How to use vega-statistics - 10 common examples
To help you get started, we’ve selected a few vega-statistics examples, based on popular ways it is used in public projects.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
domoritz / line-density / demo / demo.ts View on Github 
let canvas;
document.getElementById("regl").innerText = "";
if ((document.getElementById("debug") as HTMLInputElement).checked) {
canvas = document.createElement("canvas");
document.getElementById("regl").appendChild(canvas);
}
const maxY = (data.data as Float32Array).reduce(
(agg, val) => Math.max(agg, val),
0
);
// compute nice bin boundaries
const binConfigX = bin({ maxbins: binsx, extent: [0, points - 1] });
const binConfigY = bin({ maxbins: binsy, extent: [0, maxY] });
start = Date.now();
compute(data, binConfigX, binConfigY, canvas).then(heatmapData => {
document.getElementById("computetime").innerText = `${(Date.now() - start) /
1000} seconds`;
heatmap(heatmapData, binConfigX, binConfigY);
});
}extent.push(extent[0] === 1 ? extent[0] + 0.1 : extent[0] + 1);
}
// the performance distribution is always [0, 1]
const binIdxes = d3.range(0, this.binCount - 2);
const binFunction = d3.scaleQuantile()
.domain(d3.merge([
this.flatCounts.correct.concat(this.flatCountsRewrite.correct) as number[],
this.flatCounts.incorrect.concat(this.flatCountsRewrite.incorrect) as number[]]))
.range(binIdxes);
this.scale.attrScale_continue.domain(extent);
let bins = d3.merge([
[extent[0]],binFunction.quantiles(),
[extent[1]]]).filter(utils.uniques) as number[];
if ( (bins[bins.length-1] - bins[0] <= 1) || bins.length < this.binCount * 0.6) {
const bins_ = vegaStat.bin({extent: extent, nice: false, maxbins: this.binCount });
this.scale.bins = d3.range(bins_.start, bins_.stop + bins_.step, bins_.step);
} else {
//bins.push(extent[1] === 1 ? extent[1] + 0.1 : extent[1] + 1);
this.scale.bins = bins;
}
} else {
this.scale.bins = this.domain.slice();//this.attr.stats.map(s => s.value);
this.scale.attrScale_discrete.domain(this.scale.bins as string[]);
}
/*
if (this.props.attr.name === "groundtruths_length") {
this.scale.attrScale_continue.domain([1, 20]);
this.scale.bins = [1, 2, 3, 4, 5, 6, 7, 8, 20]
}
if (this.props.attr.name === "prediction_length") {
this.scale.attrScale_continue.domain([1, 50]);lineChart(data);
let canvas;
document.getElementById("regl").innerText = "";
if ((document.getElementById("debug") as HTMLInputElement).checked) {
canvas = document.createElement("canvas");
document.getElementById("regl").appendChild(canvas);
}
const maxY = (data.data as Float32Array).reduce(
(agg, val) => Math.max(agg, val),
0
);
// compute nice bin boundaries
const binConfigX = bin({ maxbins: binsx, extent: [0, points - 1] });
const binConfigY = bin({ maxbins: binsy, extent: [0, maxY] });
start = Date.now();
compute(data, binConfigX, binConfigY, canvas).then(heatmapData => {
document.getElementById("computetime").innerText = `${(Date.now() - start) /
1000} seconds`;
heatmap(heatmapData, binConfigX, binConfigY);
});
}add = p => {
const t = {};
for (let i=0; i add([x, model.predict(x)]));
} else {
// otherwise return trend line sample points
sampleCurve(model.predict, dom, 25, 200).forEach(add);
}
});const source = pulse.materialize(pulse.SOURCE).source,
groups = partition(pulse.source, _.groupby, identity),
smooth = _.smooth || false,
field = _.field,
step = _.step || autostep(source, field),
sort = stableCompare((a, b) => field(a) - field(b)),
as = _.as || Output,
n = groups.length;
// compute dotplot bins per group
let min = Infinity, max = -Infinity, i = 0, j;
for (; i max) max = v;
g[++j][as] = v;
}
}
this.value = {
start: min,
stop: max,
step: step
};
return pulse.reflow(true).modifies(as);
};function update(t) {
var p, idx;
if (res.length < num) {
res.push(t);
} else {
idx = ~~((cnt + 1) * random());
if (idx < res.length && idx >= cap) {
p = res[idx];
if (map[tupleid(p)]) out.rem.push(p); // eviction
res[idx] = t;
}
}
++cnt;
}prototype._bins = function(_) {
if (this.value && !_.modified()) {
return this.value;
}
var field = _.field,
bins = bin(_),
start = bins.start,
stop = bins.stop,
step = bins.step,
a, d;
if ((a = _.anchor) != null) {
d = a - (start + step * Math.floor((a - start) / step));
start += d;
stop += d;
}
var f = function(t) {
var v = field(t);
if (v == null) {
return null;
} else {prototype._bins = function(_) {
if (this.value && !_.modified()) {
return this.value;
}
var field = _.field,
bins = bin(_),
step = bins.step,
start = bins.start,
stop = start + Math.ceil((bins.stop - start) / step) * step,
a, d;
if ((a = _.anchor) != null) {
d = a - (start + step * Math.floor((a - start) / step));
start += d;
stop += d;
}
var f = function(t) {
var v = field(t);
return v == null ? null
: v < start ? -Infinity
: v > stop ? +Infinitygroups.forEach(g => {
const density = randomKDE(g, bandwidth)[method],
scale = _.counts ? g.length : 1,
local = domain || extent(g);
sampleCurve(density, local, minsteps, maxsteps).forEach(v => {
const t = {};
for (let i=0; ivar dist = parseDist(_.distribution, source(pulse)),
minsteps = _.steps || _.minsteps || 25,
maxsteps = _.steps || _.maxsteps || 200,
method = _.method || 'pdf';
if (method !== 'pdf' && method !== 'cdf') {
error('Invalid density method: ' + method);
}
if (!_.extent && !dist.data) {
error('Missing density extent parameter.');
}
method = dist[method];
var as = _.as || ['value', 'density'],
domain = _.extent || extent(dist.data()),
values = sampleCurve(method, domain, minsteps, maxsteps).map(v => {
var tuple = {};
tuple[as[0]] = v[0];
tuple[as[1]] = v[1];
return ingest(tuple);
});
if (this.value) out.rem = this.value;
this.value = out.add = out.source = values;
}
return out;
};vega-statistics
Statistical routines and probability distributions.
