added test cases
This commit is contained in:
Florian Förster
@@ -0,0 +1,333 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1">
|
||||
<meta name="generator" content="pdoc3 0.11.5">
|
||||
<title>lang_main.analysis.timeline API documentation</title>
|
||||
<meta name="description" content="">
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/13.0.0/sanitize.min.css" integrity="sha512-y1dtMcuvtTMJc1yPgEqF0ZjQbhnc/bFhyvIyVNb9Zk5mIGtqVaAB1Ttl28su8AvFMOY0EwRbAe+HCLqj6W7/KA==" crossorigin>
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/13.0.0/typography.min.css" integrity="sha512-Y1DYSb995BAfxobCkKepB1BqJJTPrOp3zPL74AWFugHHmmdcvO+C48WLrUOlhGMc0QG7AE3f7gmvvcrmX2fDoA==" crossorigin>
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/styles/default.min.css" crossorigin>
|
||||
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:1.5em;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:2em 0 .50em 0}h3{font-size:1.4em;margin:1.6em 0 .7em 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .2s ease-in-out}a:visited{color:#503}a:hover{color:#b62}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900;font-weight:bold}pre code{font-size:.8em;line-height:1.4em;padding:1em;display:block}code{background:#f3f3f3;font-family:"DejaVu Sans Mono",monospace;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source > summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible;min-width:max-content}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em 1em;margin:1em 0}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul ul{padding-left:1em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js" integrity="sha512-D9gUyxqja7hBtkWpPWGt9wfbfaMGVt9gnyCvYa+jojwwPHLCzUm5i8rpk7vD7wNee9bA35eYIjobYPaQuKS1MQ==" crossorigin></script>
|
||||
<script>window.addEventListener('DOMContentLoaded', () => {
|
||||
hljs.configure({languages: ['bash', 'css', 'diff', 'graphql', 'ini', 'javascript', 'json', 'plaintext', 'python', 'python-repl', 'rust', 'shell', 'sql', 'typescript', 'xml', 'yaml']});
|
||||
hljs.highlightAll();
|
||||
/* Collapse source docstrings */
|
||||
setTimeout(() => {
|
||||
[...document.querySelectorAll('.hljs.language-python > .hljs-string')]
|
||||
.filter(el => el.innerHTML.length > 200 && ['"""', "'''"].includes(el.innerHTML.substring(0, 3)))
|
||||
.forEach(el => {
|
||||
let d = document.createElement('details');
|
||||
d.classList.add('hljs-string');
|
||||
d.innerHTML = '<summary>"""</summary>' + el.innerHTML.substring(3);
|
||||
el.replaceWith(d);
|
||||
});
|
||||
}, 100);
|
||||
})</script>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>lang_main.analysis.timeline</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="lang_main.analysis.timeline.calc_delta_to_next_failure"><code class="name flex">
|
||||
<span>def <span class="ident">calc_delta_to_next_failure</span></span>(<span>data: pandas.core.frame.DataFrame,<br>date_feature: str = 'ErstellungsDatum',<br>name_delta_feature: str = 'Zeitspanne bis zum nächsten Ereignis [Tage]',<br>convert_to_days: bool = True) ‑> pandas.core.frame.DataFrame</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def calc_delta_to_next_failure(
|
||||
data: DataFrameTLFiltered,
|
||||
date_feature: str = 'ErstellungsDatum',
|
||||
name_delta_feature: str = NAME_DELTA_FEAT_TO_NEXT_FAILURE,
|
||||
convert_to_days: bool = True,
|
||||
) -> DataFrameTLFiltered:
|
||||
data = data.copy()
|
||||
last_val = data[date_feature].iat[-1]
|
||||
shifted = data[date_feature].shift(-1, fill_value=last_val)
|
||||
data[name_delta_feature] = shifted - data[date_feature]
|
||||
data = data.sort_values(by=name_delta_feature, ascending=False)
|
||||
|
||||
if convert_to_days:
|
||||
data[name_delta_feature] = data[name_delta_feature].dt.days
|
||||
|
||||
return data</code></pre>
|
||||
</details>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="lang_main.analysis.timeline.calc_delta_to_repair"><code class="name flex">
|
||||
<span>def <span class="ident">calc_delta_to_repair</span></span>(<span>data: pandas.core.frame.DataFrame,<br>date_feature_start: str = 'ErstellungsDatum',<br>date_feature_end: str = 'ErledigungsDatum',<br>name_delta_feature: str = 'Zeitspanne bis zur Behebung [Tage]',<br>convert_to_days: bool = True) ‑> tuple[pandas.core.frame.DataFrame]</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def calc_delta_to_repair(
|
||||
data: DataFrame,
|
||||
date_feature_start: str = 'ErstellungsDatum',
|
||||
date_feature_end: str = 'ErledigungsDatum',
|
||||
name_delta_feature: str = NAME_DELTA_FEAT_TO_REPAIR,
|
||||
convert_to_days: bool = True,
|
||||
) -> tuple[DataFrame]:
|
||||
logger.info('Calculating time differences between start and end of operations...')
|
||||
data = data.copy()
|
||||
data[name_delta_feature] = data[date_feature_end] - data[date_feature_start]
|
||||
|
||||
if convert_to_days:
|
||||
data[name_delta_feature] = data[name_delta_feature].dt.days
|
||||
|
||||
logger.info('Calculation successful.')
|
||||
|
||||
return (data,)</code></pre>
|
||||
</details>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="lang_main.analysis.timeline.cleanup_descriptions"><code class="name flex">
|
||||
<span>def <span class="ident">cleanup_descriptions</span></span>(<span>data: pandas.core.frame.DataFrame,<br>properties: Collection[str] = ('VorgangsBeschreibung', 'ErledigungsBeschreibung')) ‑> tuple[pandas.core.frame.DataFrame]</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def cleanup_descriptions(
|
||||
data: DataFrame,
|
||||
properties: Collection[str] = (
|
||||
'VorgangsBeschreibung',
|
||||
'ErledigungsBeschreibung',
|
||||
),
|
||||
) -> tuple[DataFrame]:
|
||||
logger.info('Cleaning necessary descriptions...')
|
||||
data = data.copy()
|
||||
features = list(properties)
|
||||
data[features] = data[features].fillna('N.V.')
|
||||
(data,) = entry_wise_cleansing(data, target_features=features)
|
||||
logger.info('Cleansing successful.')
|
||||
|
||||
return (data.copy(),)</code></pre>
|
||||
</details>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="lang_main.analysis.timeline.filter_activities_per_obj_id"><code class="name flex">
|
||||
<span>def <span class="ident">filter_activities_per_obj_id</span></span>(<span>data: pandas.core.frame.DataFrame,<br>activity_feature: str = 'VorgangsTypName',<br>relevant_activity_types: Iterable[str] = ('Reparaturauftrag (Portal)',),<br>feature_obj_id: str = 'ObjektID',<br>threshold_num_activities: int = 1) ‑> tuple[pandas.core.frame.DataFrame, pandas.core.series.Series]</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def filter_activities_per_obj_id(
|
||||
data: DataFrame,
|
||||
activity_feature: str = 'VorgangsTypName',
|
||||
relevant_activity_types: Iterable[str] = ('Reparaturauftrag (Portal)',),
|
||||
feature_obj_id: str = 'ObjektID',
|
||||
threshold_num_activities: int = 1,
|
||||
) -> tuple[DataFrame, Series]:
|
||||
data = data.copy()
|
||||
# filter only relevant activities, count occurrences for each ObjectID
|
||||
logger.info('Filtering activities per ObjectID...')
|
||||
filt_rel_activities = data[activity_feature].isin(relevant_activity_types)
|
||||
data_filter_activities = data.loc[filt_rel_activities].copy()
|
||||
num_activities_per_obj_id = cast(
|
||||
Series, data_filter_activities[feature_obj_id].value_counts(sort=True)
|
||||
)
|
||||
# filter for ObjectIDs with more than given number of activities
|
||||
filt_below_thresh = num_activities_per_obj_id <= threshold_num_activities
|
||||
# index of series contains ObjectIDs
|
||||
obj_ids_below_thresh = num_activities_per_obj_id[filt_below_thresh].index
|
||||
filt_entries_below_thresh = data_filter_activities[feature_obj_id].isin(
|
||||
obj_ids_below_thresh
|
||||
)
|
||||
|
||||
num_activities_per_obj_id = num_activities_per_obj_id.loc[~filt_below_thresh]
|
||||
data_filter_activities = data_filter_activities.loc[~filt_entries_below_thresh]
|
||||
logger.info('Activities per ObjectID filtered successfully.')
|
||||
|
||||
return data_filter_activities, num_activities_per_obj_id</code></pre>
|
||||
</details>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="lang_main.analysis.timeline.filter_timeline_cands"><code class="name flex">
|
||||
<span>def <span class="ident">filter_timeline_cands</span></span>(<span>data: pandas.core.frame.DataFrame,<br>cands: dict[int, tuple[tuple[int | numpy.int64, ...], ...]],<br>obj_id: int,<br>entry_idx: int,<br>sort_feature: str = 'ErstellungsDatum') ‑> pandas.core.frame.DataFrame</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def filter_timeline_cands(
|
||||
data: DataFrame,
|
||||
cands: TimelineCandidates,
|
||||
obj_id: ObjectID,
|
||||
entry_idx: int,
|
||||
sort_feature: str = 'ErstellungsDatum',
|
||||
) -> DataFrameTLFiltered:
|
||||
data = data.copy()
|
||||
cands_for_obj_id = cands[obj_id]
|
||||
cands_choice = cands_for_obj_id[entry_idx]
|
||||
data = data.loc[list(cands_choice)].sort_values(
|
||||
by=sort_feature,
|
||||
ascending=True,
|
||||
)
|
||||
|
||||
return data</code></pre>
|
||||
</details>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="lang_main.analysis.timeline.generate_model_input"><code class="name flex">
|
||||
<span>def <span class="ident">generate_model_input</span></span>(<span>data: pandas.core.frame.DataFrame,<br>target_feature_name: str = 'nlp_model_input',<br>model_input_features: Iterable[str] = ('VorgangsTypName', 'VorgangsArtText', 'VorgangsBeschreibung')) ‑> tuple[pandas.core.frame.DataFrame]</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate_model_input(
|
||||
data: DataFrame,
|
||||
target_feature_name: str = 'nlp_model_input',
|
||||
model_input_features: Iterable[str] = (
|
||||
'VorgangsTypName',
|
||||
'VorgangsArtText',
|
||||
'VorgangsBeschreibung',
|
||||
),
|
||||
) -> tuple[DataFrame]:
|
||||
logger.info('Generating concatenation of model input features...')
|
||||
data = data.copy()
|
||||
model_input_features = list(model_input_features)
|
||||
input_features = data[model_input_features].fillna('').astype(str)
|
||||
data[target_feature_name] = input_features.apply(
|
||||
lambda x: ' - '.join(x),
|
||||
axis=1,
|
||||
)
|
||||
logger.info('Model input generated successfully.')
|
||||
|
||||
return (data,)</code></pre>
|
||||
</details>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="lang_main.analysis.timeline.get_timeline_candidates"><code class="name flex">
|
||||
<span>def <span class="ident">get_timeline_candidates</span></span>(<span>data: pandas.core.frame.DataFrame,<br>num_activities_per_obj_id: pandas.core.series.Series,<br>*,<br>model: sentence_transformers.SentenceTransformer.SentenceTransformer,<br>cos_sim_threshold: float,<br>feature_obj_id: str = 'ObjektID',<br>feature_obj_text: str = 'HObjektText',<br>model_input_feature: str = 'nlp_model_input') ‑> tuple[dict[int, tuple[tuple[int | numpy.int64, ...], ...]], dict[int, str]]</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def get_timeline_candidates(
|
||||
data: DataFrame,
|
||||
num_activities_per_obj_id: Series,
|
||||
*,
|
||||
model: SentenceTransformer,
|
||||
cos_sim_threshold: float,
|
||||
feature_obj_id: str = 'ObjektID',
|
||||
feature_obj_text: str = 'HObjektText',
|
||||
model_input_feature: str = 'nlp_model_input',
|
||||
) -> tuple[TimelineCandidates, dict[ObjectID, str]]:
|
||||
logger.info('Obtaining timeline candidates...')
|
||||
candidates = _get_timeline_candidates_index(
|
||||
data=data,
|
||||
num_activities_per_obj_id=num_activities_per_obj_id,
|
||||
model=model,
|
||||
cos_sim_threshold=cos_sim_threshold,
|
||||
feature_obj_id=feature_obj_id,
|
||||
model_input_feature=model_input_feature,
|
||||
)
|
||||
tl_candidates = _transform_timeline_candidates(candidates)
|
||||
logger.info('Timeline candidates obtained successfully.')
|
||||
# text mapping to obtain object descriptors
|
||||
logger.info('Mapping ObjectIDs to their respective text descriptor...')
|
||||
map_obj_text = _map_obj_id_to_texts(
|
||||
data=data,
|
||||
feature_obj_id=feature_obj_id,
|
||||
feature_obj_text=feature_obj_text,
|
||||
)
|
||||
logger.info('ObjectIDs successfully mapped to text descriptors.')
|
||||
|
||||
return tl_candidates, map_obj_text</code></pre>
|
||||
</details>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="lang_main.analysis.timeline.remove_non_relevant_obj_ids"><code class="name flex">
|
||||
<span>def <span class="ident">remove_non_relevant_obj_ids</span></span>(<span>data: pandas.core.frame.DataFrame,<br>thresh_unique_feat_per_id: int,<br>*,<br>feature_uniqueness: str = 'HObjektText',<br>feature_obj_id: str = 'ObjektID') ‑> tuple[pandas.core.frame.DataFrame]</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def remove_non_relevant_obj_ids(
|
||||
data: DataFrame,
|
||||
thresh_unique_feat_per_id: int,
|
||||
*,
|
||||
feature_uniqueness: str = 'HObjektText',
|
||||
feature_obj_id: str = 'ObjektID',
|
||||
) -> tuple[DataFrame]:
|
||||
logger.info('Removing non-relevant ObjectIDs from dataset...')
|
||||
data = data.copy()
|
||||
ids_to_ignore = _non_relevant_obj_ids(
|
||||
data=data,
|
||||
thresh_unique_feat_per_id=thresh_unique_feat_per_id,
|
||||
feature_uniqueness=feature_uniqueness,
|
||||
feature_obj_id=feature_obj_id,
|
||||
)
|
||||
# only retain entries with ObjectIDs not in IDs to ignore
|
||||
data = data.loc[~(data[feature_obj_id].isin(ids_to_ignore))]
|
||||
logger.debug('Ignored ObjectIDs: %s', ids_to_ignore)
|
||||
logger.info('Non-relevant ObjectIDs removed successfully.')
|
||||
|
||||
return (data,)</code></pre>
|
||||
</details>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="lang_main.analysis" href="index.html">lang_main.analysis</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="lang_main.analysis.timeline.calc_delta_to_next_failure" href="#lang_main.analysis.timeline.calc_delta_to_next_failure">calc_delta_to_next_failure</a></code></li>
|
||||
<li><code><a title="lang_main.analysis.timeline.calc_delta_to_repair" href="#lang_main.analysis.timeline.calc_delta_to_repair">calc_delta_to_repair</a></code></li>
|
||||
<li><code><a title="lang_main.analysis.timeline.cleanup_descriptions" href="#lang_main.analysis.timeline.cleanup_descriptions">cleanup_descriptions</a></code></li>
|
||||
<li><code><a title="lang_main.analysis.timeline.filter_activities_per_obj_id" href="#lang_main.analysis.timeline.filter_activities_per_obj_id">filter_activities_per_obj_id</a></code></li>
|
||||
<li><code><a title="lang_main.analysis.timeline.filter_timeline_cands" href="#lang_main.analysis.timeline.filter_timeline_cands">filter_timeline_cands</a></code></li>
|
||||
<li><code><a title="lang_main.analysis.timeline.generate_model_input" href="#lang_main.analysis.timeline.generate_model_input">generate_model_input</a></code></li>
|
||||
<li><code><a title="lang_main.analysis.timeline.get_timeline_candidates" href="#lang_main.analysis.timeline.get_timeline_candidates">get_timeline_candidates</a></code></li>
|
||||
<li><code><a title="lang_main.analysis.timeline.remove_non_relevant_obj_ids" href="#lang_main.analysis.timeline.remove_non_relevant_obj_ids">remove_non_relevant_obj_ids</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.5</a>.</p>
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user