Published
Edited
Jan 30, 2020
md`# Probe Similarity small multiples`
probesFiltered = Object.assign({ prb_ids: psMeta.filter(prbFilterFn).map(p => p.id) });
sims = fetch(`https://8100.ripe.net/probes?limit=0`, {
method: 'POST',
headers: {'Content-Type': 'application/json'
},
mode: 'cors',
body: JSON.stringify(probesFiltered)
})
.then(res => res.json());
ss = sims.flatMap(r => {
let facet = psMeta2.find(m => m.id === r.id)
facet = facet && facet["closest_city150"][facetParm]
return r.result.map(res => {
return (
{ ...res, src_prb_id: r.id, facet: facet })
})
});
psMeta2 = await FileAttachment("active_probes_per_city_jan_2020.json").json();
psMeta = fetch("https://atlas.ripe.net/api/v2/probes/archive/?status=1").then(d => d.json()).then(d => d.results);
bb = vl.markLine({interpolate: 'step-before', opacity: 1.0, strokeWidth: 0.8})
.encode(
vl.y().fieldQ("similarity").title('Similarity (jaccard index)'),
vl.x().fieldQ('Cumulative_Sum').title('Cumulative Count of Probes'),
vl.color().fieldN('facet').title(null),
vl.color().fieldN('src_prb_id').title(null)
)
.width(180)
.height(180)
.facet({column: vl.field("facet"), columns: 2})
.data(ss)
.title('probe similarities')
.transform(
vl.groupby(["similarity", "src_prb_id", "facet"]).aggregate(vl.count().as('Count')),
vl.window(vl.sum('Count').as('Cumulative_Sum')).groupby(['src_prb_id']).sort(vl.field("similarity")),
vl.groupby("facet")
)
prbFilterFn = p => p.country_code === 'NL' && (p.asn_v4 === 1136 || p.asn_v4 === 33915 || p.asn_v4 === 6830 || p.asn_v4 === 3265)
facetParm = 'countryState'
probesFiltered.prb_ids.length
import {vl} from '@vega/vega-lite-api'
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Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
