_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4869,3536,0.196916039776570461761195929284,1,0.010000000000000000208166817117
297,297_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3237,1,0.330843579006998345093393254501,50,0.005000000000000000104083408559
298,298_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,989,3411,0.067622051953992970418916286235,50,0.100000000000000005551115123126
299,299_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3797,4721,0.030037504036198325180206936125,1,0.010000000000000000208166817117
300,300_0,FAILED,BoTorch,BOTORCH_MODULAR,,,191,1,0.792846959919017324480705610767,50,0.010000000000000000208166817117
301,301_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3435,4065,0.423169089231046513521761198717,50,0.001000000000000000020816681712
302,302_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3430,3155,0.923147438057716929193929900066,39,0.010000000000000000208166817117
303,303_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,771,1,0.001000000000000000020816681712,1,0.100000000000000005551115123126
304,304_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2193,3975,0.318179646934133253388665707462,35,0.025000000000000001387778780781
305,305_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,1694,1278,0.937666850550623043325515482138,47,0.025000000000000001387778780781
306,306_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4319,941,0.34802048452928141530904326828,29,0.005000000000000000104083408559
307,307_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,5000,838,0.343727964075298153545645618578,31,0.100000000000000005551115123126
308,308_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1753,4433,0.117252169473408246469503524168,21,0.001000000000000000020816681712
309,309_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4205,2168,0.665667381919311296201158256736,30,0.25
310,310_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4269,2421,0.052532898016548493225208460444,1,0.005000000000000000104083408559
311,311_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,6,4590,950,0.276081194040865451899691151993,25,0.25
312,312_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4981,3642,0.59595507528789515294675993573,50,0.010000000000000000208166817117
313,313_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,8,4554,572,0.676272817416324723005516261765,32,0.010000000000000000208166817117
314,314_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,4,1851,935,0.501070059251200627947753218905,1,0.100000000000000005551115123126
315,315_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2579,4636,0.533445573234341163804117513791,1,0.005000000000000000104083408559
316,316_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4780,1006,0.001000000000000000020816681712,22,0.050000000000000002775557561563
317,317_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,1839,1089,0.865773774127336803729804159957,1,0.100000000000000005551115123126
318,318_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2103,1,0.360292652360080567497391257348,50,0.050000000000000002775557561563
319,319_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4716,961,0.65300974744272566319125417067,32,0.100000000000000005551115123126
320,320_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4292,1691,0.920257049209951771828741584613,1,0.050000000000000002775557561563
321,321_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2149,2043,0.323546392603398513809764835969,50,0.001000000000000000020816681712
322,322_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1762,1,0.701968782702865001787984056136,50,0.010000000000000000208166817117
323,323_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1920,1814,0.097368053091545503363590796653,1,0.25
324,324_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1832,3049,0.918822952282143945446080124384,31,0.025000000000000001387778780781
325,325_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,3998,931,0.358372739695010489313631296682,32,0.005000000000000000104083408559
326,326_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1038,4079,0.38874137501499889113532049123,50,0.005000000000000000104083408559
327,327_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4759,4127,0.196270681719800749176485510361,15,0.100000000000000005551115123126
328,328_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,3849,1078,0.351649236975217938105942039329,50,0.100000000000000005551115123126
329,329_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,5000,1031,0.9989999999999999991118215803,28,0.005000000000000000104083408559
330,330_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4453,1135,0.277829861293380608877612303331,47,0.010000000000000000208166817117
331,331_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4940,1,0.681118885547140728853321434144,50,0.025000000000000001387778780781
332,332_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1625,4971,0.734173646352904274436923515168,50,0.001000000000000000020816681712
333,333_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3535,2145,0.00465851500726207831681868754,49,0.001000000000000000020816681712
334,334_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1998,5000,0.263193444313971181003353194683,14,0.100000000000000005551115123126
335,335_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3187,5000,0.343009863201128806231565704365,27,0.005000000000000000104083408559
336,336_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,586,2847,0.342216193973695459451533906758,50,0.010000000000000000208166817117
337,337_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,494,4523,0.739243754821771403840102721006,31,0.005000000000000000104083408559
338,338_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4908,2227,0.616215269017836408949051474337,17,0.025000000000000001387778780781
339,339_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4457,1,0.334846534521473282453030151373,50,0.100000000000000005551115123126
340,340_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3154,3460,0.093680597606027607437972903881,1,0.005000000000000000104083408559
341,341_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4340,1100,0.9989999999999999991118215803,36,0.050000000000000002775557561563
342,342_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3238,1,0.371041981868218129214653799863,50,0.100000000000000005551115123126
343,343_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1111,3209,0.061809031298182666447349475902,21,0.100000000000000005551115123126
344,344_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,151,2808,0.643362361643450397785670702433,28,0.25
345,345_0,FAILED,BoTorch,BOTORCH_MODULAR,,,579,1,0.346730807222038972525979261263,50,0.100000000000000005551115123126
346,346_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,31,4751,898,0.001000000000000000020816681712,31,0.005000000000000000104083408559
347,347_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,15,5000,204,0.676147664376737234093184270023,1,0.100000000000000005551115123126
348,348_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4825,4723,0.50442446834681831102642490805,1,0.001000000000000000020816681712
349,349_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,3354,999,0.9989999999999999991118215803,50,0.010000000000000000208166817117
350,350_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2235,1,0.351669731429964660396336739723,50,0.050000000000000002775557561563
351,351_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4394,1,0.347522318476900571315724164378,26,0.025000000000000001387778780781
352,352_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4195,1,0.821257304067079862974765092076,27,0.025000000000000001387778780781
353,353_0,FAILED,BoTorch,BOTORCH_MODULAR,,,239,1,0.362823441811159608239023555143,20,0.001000000000000000020816681712
354,354_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2261,1,0.388577007151731546041872888964,37,0.025000000000000001387778780781
355,355_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3006,1,0.370166594237431534342164241025,50,0.25
356,356_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3131,2133,0.327984921546929486524390995328,14,0.005000000000000000104083408559
357,357_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,207,4555,83,0.348531106754505548561695604803,50,0.010000000000000000208166817117
358,358_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3752,2568,0.288200665137752498612400131606,17,0.005000000000000000104083408559
359,359_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,16,3686,535,0.526051016875950905316017269797,1,0.010000000000000000208166817117
360,360_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3830,2840,0.153866256426082925745646434734,11,0.001000000000000000020816681712
361,361_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4136,972,0.400917272950737035852597500707,31,0.25
362,362_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4115,3183,0.387595704093567472448000899021,21,0.001000000000000000020816681712
363,363_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4786,1070,0.658581187955702640479671572393,50,0.050000000000000002775557561563
364,364_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1869,2136,0.163907731033247600072044747321,30,0.010000000000000000208166817117
365,365_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1457,3152,0.044688295698815209966436867717,1,0.005000000000000000104083408559
366,366_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4481,4897,0.261847795900917945655095309121,1,0.025000000000000001387778780781
367,367_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,553,2001,0.169119693623072026023024250208,18,0.001000000000000000020816681712
368,368_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,354,2791,0.948938169169960099580407586473,1,0.100000000000000005551115123126
369,369_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3228,2675,0.777093617727159013952586974483,26,0.25
370,370_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2793,4761,0.645224510037000764661740959127,18,0.25
371,371_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,5,4504,912,0.71752368583896775078301288886,31,0.005000000000000000104083408559
372,372_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,5000,978,0.398793280558678464586819245596,50,0.100000000000000005551115123126
373,373_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4410,2470,0.462112822267943035292603326525,1,0.001000000000000000020816681712
374,374_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3652,3760,0.279747431439304750089291928816,50,0.001000000000000000020816681712
375,375_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4359,1059,0.9989999999999999991118215803,27,0.005000000000000000104083408559
376,376_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1171,1,0.596136406137120267167972542666,50,0.010000000000000000208166817117
377,377_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4911,964,0.405732515896010059730514285548,32,0.25
378,378_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2631,2370,0.898309749750145769553455465939,32,0.005000000000000000104083408559
379,379_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,2395,710,0.837865722475157670423584477248,42,0.100000000000000005551115123126
380,380_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4516,2310,0.722349221796857898780785944837,1,0.005000000000000000104083408559
381,381_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,5000,1128,0.099440259367927816280818831274,1,0.050000000000000002775557561563
382,382_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3739,1,0.598680201009974322090556597686,24,0.005000000000000000104083408559
383,383_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4075,1019,0.671514560605799504244828312949,50,0.025000000000000001387778780781
384,384_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,3636,1101,0.244326928811900545568391862616,1,0.050000000000000002775557561563
385,385_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4793,1114,0.305014223853020960408599648872,30,0.100000000000000005551115123126
386,386_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3996,2953,0.502141035845278471683172938356,50,0.025000000000000001387778780781
387,387_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,188,2785,0.715308551575822071377785960067,1,0.001000000000000000020816681712
388,388_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,912,2754,0.479490379065456273632861439182,50,0.010000000000000000208166817117
389,389_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2695,5,0.377324231032380830175299024631,50,0.001000000000000000020816681712
390,390_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,5000,1045,0.348394771238256684142697849893,50,0.005000000000000000104083408559
391,391_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,1871,0.67117386862667272051652389564,1,0.010000000000000000208166817117
392,392_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2172,3585,0.918849166193776767386225401424,1,0.050000000000000002775557561563
393,393_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.374922363387436174964051360803,1,0.001000000000000000020816681712
394,394_0,FAILED,BoTorch,BOTORCH_MODULAR,,,302,1,0.001000000000000000020816681712,36,0.001000000000000000020816681712
395,395_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,3829,927,0.917421964468530526382039624878,47,0.010000000000000000208166817117
396,396_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,31,1865,0.9989999999999999991118215803,50,0.005000000000000000104083408559
397,397_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1437,3614,0.529579048313088107668988868681,1,0.010000000000000000208166817117
398,398_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3194,5000,0.6422944712469428329981724346,1,0.025000000000000001387778780781
399,399_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4824,2524,0.826793825650827596263070518035,50,0.25
400,400_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.810000000000000053290705182008,9,4961,257,0.001000000000000000020816681712,5,0.010000000000000000208166817117
401,401_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,113,4823,0.211145432686226597329692822314,50,0.005000000000000000104083408559
402,402_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,1996,0.061499578888370383389361251147,50,0.050000000000000002775557561563
403,403_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,31,4235,144,0.001000000000000000020816681712,50,0.001000000000000000020816681712
404,404_0,FAILED,BoTorch,BOTORCH_MODULAR,,,429,1,0.598987567592262459825747100695,50,0.25
405,405_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,267,3678,0.711194242814608190705882861948,50,0.010000000000000000208166817117
406,406_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2407,4607,0.32488919401047666735138363947,50,0.050000000000000002775557561563
407,407_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,656,3561,0.368819955425105205915059514155,50,0.050000000000000002775557561563
408,408_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4804,2179,0.307082189634378721621033037081,1,0.010000000000000000208166817117
409,409_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1622,1,0.607050659994536112584739839804,50,0.25
410,410_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4709,3816,0.82514105540286841744546109112,50,0.010000000000000000208166817117
411,411_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2258,3254,0.261245249506803334238469460615,1,0.050000000000000002775557561563
412,412_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3719,3295,0.164650897192995082640720738709,50,0.010000000000000000208166817117
413,413_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3885,3400,0.957651249997728881702130365738,50,0.050000000000000002775557561563
414,414_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2759,2410,0.9989999999999999991118215803,38,0.010000000000000000208166817117
415,415_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3508,2100,0.963537123267291684136637286429,1,0.100000000000000005551115123126
416,416_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,0,2320,3035,0.651224146144145255021840057452,50,0.050000000000000002775557561563
417,417_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1094,1702,0.622026425671790339499978017557,25,0.25
418,418_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4634,1106,0.250898969432490348552278192074,23,0.005000000000000000104083408559
419,419_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,333,3504,0.432043025651794143637829392901,32,0.005000000000000000104083408559
420,420_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,5000,989,0.364311756907000905414406588534,50,0.050000000000000002775557561563
421,421_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,315,4653,0.416383188430092143761385159451,1,0.005000000000000000104083408559
422,422_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4506,3006,0.610623937531197813477490399237,50,0.010000000000000000208166817117
423,423_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4891,1909,0.236053033456877475737556437707,50,0.010000000000000000208166817117
424,424_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,111,3444,0.90435723602327777737031055949,40,0.010000000000000000208166817117
425,425_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1598,1,0.819750528090016472582135520497,33,0.050000000000000002775557561563
426,426_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1678,4050,0.158088005344405618801673085727,1,0.010000000000000000208166817117
427,427_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4387,4739,0.973780788753865977902535178146,50,0.025000000000000001387778780781
428,428_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,5,3832,862,0.423811792204129011985003216978,38,0.025000000000000001387778780781
429,429_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2238,1,0.401188058346531761788611447628,50,0.001000000000000000020816681712
430,430_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2814,2189,0.955065209388410552016068777448,50,0.010000000000000000208166817117
431,431_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4671,3213,0.001000000000000000020816681712,1,0.005000000000000000104083408559
432,432_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,263,4249,0.167455842103510998963855627153,1,0.025000000000000001387778780781
433,433_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,979,4286,0.354571507259585638749399549852,47,0.100000000000000005551115123126
434,434_0,FAILED,BoTorch,BOTORCH_MODULAR,,,44,1,0.599649960711373886113051412394,50,0.025000000000000001387778780781
435,435_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1559,3761,0.061806600500925963637133264683,8,0.025000000000000001387778780781
436,436_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3295,4029,0.290236267398100100667335254911,50,0.25
437,437_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2832,1672,0.001000000000000000020816681712,1,0.010000000000000000208166817117
438,438_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,389,4433,0.037068743668911559308032366289,50,0.005000000000000000104083408559
439,439_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1069,1,0.598132146781776841137912015256,50,0.010000000000000000208166817117
440,440_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3621,1,0.605461127087646766575801393628,50,0.25
441,441_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3240,2814,0.199754216293445446828158651442,20,0.005000000000000000104083408559
442,442_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,693,1935,0.268469251396022012823294744521,1,0.100000000000000005551115123126
443,443_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4438,2668,0.953028568856673530262924032286,39,0.010000000000000000208166817117
444,444_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2622,1,0.398744651041376130251592257991,50,0.001000000000000000020816681712
445,445_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,4556,0.39266186920830004059013162987,50,0.010000000000000000208166817117
446,446_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,803,2715,0.397993278822042328979335934491,50,0.050000000000000002775557561563
447,447_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2624,1,0.603473707772766321255630828091,31,0.100000000000000005551115123126
448,448_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.829999999999999960031971113494,17,692,84,0.816422516217984894915105087421,50,0.25
449,449_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4105,1,0.598339239632746933139628708886,50,0.005000000000000000104083408559
450,450_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4041,2833,0.421262329285663594813371446435,50,0.25
451,451_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1995,3870,0.677300300451459946771137765609,9,0.25
452,452_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1559,3255,0.9989999999999999991118215803,1,0.100000000000000005551115123126
453,453_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,444,3144,0.6425408103952569005556938464,20,0.050000000000000002775557561563
454,454_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1933,1,0.598361239068820993658448514907,50,0.010000000000000000208166817117
455,455_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4415,3805,0.378550856788054113089003749337,1,0.25
456,456_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,1674,1287,0.001000000000000000020816681712,1,0.025000000000000001387778780781
457,457_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2621,1,0.601053790973286372611994465842,50,0.010000000000000000208166817117
458,458_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,5000,1041,0.9989999999999999991118215803,30,0.25
459,459_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1594,5000,0.186443794103930277072933563431,1,0.010000000000000000208166817117
460,460_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4832,4907,0.868877690375300604230801582162,30,0.010000000000000000208166817117
461,461_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2751,2080,0.662419533392489934264801831887,1,0.050000000000000002775557561563
462,462_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,131,4168,0.938554305438008995565724035259,18,0.25
463,463_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1157,3322,0.558755553627567680408105843526,22,0.005000000000000000104083408559
464,464_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,4454,1103,0.944069629645629238012816131231,32,0.005000000000000000104083408559
465,465_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3329,2860,0.591638125786772772762844851968,25,0.050000000000000002775557561563
466,466_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1050,3853,0.10745019275798040059299864879,1,0.050000000000000002775557561563
467,467_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4725,1062,0.9989999999999999991118215803,28,0.100000000000000005551115123126
468,468_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,20,595,183,0.387435311125625236883252000553,1,0.001000000000000000020816681712
469,469_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3457,5000,0.722596595902157745605620675633,23,0.005000000000000000104083408559
470,470_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2416,2148,0.528460032120146916412295468035,31,0.005000000000000000104083408559
471,471_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2586,2294,0.802392941898661615773846733646,1,0.100000000000000005551115123126
472,472_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,816,950,0.691316224248219368675449914008,33,0.100000000000000005551115123126
473,473_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,498,2608,0.928303945270452790516912955354,1,0.050000000000000002775557561563
474,474_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4033,4066,0.719254078687116549239988216868,50,0.005000000000000000104083408559
475,475_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1117,3602,0.173101108230149097755301568213,24,0.010000000000000000208166817117
476,476_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3071,1552,0.607630379951314858466560053785,1,0.010000000000000000208166817117
477,477_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4667,1906,0.303678604401932950018760948296,1,0.010000000000000000208166817117
478,478_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,5000,1078,0.162155211109547725545354524002,29,0.025000000000000001387778780781
479,479_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2433,4611,0.013907536670225066274819702983,24,0.005000000000000000104083408559
480,480_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,855,624,0.766055596206245859036698675482,50,0.100000000000000005551115123126
481,481_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4185,4188,0.10746264948137121408855421123,1,0.005000000000000000104083408559
482,482_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4687,4393,0.788637767369340991763237980194,30,0.050000000000000002775557561563
483,483_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,101,1905,0.580136121383739711276916750649,32,0.025000000000000001387778780781
484,484_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1182,1,0.597171713397556369606888893031,5,0.100000000000000005551115123126
485,485_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,649,3137,0.211363384248726970948695225161,14,0.001000000000000000020816681712
486,486_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,74,3912,0.702689868677967899301961551828,1,0.010000000000000000208166817117
487,487_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2355,4928,0.482232505072593853334694813384,15,0.100000000000000005551115123126
488,488_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,75,4618,0.851631585590428730192513739894,12,0.010000000000000000208166817117
489,489_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,292,3360,59,0.596242715271064982474058524531,50,0.005000000000000000104083408559
490,490_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2786,4037,0.095183075607618586322367093544,29,0.010000000000000000208166817117
491,491_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,5,1098,522,0.075493383959064158661433907582,50,0.100000000000000005551115123126
492,492_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,2194,529,0.054006851147438264537647967245,50,0.050000000000000002775557561563
493,493_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.829999999999999960031971113494,5,425,516,0.9989999999999999991118215803,48,0.100000000000000005551115123126
494,494_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4572,1117,0.9989999999999999991118215803,34,0.001000000000000000020816681712
495,495_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.829999999999999960031971113494,4,1575,760,0.729832434429429244282516719977,42,0.25
496,496_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4551,1075,0.419752052533555963798050925107,22,0.025000000000000001387778780781
497,497_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,5,5000,786,0.9989999999999999991118215803,43,0.25
498,498_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,5000,847,0.659174376898254088530393346446,50,0.100000000000000005551115123126
499,499_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2678,4057,0.9989999999999999991118215803,13,0.001000000000000000020816681712
500,500_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,5000,955,0.462463336036934402617504247246,3,0.100000000000000005551115123126
501,501_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4010,1138,0.795622566620014626259660417418,25,0.050000000000000002775557561563
502,502_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1331,1548,0.9989999999999999991118215803,21,0.100000000000000005551115123126
503,503_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,32,4759,1167,0.9989999999999999991118215803,35,0.001000000000000000020816681712
504,504_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3309,3512,0.869332763925816598415963198931,8,0.100000000000000005551115123126
505,505_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,5,5000,902,0.5042585332733144909411748813,40,0.005000000000000000104083408559
506,506_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,350,3922,0.001000000000000000020816681712,50,0.001000000000000000020816681712
507,507_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2204,4844,0.9989999999999999991118215803,37,0.100000000000000005551115123126
508,508_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,2498,0.001000000000000000020816681712,50,0.100000000000000005551115123126
509,509_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4358,4379,0.552659820699948345357199741557,50,0.100000000000000005551115123126
510,510_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,4947,0.001000000000000000020816681712,1,0.100000000000000005551115123126
511,511_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,1241,1221,0.9989999999999999991118215803,1,0.050000000000000002775557561563
512,512_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4850,1097,0.508418873575959717747707600211,27,0.050000000000000002775557561563
513,513_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3131,3797,0.001000000000000000020816681712,19,0.010000000000000000208166817117
514,514_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,403,3776,0.9989999999999999991118215803,12,0.001000000000000000020816681712
515,515_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,529,4292,100,0.933794720253530718956369582884,30,0.001000000000000000020816681712
516,516_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2284,3106,0.001000000000000000020816681712,38,0.001000000000000000020816681712
517,517_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,626,1735,0.837720550201719094118857356079,50,0.010000000000000000208166817117
518,518_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,7,4411,648,0.663172077497174439209004503937,50,0.005000000000000000104083408559
519,519_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4847,3274,0.861494722644784105547444141848,50,0.001000000000000000020816681712
520,520_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,944,4082,0.167502567552075071199979561243,27,0.001000000000000000020816681712
521,521_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1722,4607,0.371554434387719623167356530757,1,0.001000000000000000020816681712
522,522_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3102,1845,0.021300761677319920289752630538,50,0.25
523,523_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.829999999999999960031971113494,9,2984,294,0.784668733408005025786735586735,50,0.025000000000000001387778780781
524,524_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2569,1870,0.9989999999999999991118215803,50,0.050000000000000002775557561563
525,525_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1496,1676,0.247670688000250688309833435596,20,0.001000000000000000020816681712
526,526_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3195,5000,0.179179557749672646593808167381,50,0.001000000000000000020816681712
527,527_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2264,1699,0.9989999999999999991118215803,26,0.001000000000000000020816681712
528,528_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2877,2170,0.9989999999999999991118215803,1,0.001000000000000000020816681712
529,529_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,3,2240,633,0.458640446676525814773128786328,50,0.001000000000000000020816681712
530,530_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,96,3902,668,0.451584846849624965692271416629,50,0.001000000000000000020816681712
531,531_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,4,3741,993,0.851414408726479554623267631541,50,0.100000000000000005551115123126
532,532_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,5,4972,962,0.856078268818449172883333631034,50,0.25
533,533_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3399,1745,0.001000000000000000020816681712,1,0.001000000000000000020816681712
534,534_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,4644,0.9989999999999999991118215803,50,0.001000000000000000020816681712
535,535_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,2274,0.646190882325133886254775461566,50,0.001000000000000000020816681712
536,536_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,490,4447,0.793570470657920412627106543368,1,0.001000000000000000020816681712
537,537_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4392,2748,0.001000000000000000020816681712,50,0.005000000000000000104083408559
538,538_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,3921,0.94886517356246591337054496762,1,0.005000000000000000104083408559
539,539_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,119,2659,431,0.374440484425379305211123437402,50,0.001000000000000000020816681712
540,540_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,2533,0.9989999999999999991118215803,1,0.001000000000000000020816681712
541,541_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,970,4304,0.9100545997685395294141130762,50,0.005000000000000000104083408559
542,542_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,313,3660,0.07912243896669511611374758786,50,0.001000000000000000020816681712
543,543_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,1711,0.114784318141004815228001234573,1,0.001000000000000000020816681712
544,544_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,4894,650,0.531276957456757292774796042067,44,0.005000000000000000104083408559
545,545_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,5000,1044,0.360086148739381495254008314078,50,0.25
546,546_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,22,2337,509,0.001000000000000000020816681712,50,0.005000000000000000104083408559
547,547_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3171,1,0.431872952822643541015423807039,1,0.001000000000000000020816681712
548,548_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,20,4876,178,0.843968505242481792372188920126,1,0.25
549,549_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3628,1,0.368083275533801823620194682007,1,0.001000000000000000020816681712
550,550_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3245,4754,0.001000000000000000020816681712,50,0.001000000000000000020816681712
551,551_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3571,1883,0.510968199762638186633978421014,1,0.001000000000000000020816681712
552,552_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2071,4522,0.001000000000000000020816681712,50,0.050000000000000002775557561563
553,553_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,3661,0.871550697353765779773482336168,50,0.005000000000000000104083408559
554,554_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1191,4208,0.527547614292993682916232955904,1,0.001000000000000000020816681712
555,555_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4012,1,0.433570308924728531341230564067,1,0.001000000000000000020816681712
556,556_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.819999999999999951150186916493,9,1313,672,0.527768740293653726425304739678,1,0.010000000000000000208166817117
557,557_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,7,4702,701,0.011963066393449814026062050232,17,0.25
558,558_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4692,1,0.432672854652402183450732309211,1,0.001000000000000000020816681712
559,559_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3086,5000,0.873538085375961892431462274544,50,0.001000000000000000020816681712
560,560_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4488,1,0.431371646341773717914236385695,1,0.001000000000000000020816681712
561,561_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4437,4446,0.623974932370790824620598868933,50,0.005000000000000000104083408559
562,562_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,2587,0.549907295645997939992355441063,46,0.001000000000000000020816681712
563,563_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3743,1,0.431658473453009949860614824502,1,0.001000000000000000020816681712
564,564_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2910,3656,0.001000000000000000020816681712,1,0.005000000000000000104083408559
565,565_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4477,4778,0.217159231271752267700136940221,50,0.001000000000000000020816681712
566,566_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4502,1,0.432217416366887818846009849949,1,0.001000000000000000020816681712
567,567_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,4634,649,0.001000000000000000020816681712,50,0.25
568,568_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3288,3014,0.569847517471062547933513542375,1,0.001000000000000000020816681712
569,569_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4785,3231,0.9989999999999999991118215803,50,0.005000000000000000104083408559
570,570_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2164,4719,0.890906215810687407419266037323,50,0.001000000000000000020816681712
571,571_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.849999999999999977795539507497,3,4494,1073,0.363624840245499236779380680673,50,0.050000000000000002775557561563
572,572_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,4037,3952,0.522311924630248003431631786952,50,0.001000000000000000020816681712
573,573_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,873,2136,0.538629635294623820840342887095,50,0.001000000000000000020816681712
574,574_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,4879,0.087847194785521054427590570413,50,0.001000000000000000020816681712
575,575_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,2647,1162,0.9989999999999999991118215803,50,0.100000000000000005551115123126
576,576_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1391,3416,0.001000000000000000020816681712,1,0.010000000000000000208166817117
577,577_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4416,1,0.432806073446397376880412366518,1,0.001000000000000000020816681712
578,578_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,3951,4976,0.463685861730292636639916281638,1,0.001000000000000000020816681712
579,579_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,4581,1786,0.9989999999999999991118215803,50,0.001000000000000000020816681712
580,580_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,3871,607,0.001000000000000000020816681712,30,0.100000000000000005551115123126
581,581_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,2031,0.001000000000000000020816681712,50,0.005000000000000000104083408559
582,582_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,2,2507,1149,0.9989999999999999991118215803,1,0.025000000000000001387778780781
583,583_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4359,1,0.433457503905080421269246926386,1,0.001000000000000000020816681712
584,584_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,976,2957,0.9989999999999999991118215803,50,0.010000000000000000208166817117
585,585_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2814,4386,0.903219053607413613349308434408,1,0.001000000000000000020816681712
586,586_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,1797,632,0.9989999999999999991118215803,50,0.005000000000000000104083408559
587,587_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,1542,0.9989999999999999991118215803,50,0.001000000000000000020816681712
588,588_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,2026,3935,0.101221731278705956436070323434,1,0.001000000000000000020816681712
589,589_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1,4431,0.885044371360052983810362547956,1,0.010000000000000000208166817117
590,590_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,6,3829,1248,0.180067020842830355364938554885,1,0.005000000000000000104083408559
591,591_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,1316,4023,0.788490566247042612424422713957,1,0.001000000000000000020816681712
592,592_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.839999999999999968913755310496,1,5000,4409,0.001000000000000000020816681712,1,0.010000000000000000208166817117
593,593_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,333,2029,0.848250785050976952028634059388,50,0.005000000000000000104083408559</pre>
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<h1><img class='invert_icon' src='i/terminal.svg' style='height: 1em' />&nbsp;Errors</h1>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'><img src='i/clipboard.svg' style='height: 1em'> Copy raw data to clipboard</button>
<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'><img src='i/download.svg' style='height: 1em'> 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_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4907967/4907967_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4909556/4909556_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4911186/4911186_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4912448/4912448_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4912531/4912531_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4915216/4915216_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4917704/4917704_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4925514/4925514_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4927426/4927426_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4929183/4929183_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4930471/4930471_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4930787/4930787_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4932235/4932235_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4933641/4933641_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4934165/4934165_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4936066/4936066_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4936993/4936993_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4951609/4951609_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4987616/4987616_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4988448/4988448_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4993410/4993410_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4995125/4995125_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4996034/4996034_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4997174/4997174_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4998457/4998457_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5004749/5004749_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_TMDBalanced5s_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5008422/5008422_0_log.err not found

</span></pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'><img src='i/clipboard.svg' style='height: 1em'> Copy raw data to clipboard</button>
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<h1><img class='invert_icon' src='i/table.svg' style='height: 1em' />&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_TMDBalanced5s_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><img class='invert_icon' src='i/plot.svg' style='height: 1em' />&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")'><img src='i/clipboard.svg' style='height: 1em'> Copy raw data to clipboard</button>
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<pre id="pre_tab_worker_usage">1746192404.576117,20,0,0
1746192414.2481072,20,0,0
1746194074.922159,20,0,0
1746194075.3959787,20,0,0
1746194077.812948,20,1,5
1746194083.2556992,20,1,5
1746195184.6631868,20,1,5
1746195185.3317547,20,1,5
1746195187.3136675,20,2,10
1746195188.248164,20,2,10
1746195197.314969,20,1,5
1746195197.3977365,20,1,5
1746197504.501249,20,1,5
1746197505.2188802,20,1,5
1746197507.2942784,20,2,10
1746197508.5858116,20,2,10
1746197517.6974504,20,1,5
1746197517.775459,20,1,5
1746198396.2650843,20,1,5
1746198396.8356419,20,1,5
1746198398.6118076,20,2,10
1746198399.8569098,20,2,10
1746198410.1868377,20,1,5
1746198410.277464,20,1,5
1746199645.0288963,20,1,5
1746199645.373289,20,1,5
1746199647.2626655,20,2,10
1746199648.0454097,20,2,10
1746199657.2768652,20,1,5
1746199657.369687,20,1,5
1746200982.0349905,20,1,5
1746200982.5829246,20,1,5
1746200984.3635194,20,2,10
1746200985.641046,20,2,10
1746200994.4538074,20,1,5
1746200994.551636,20,1,5
1746201517.2372172,20,1,5
1746201518.913668,20,1,5
1746201520.6151893,20,2,10
1746201522.0466332,20,2,10
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1746563788.5151942,20,1,5
1746563788.6169708,20,1,5
1746566721.1242504,20,1,5
1746566721.4330602,20,1,5
1746566723.1857305,20,2,10
1746566723.9872406,20,2,10
1746566734.1346693,20,1,5
1746566734.2251096,20,1,5
1746570917.0076082,20,1,5
1746570917.5638268,20,1,5
1746570919.4125578,20,2,10
1746570920.647377,20,2,10
1746570931.515925,20,1,5
1746570931.6157246,20,1,5
1746573798.6682658,20,1,5
1746573799.157468,20,1,5
1746573800.7852957,20,2,10
1746573801.7624648,20,2,10
1746573812.5722713,20,1,5
1746573812.6677086,20,1,5
1746578510.3846943,20,1,5
1746578511.2780752,20,1,5
1746578513.4298093,20,2,10
1746578514.7106977,20,2,10
1746578525.9849765,20,1,5
1746578526.1928504,20,1,5
1746582432.6498618,20,1,5
1746582433.1008513,20,1,5
1746582434.8841798,20,2,10
1746582435.7449148,20,2,10
1746582446.1041892,20,1,5
1746582446.1927788,20,1,5
1746584829.7683458,20,1,5
1746584830.1031678,20,1,5
1746584831.61233,20,2,10
1746584832.6301725,20,2,10
1746584842.9835353,20,1,5
1746584843.0714614,20,1,5
1746586817.7222505,20,1,5
1746586818.2345946,20,1,5
1746586820.147653,20,2,10
1746586821.1249483,20,2,10
1746586830.8941095,20,1,5
1746586830.977326,20,1,5
1746590405.1950703,20,1,5
1746590405.6432605,20,1,5
1746590407.2568154,20,2,10
1746590408.0275655,20,2,10
1746590418.6613142,20,1,5
1746590418.8688543,20,1,5
1746592708.6786654,20,1,5
1746592709.1084974,20,1,5
1746592711.0686727,20,2,10
1746592711.9954336,20,2,10
1746592722.763808,20,1,5
1746592722.845665,20,1,5
1746596228.8852112,20,1,5
1746596229.3056028,20,1,5
1746596231.1839635,20,2,10
1746596231.978375,20,2,10
1746596242.0125382,20,1,5
1746596242.0793192,20,1,5
1746597552.6700106,20,1,5
1746597552.9911397,20,1,5
1746597554.4135213,20,2,10
1746597554.9738567,20,2,10
1746597565.2797022,20,1,5
1746597565.3957663,20,1,5
1746597573.194353,20,1,5
1746597584.4180822,20,1,5
1746597595.4925256,20,1,5
1746597607.0533788,20,1,5
1746597618.9645655,20,1,5
1746597630.5370846,20,1,5
1746597642.1142561,20,1,5
1746597653.713182,20,1,5
1746597665.2867453,20,1,5
1746597676.8613014,20,1,5
1746597688.4785662,20,1,5
1746597700.171806,20,1,5
1746597705.83373,20,1,5
1746597712.7625756,20,0,0
1746597724.067868,20,0,0
1746600111.6557393,20,0,0
1746600112.144119,20,0,0
1746600113.9401746,20,1,5
1746600121.347726,20,1,5
1746603761.121845,20,1,5
1746603761.7168674,20,1,5
1746603763.484913,20,2,10
1746603764.8002594,20,2,10
1746603776.7828186,20,1,5
1746603776.9033327,20,1,5
1746604652.711604,20,1,5
1746604653.2493763,20,1,5
1746604655.28171,20,2,10
1746604656.5347736,20,2,10
1746604668.3411467,20,1,5
1746604668.4512715,20,1,5
1746608139.9278545,20,1,5
1746608140.380734,20,1,5
1746608142.1996257,20,2,10
1746608143.0116866,20,2,10
1746608152.766993,20,1,5
1746608152.8604677,20,1,5
1746610367.3379867,20,1,5
1746610368.0240679,20,1,5
1746610369.5483174,20,2,10
1746610370.370839,20,2,10
1746610380.571892,20,1,5
1746610380.6421387,20,1,5
1746615167.2518237,20,1,5
1746615167.7520742,20,1,5
1746615169.4087455,20,2,10
1746615170.2304265,20,2,10
1746615180.7154772,20,1,5
1746615180.7971332,20,1,5
1746617710.16141,20,1,5
1746617710.4852843,20,1,5
1746617712.0939457,20,2,10
1746617712.6050458,20,2,10
1746617722.4295754,20,1,5
1746617722.5056484,20,1,5
1746620798.6994927,20,1,5
1746620799.1206877,20,1,5
1746620800.7460644,20,2,10
1746620801.3815653,20,2,10
1746620811.5406554,20,1,5
1746620811.6216009,20,1,5
1746624568.0018926,20,1,5
1746624568.5175755,20,1,5
1746624570.3567116,20,2,10
1746624570.9494004,20,2,10
1746624582.8706222,20,1,5
1746624583.0022566,20,1,5
1746626409.7514172,20,1,5
1746626410.1031396,20,1,5
1746626412.6026473,20,2,10
1746626413.1932018,20,2,10
1746626422.6521313,20,1,5
1746626422.7241259,20,1,5
1746629429.60464,20,1,5
1746629429.9676123,20,1,5
1746629431.4084167,20,2,10
1746629431.719594,20,2,10
1746629441.8192248,20,1,5
1746629441.8886101,20,1,5
1746631717.6041124,20,1,5
1746631717.9738271,20,1,5
1746631719.4687872,20,2,10
1746631720.005998,20,2,10
1746631729.219842,20,1,5
1746631729.3029294,20,1,5
1746633878.6998985,20,1,5
1746633879.0561934,20,1,5
1746633880.5084226,20,2,10
1746633881.2297444,20,2,10
1746633890.312746,20,1,5
1746633890.3884406,20,1,5
1746636156.0840647,20,1,5
1746636156.4820523,20,1,5
1746636158.0789323,20,2,10
1746636158.6828423,20,2,10
1746636168.0360844,20,1,5
1746636168.2316046,20,1,5
1746638955.29291,20,1,5
1746638955.8721974,20,1,5
1746638957.3893547,20,2,10
1746638958.2115858,20,2,10
1746638968.6944594,20,1,5
1746638968.7855132,20,1,5
1746642487.9338512,20,1,5
1746642488.2118268,20,1,5
1746642489.8681762,20,2,10
1746642490.3149755,20,2,10
1746642499.8929968,20,1,5
1746642499.9875743,20,1,5
1746646844.8314204,20,1,5
1746646845.3263338,20,1,5
1746646847.309676,20,2,10
1746646847.8879733,20,2,10
1746646858.7001886,20,1,5
1746646858.8982008,20,1,5
1746651366.6161695,20,1,5
1746651367.098665,20,1,5
1746651368.7174103,20,2,10
1746651369.3949385,20,2,10
1746651379.5051627,20,1,5
1746651379.583405,20,1,5
1746654242.5667794,20,1,5
1746654243.0793726,20,1,5
1746654244.6620915,20,2,10
1746654245.4106774,20,2,10
1746654255.9591475,20,1,5
1746654256.0413733,20,1,5
1746657331.0311923,20,1,5
1746657331.5505424,20,1,5
1746657333.3153884,20,2,10
1746657333.8761399,20,2,10
1746657346.0943048,20,1,5
1746657346.2961297,20,1,5
1746657353.1889286,20,1,5
1746657364.779443,20,1,5
1746657370.8076432,20,1,5
1746657377.9207647,20,0,0
1746657389.4067671,20,0,0
1746660367.6172092,20,0,0
1746660367.8848002,20,0,0
1746660369.3682704,20,1,5
1746660375.7502527,20,1,5
1746663214.9883144,20,1,5
1746663215.4107952,20,1,5
1746663217.2627254,20,2,10
1746663217.788581,20,2,10
1746663228.5389893,20,1,5
1746663228.6305418,20,1,5
1746664805.560335,20,1,5
1746664805.9773142,20,1,5
1746664808.3720405,20,2,10
1746664808.9282713,20,2,10
1746664818.754782,20,1,5
1746664818.8325899,20,1,5
1746668201.5927777,20,1,5
1746668202.2408981,20,1,5
1746668204.305429,20,2,10
1746668205.4412324,20,2,10
1746668216.2315018,20,1,5
1746668216.3245142,20,1,5
1746670984.0753295,20,1,5
1746670984.3480911,20,1,5
1746670986.0333664,20,2,10
1746670986.314562,20,2,10
1746670996.781092,20,1,5
1746670996.8422723,20,1,5
1746674532.377342,20,1,5
1746674533.037176,20,1,5
1746674534.5472422,20,2,10
1746674535.2511055,20,2,10
1746674545.4734876,20,1,5
1746674546.2568972,20,1,5
1746677224.2533524,20,1,5
1746677224.6536734,20,1,5
1746677226.119971,20,2,10
1746677226.8751876,20,2,10
1746677236.278577,20,1,5
1746677236.529197,20,1,5
1746681357.6356447,20,1,5
1746681358.1928194,20,1,5
1746681360.1481953,20,2,10
1746681360.7453773,20,2,10
1746681372.9111662,20,1,5
1746681372.9906392,20,1,5
1746683695.92191,20,1,5
1746683696.1992354,20,1,5
1746683697.6611698,20,2,10
1746683698.2165143,20,2,10
1746683708.7081861,20,1,5
1746683708.7721064,20,1,5
1746687665.1327586,20,1,5
1746687665.7165382,20,1,5
1746687667.2025545,20,2,10
1746687667.7019312,20,2,10
1746687676.8782833,20,1,5
1746687677.0396078,20,1,5
1746691525.8735225,20,1,5
1746691526.303295,20,1,5
1746691528.1010206,20,2,10
1746691528.8082402,20,2,10
1746691538.392476,20,1,5
1746691538.850835,20,1,5
1746695674.7322385,20,1,5
1746695675.2362194,20,1,5
1746695677.0724,20,2,10
1746695677.977818,20,2,10
1746695688.6168923,20,1,5
1746695688.6986187,20,1,5
1746699948.2863963,20,1,5
1746699948.6819105,20,1,5
1746699950.1937115,20,2,10
1746699950.7217653,20,2,10
1746699962.297569,20,1,5
1746699962.355615,20,1,5
1746703887.2401977,20,1,5
1746703887.7933607,20,1,5
1746703889.4473526,20,2,10
1746703890.646662,20,2,10
1746703902.781239,20,1,5
1746703902.875734,20,1,5
1746708113.0453317,20,1,5
1746708113.520706,20,1,5
1746708115.2484808,20,2,10
1746708116.2593062,20,2,10
1746708127.823411,20,1,5
1746708127.899338,20,1,5
1746711811.779542,20,1,5
1746711812.2536263,20,1,5
1746711814.300191,20,2,10
1746711815.5032508,20,2,10
1746711828.0448003,20,1,5
1746711828.155321,20,1,5
1746715984.3753142,20,1,5
1746715985.0903432,20,1,5
1746715986.7314878,20,2,10
1746715987.589728,20,2,10
1746716000.7661467,20,1,5
1746716000.8631952,20,1,5
1746719848.4471083,20,1,5
1746719849.0642974,20,1,5
1746719850.500145,20,2,10
1746719851.0645025,20,2,10
1746719863.4603086,20,1,5
1746719863.8472311,20,1,5
1746725136.5253952,20,1,5
1746725137.2602224,20,1,5
1746725139.270033,20,2,10
1746725139.9861968,20,2,10
1746725154.287446,20,1,5
1746725154.38022,20,1,5
1746728387.7288558,20,1,5
1746728388.0845475,20,1,5
1746728389.5017066,20,2,10
1746728390.118314,20,2,10
1746728402.3690898,20,1,5
1746728402.945541,20,1,5
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'><img src='i/clipboard.svg' style='height: 1em'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'><img src='i/download.svg' style='height: 1em'> Download &raquo;worker_usage.csv&laquo; as file</button>

<h1><img class='invert_icon' src='i/cpu.svg' style='height: 1em' />&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")'><img src='i/clipboard.svg' style='height: 1em'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'><img src='i/download.svg' style='height: 1em'> 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
1746192404,633.0546875,30.6
1746192404,633.2109375,32.0
1746192404,633.2109375,32.1
1746192404,633.2109375,35.4
1746192404,633.2109375,26.5
1746192404,633.2109375,32.6
1746192404,633.2109375,33.3
1746220096,910.23046875,33.0
1746220096,910.23046875,18.3
1746220096,910.23046875,18.5
1746220096,910.23046875,18.9
1746240682,815.12890625,16.4
1746240682,815.12890625,15.4
1746240682,815.12890625,15.7
1746240682,815.12890625,16.0
1746279396,885.99609375,16.3
1746279396,885.99609375,16.5
1746279396,885.99609375,16.6
1746279396,885.99609375,23.1
1746311764,891.70703125,20.0
1746311764,891.70703125,19.3
1746311764,891.70703125,18.7
1746311764,891.70703125,22.2
1746347162,962.37890625,20.2
1746347162,962.37890625,20.1
1746347162,962.37890625,19.0
1746347162,962.37890625,18.2
1746401235,930.8203125,25.0
1746401235,930.8203125,27.2
1746401235,930.8203125,26.6
1746401235,930.8203125,26.9
1746465635,1093.26953125,20.9
1746465635,1093.26953125,17.0
1746465635,1093.26953125,15.6
1746465635,1093.26953125,20.0
1746540547,1009.515625,20.4
1746540547,1009.515625,19.1
1746540547,1009.515625,20.4
1746540547,1009.515625,18.5
1746597572,1034.0625,16.1
1746597572,1034.0625,17.3
1746597573,1034.0625,16.7
1746597573,1034.0625,19.0
1746657352,1043.56640625,13.1
1746657352,1043.56640625,10.8
1746657352,1043.56640625,10.9
1746657352,1043.56640625,10.9
1746728409,1063.4921875,15.1
1746728409,1063.4921875,23.6
1746728410,1063.4921875,24.0
1746728410,1063.4921875,19.6
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'><img src='i/clipboard.svg' style='height: 1em'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'><img src='i/download.svg' style='height: 1em'> Download &raquo;cpu_ram_usage.csv&laquo; as file</button>

<h1><img class='invert_icon' src='i/plot.svg' style='height: 1em' />&nbsp;Param-Distrib by Status</h1>
<div class='invert_in_dark_mode' id='parameter_by_status_distribution'></div>
<h1><img class='invert_icon' src='i/insights.svg' style='height: 1em' />&nbsp;Insights</h1>
<h2>Visualization for result: <code>ACCURACY</code> (goal: <b>maximize</b>)</h2><p>Best value: <b>0.85</b><br />
Achieved at:<br />
- <code>RUNTIME</code> = 6<br />
- <code>recent_samples_size</code> = 4753<br />
- <code>n_samples</code> = 832<br />
- <code>feature_proportion</code> = 0.40686320008337<br />
- <code>n_clusters</code> = 28<br />
- <code>confidence</code> = 0.005</p><div class="result_parameter_visualization" data-resname="ACCURACY"></div><h2>Visualization for result: <code>RUNTIME</code> (goal: <b>minimize</b>)</h2><p>Best value: <b>0</b><br />
Achieved at:<br />
- <code>ACCURACY</code> = 0.84<br />
- <code>recent_samples_size</code> = 2320<br />
- <code>n_samples</code> = 3035<br />
- <code>feature_proportion</code> = 0.65122414614415<br />
- <code>n_clusters</code> = 50<br />
- <code>confidence</code> = 0.05</p><div class="result_parameter_visualization" data-resname="RUNTIME"></div><h2>Parameter statistics</h2><table border='1' cellpadding='5' cellspacing='0'><thead><tr><th>Parameter</th><th>Min</th><th>Max</th><th>Mean</th><th>Std Dev</th><th>Count</th></tr></thead><tbody><tr><td>generation_method</td><td colspan='5' style='text-align:center;'>No numerical statistics available</td></tr><tr><td>ACCURACY</td><td>0.81</td><td>0.85</td><td>0.8414</td><td>0.0043</td><td>543</td></tr><tr><td>RUNTIME</td><td>0</td><td>529</td><td>5.8545</td><td>30.1325</td><td>543</td></tr><tr><td>recent_samples_size</td><td>1</td><td>5000</td><td>2832.1616</td><td>1649.5404</td><td>594</td></tr><tr><td>n_samples</td><td>1</td><td>5000</td><td>2382.3603</td><td>1561.7867</td><td>594</td></tr><tr><td>feature_proportion</td><td>0.001</td><td>0.999</td><td>0.5</td><td>0.3319</td><td>594</td></tr><tr><td>n_clusters</td><td>1</td><td>50</td><td>26.4091</td><td>20.162</td><td>594</td></tr><tr><td>confidence</td><td>0.001</td><td>0.25</td><td>0.0583</td><td>0.0808</td><td>594</td></tr></tbody></table>
<h1><img class='invert_icon' src='i/plot.svg' style='height: 1em' />&nbsp;Parallel Plot</h1>
<input type="checkbox" id="enable_slurm_id_if_exists" onchange="createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names, true)" /> Show SLURM-Job-ID (if it exists)<br><div class="invert_in_dark_mode" id="parallel-plot"></div>
<h1><img class='invert_icon' src='i/scatter.svg' style='height: 1em' />&nbsp;Scatter-2D</h1>
<div class='invert_in_dark_mode' id='plotScatter2d'></div>
<h1><img class='invert_icon' src='i/scatter.svg' style='height: 1em' />&nbsp;Scatter-3D</h1>
<div class='invert_in_dark_mode' id='plotScatter3d'></div>
<h1><img class='invert_icon' src='i/plot.svg' style='height: 1em' />&nbsp;Job Status Distribution</h1>
<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
<h1><img class='invert_icon' src='i/plot.svg' style='height: 1em' />&nbsp;Boxplots</h1>
<div class="invert_in_dark_mode" id="plotBoxplot"></div>
<h1><img class='invert_icon' src='i/violin.svg' style='height: 1em' />&nbsp;Violin</h1>
<div class="invert_in_dark_mode" id="plotViolin"></div>
<h1><img class='invert_icon' src='i/plot.svg' style='height: 1em' />&nbsp;Histogram</h1>
<div class="invert_in_dark_mode" id="plotHistogram"></div>
<h1><img class='invert_icon' src='i/plot.svg' style='height: 1em' />&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><img class='invert_icon' src='i/scatter.svg' style='height: 1em' />&nbsp;Result-Pairs</h1>
<div class="invert_in_dark_mode" id="plotResultPairs"></div>
<h1><img class='invert_icon' src='i/evolution.svg' style='height: 1em' />&nbsp;Evolution</h1>
<div class="invert_in_dark_mode" id="plotResultEvolution"></div>
	</body>
</html>
Experiment overview:

Job Summary per Generation Node

Experiment parameters:

Number of evaluations

Result names and types

Git-Version

Arguments Overview:

Visualization for result: `ACCURACY` (goal: maximize)

Visualization for result: `RUNTIME` (goal: minimize)

Parameter statistics

Correlation Heatmap Explanation

How It Works

Color Scale: Yellow to Purple (Viridis)

What the Heatmap Shows

Experiment overview:

Job Summary per Generation Node

Experiment parameters:

Number of evaluations

Result names and types

Git-Version

Arguments Overview:

Visualization for result: ACCURACY (goal: maximize)

Visualization for result: RUNTIME (goal: minimize)

Parameter statistics

Correlation Heatmap Explanation

How It Works

Color Scale: Yellow to Purple (Viridis)

What the Heatmap Shows

Visualization for result: `ACCURACY` (goal: maximize)

Visualization for result: `RUNTIME` (goal: minimize)
