mirror of
https://github.com/pyscript/pyscript.git
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181 lines
6.9 KiB
HTML
181 lines
6.9 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<title>Pyscript/Panel KMeans Demo</title>
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<link rel="icon" href="https://unpkg.com/@holoviz/panel@0.13.0-rc.10/dist/icons/favicon.ico" type="">
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<meta name="name" content="PyScript/Panel KMeans Demo">
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css" type="text/css" />
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<link rel="stylesheet" href="https://unpkg.com/@holoviz/panel@0.13.0-rc.10/dist/css/widgets.css" type="text/css" />
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<link rel="stylesheet" href="https://unpkg.com/@holoviz/panel@0.13.0-rc.10/dist/css/markdown.css" type="text/css" />
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<link rel="stylesheet" href="https://unpkg.com/@holoviz/panel@0.13.0-rc.10/dist/css/loading.css" type="text/css" />
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<link rel="stylesheet" href="https://unpkg.com/@holoviz/panel@0.13.0-rc.10/dist/css/dataframe.css" type="text/css" />
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<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega@5"></script>
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<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega-lite@5"></script>
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<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega-embed@6"></script>
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<script type="text/javascript" src="https://unpkg.com/tabulator-tables@4.9.3/dist/js/tabulator.js"></script>
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<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.js"></script>
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<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js"></script>
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<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js"></script>
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<script type="text/javascript" src="https://unpkg.com/@holoviz/panel@0.13.0-rc.10/dist/panel.min.js"></script>
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<script type="text/javascript">
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Bokeh.set_log_level("info");
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</script>
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css">
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<link rel="stylesheet" href="https://unpkg.com/@holoviz/panel@0.13.0-rc.10/dist/bundled/bootstraptemplate/bootstrap.css">
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<link rel="stylesheet" href="https://unpkg.com/@holoviz/panel@0.13.0-rc.10/dist/bundled/defaulttheme/default.css">
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<style>
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#sidebar {
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width: 350px;
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}
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</style>
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<script src="https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.slim.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/js/bootstrap.bundle.min.js"></script>
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<link rel="stylesheet" href="../build/pyscript.css" />
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<script defer src="../build/pyscript.js"></script>
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</head>
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<body>
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<py-env>
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- bokeh
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- numpy
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- pandas
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- scikit-learn
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</py-env>
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<div class="container-fluid d-flex flex-column vh-100 overflow-hidden" id="container">
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<nav class="navbar navbar-expand-md navbar-dark sticky-top shadow" style="" id="header">
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<button type="button" class="navbar-toggle collapsed" id="sidebarCollapse">
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<span class="navbar-toggler-icon"></span>
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</button>
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<div class="app-header">
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<a class="title" href="/" > Panel</a>
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<span class="title"> -</span>
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<a class="title" href="" > Pyscript KMeans Clustering Demo</a>
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</div>
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</nav>
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<div class="row overflow-hidden" id="content">
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<div class="sidenav" id="sidebar">
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<ul class="nav flex-column">
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<div class="bk-root" id="x-widget" data-root-id="1021"></div>
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<div class="bk-root" id="y-widget" data-root-id="1026"></div>
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<div class="bk-root" id="n-widget" data-root-id="1031"></div>
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</ul>
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</div>
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<div class="col mh-100 float-left" id="main">
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<div class="bk-root" id="intro" data-root-id="1008"></div>
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<div class="bk-root" id="cluster-plot" data-root-id="1009"></div>
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<div class="bk-root" id="table" data-root-id="1009"></div>
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</div>
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</div>
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</div>
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<py-script>
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import asyncio
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import micropip
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from io import StringIO
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from js import fetch
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await micropip.install(['panel==0.13.0rc10', 'altair'])
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import altair as alt
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import panel as pn
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import pandas as pd
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from panel.io.pyodide import show
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from sklearn.cluster import KMeans
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pn.config.sizing_mode = 'stretch_width'
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data = await fetch('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins.csv')
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penguins = pd.read_csv(StringIO(await data.text())).dropna()
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cols = list(penguins.columns)[2:6]
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x = pn.widgets.Select(name='x', options=cols, value='bill_depth_mm')
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y = pn.widgets.Select(name='y', options=cols, value='bill_length_mm')
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n_clusters = pn.widgets.IntSlider(name='n_clusters', start=1, end=5, value=3)
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brush = alt.selection_interval(name='brush') # selection of type "interval"
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def get_clusters(n_clusters):
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kmeans = KMeans(n_clusters=n_clusters)
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est = kmeans.fit(penguins[cols].values)
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df = penguins.copy()
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df['labels'] = est.labels_.astype('str')
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return df
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def get_chart(x, y, df):
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centers = df.groupby('labels').mean()
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return (alt.Chart(df)
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.mark_point(size=100)
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.encode(
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x=alt.X(x, scale=alt.Scale(zero=False)),
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y=alt.Y(y, scale=alt.Scale(zero=False)),
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shape='labels',
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color='species'
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).add_selection(brush).properties(width=800) +
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alt.Chart(centers)
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.mark_point(size=250, shape='cross', color='black')
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.encode(x=x+':Q', y=y+':Q')
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)
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chart = pn.pane.Vega()
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table = pn.widgets.Tabulator(pagination='remote', page_size=10)
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def update_table(event=None):
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table.value = get_clusters(n_clusters.value)
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n_clusters.param.watch(update_table, 'value')
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@pn.depends(x, y, n_clusters, watch=True)
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def update_chart(*events):
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chart.object = get_chart(x.value, y.value, table.value)
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chart.selection.param.watch(update_filters, 'brush')
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def update_filters(event=None):
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filters = []
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for k, v in (getattr(event, 'new') or {}).items():
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filters.append(dict(field=k, type='>=', value=v[0]))
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filters.append(dict(field=k, type='<=', value=v[1]))
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table.filters = filters
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update_table()
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update_chart()
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intro = """
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This app provides an example of **building a simple dashboard using
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Panel**.\n\nIt demonstrates how to take the output of **k-means
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clustering on the Penguins dataset** using scikit-learn,
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parameterizing the number of clusters and the variables to
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plot.\n\nThe plot and the table are linked, i.e. selecting on the plot
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will filter the data in the table.\n\n The **`x` marks the center** of
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the cluster.
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"""
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await show(x, 'x-widget')
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await show(y, 'y-widget')
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await show(n_clusters, 'n-widget')
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await show(intro, 'intro')
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await show(chart, 'cluster-plot')
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await show(table, 'table')
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</py-script>
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<script>
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$(document).ready(function () {
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$('#sidebarCollapse').on('click', function () {
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$('#sidebar').toggleClass('active')
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$(this).toggleClass('active')
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var interval = setInterval(function () { window.dispatchEvent(new Event('resize')); }, 10);
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setTimeout(function () { clearInterval(interval) }, 210)
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});
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});
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</script>
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</body>
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</html>
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