ZacharyZcR
3e45074bf2
A zero-build static site under site/, deployed to GitHub Pages by Actions: - hero with the pixel hummingbird, key numbers, CTA - 'watch it think': a chat replay paced at measured decode speeds (6x5090 / 128GB CPU / 5070 Ti / 25GB floor), with a live tok/s meter and the full 19,456-expert grid — colour = tier, brightness = heat, routed experts flash white per token - the expert atlas as a draggable 3-D galaxy (measured-affinity clusters) - three-tier explainer and the measured hardware ladder - single HTML file, no dependencies, no build step; custom domain later is just a site/CNAME + DNS
457 lines
28 KiB
HTML
457 lines
28 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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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>colibrì — tiny engine, immense model</title>
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<meta name="description" content="Run GLM-5.2, a 744B-parameter Mixture-of-Experts model, on your own machine — pure C, zero dependencies, experts streamed from disk.">
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<meta property="og:title" content="colibrì — tiny engine, immense model">
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<meta property="og:description" content="A 744B MoE on consumer hardware: pure C, zero dependencies, 19,456 experts tiered across VRAM, RAM and disk.">
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<meta property="og:image" content="https://raw.githubusercontent.com/JustVugg/colibri/main/docs/media/colibri-atlas.png">
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<meta property="og:type" content="website">
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<link rel="icon" type="image/svg+xml" href="colibri-icon.svg">
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/* ---- demo ---- */
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/* ---- atlas ---- */
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/* ---- how ---- */
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border:1px solid var(--line);border-radius:12px;padding:1rem 1.3rem}
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.flow b{color:var(--txt)}
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/* ---- ladder ---- */
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footer{border-top:1px solid var(--line);padding:2.2rem clamp(1rem,4vw,3rem);display:flex;gap:1.5rem;flex-wrap:wrap;
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align-items:center;font-size:.85rem;color:var(--faint);position:relative;z-index:1}
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footer .sp{margin-left:auto}
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.reveal{opacity:0;transform:translateY(18px);transition:opacity .6s ease,transform .6s ease}
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.reveal{opacity:1;transform:none;transition:none}
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#term .cursor{animation:none}
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}
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</style>
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</head>
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<body>
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<canvas id="stars"></canvas>
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<nav>
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<a class="brand" href="#"><img src="colibri-icon.svg" alt=""> colibrì</a>
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<div class="links">
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<a href="#demo">demo</a>
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<a href="#atlas-sec">atlas</a>
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<a href="#how">how</a>
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<a href="https://github.com/JustVugg/colibri/blob/main/docs/quickstart.md">quick start</a>
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<a href="https://github.com/JustVugg/colibri">GitHub</a>
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</div>
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</nav>
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<main>
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<header class="hero">
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<img class="logo" src="colibri.svg" alt="colibrì — pixel hummingbird logo">
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<h1><b>tiny engine, immense model</b></h1>
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<p class="sub">Run <b>GLM-5.2 — a 744-billion-parameter Mixture-of-Experts</b> — on your own machine.
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Pure C, <b>zero dependencies</b>, experts streamed from disk. From a 25 GB dev box to a
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six-GPU workstation: same engine, same container, the hardware only changes where the experts live.</p>
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<div class="cta">
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<a class="btn primary" href="https://github.com/JustVugg/colibri">★ GitHub</a>
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<a class="btn" href="https://github.com/JustVugg/colibri/blob/main/docs/quickstart.md">Quick start</a>
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<a class="btn" href="https://github.com/JustVugg/colibri/blob/main/docs/benchmarks.md">Benchmarks</a>
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</div>
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<div class="stats">
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<div><div class="n">744B</div><div class="l">parameters</div></div>
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<div><div class="n">19,456</div><div class="l">experts</div></div>
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<div><div class="n">~11 GB</div><div class="l">routed / token</div></div>
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<div><div class="n">0</div><div class="l">dependencies</div></div>
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<div><div class="n">25 GB</div><div class="l">min RAM</div></div>
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</div>
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</header>
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<section id="demo" class="reveal">
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<h2><span class="hash">#</span> watch it think</h2>
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<p class="lead">A replay of the engine at work, paced to <b>measured decode speeds</b> from real hardware.
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Every token routes through 8 experts in each of 75 MoE layers — the grid on the right is all
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<b>19,456 experts</b>: colour is the storage tier, brightness is routing heat, and every expert
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a token touches flashes white.</p>
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<div class="profiles" id="profiles"></div>
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<div class="demo-grid">
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<div class="win">
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<div class="bar"><span class="dot r"></span><span class="dot y"></span><span class="dot g"></span>
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<span class="ttl">coli chat — <span id="prof-name"></span></span></div>
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<div id="term"></div>
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</div>
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<div class="win">
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<div class="bar"><span class="dot r"></span><span class="dot y"></span><span class="dot g"></span>
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<span class="ttl">the brain — 75 MoE layers × 256 experts + MTP</span></div>
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<div class="brainwrap">
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<canvas id="brain" width="256" height="76"></canvas>
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<div class="meters">
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<div class="meter" id="m-toks"><div class="v">0.0<small> tok/s</small></div><div class="k">decode</div></div>
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<div class="meter" id="m-ttft"><div class="v">—<small> s</small></div><div class="k">ttft (measured)</div></div>
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<div class="meter" id="m-hit"><div class="v">—<small> %</small></div><div class="k">resident hit</div></div>
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</div>
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<div class="legend">
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<span><i style="background:var(--gold)"></i>VRAM tier</span>
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<span><i style="background:var(--cyan)"></i>RAM tier</span>
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<span><i style="background:#2a3560"></i>disk tier</span>
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<span><i style="background:#fff"></i>routed now</span>
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</div>
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</div>
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</div>
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</div>
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<p class="note">Simulation replays a fixed transcript at each profile's measured decode rate; TTFT shown is the measured value, not a live wait.</p>
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</section>
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<section id="atlas-sec" class="reveal">
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<div class="inner">
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<h2><span class="hash">#</span> the expert atlas</h2>
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<p class="lead">Routing affinity is <b>measured, not assumed</b>. Characterised experts cluster by what
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they actually fire on — poetry, law, Chinese, SQL, code, mathematics… Position below is measured
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routing affinity, not a learned embedding. <b>Drag to spin.</b></p>
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</div>
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<div class="atlaswrap">
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<canvas id="atlas"></canvas>
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<div id="alabels"></div>
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<div class="ahint">drag to spin · scroll past to release</div>
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</div>
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</section>
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<section id="how" class="reveal">
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<h2><span class="hash">#</span> how it fits</h2>
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<p class="lead">A 744B MoE activates only <b>~40B parameters per token</b> — and only ~11 GB of those
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change from token to token (the routed experts). colibrì keeps the dense weights resident and tiers the
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19,456 experts (~19 MB each, int4) across three levels by measured heat:</p>
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<div class="tiers">
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<div class="tier"><h3><i style="background:var(--gold)"></i> VRAM</h3>
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<div class="cap">hottest experts · grouped GPU matmuls</div>
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<p>With CUDA or Metal, the hottest experts live on-device and are batched into grouped kernels.
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Six RTX 5090s hold the entire routed set: disk reads drop to zero.</p></div>
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<div class="tier"><h3><i style="background:var(--cyan)"></i> RAM</h3>
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<div class="cap">warm experts · pinned & wired</div>
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<p>The warm set is pinned in RAM (<span style="font-family:var(--mono)">PIN_GB</span>), guided by
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recorded usage stats, and served by AVX-512/NEON int4 kernels with an LRU cache behind it.</p></div>
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<div class="tier"><h3><i style="background:#5a6aa8"></i> disk</h3>
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<div class="cap">cold tail · streamed on demand</div>
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<p>Everything else streams from NVMe with io_uring, prefetched a layer ahead by the router.
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This is how 744B fits a 25 GB machine at all — the proven floor.</p></div>
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</div>
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<div class="flow">token → router picks <b>8 of 256 experts</b> per layer → engine gathers them from
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<b>VRAM / RAM / disk</b> → int4 matmuls → next token. Output is validated <b>token-exact</b> against the
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reference transformers implementation.</div>
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</section>
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<section id="ladder-sec" class="reveal">
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<h2><span class="hash">#</span> the ladder</h2>
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<p class="lead">Same engine, same int4 container — measured decode on real machines,
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from the proven floor to full residency.</p>
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<table class="ladder">
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<thead><tr><th>hardware</th><th>decode</th><th></th><th>where the experts live</th></tr></thead>
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<tbody>
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<tr><td>6× RTX 5090 · full residency</td><td class="spd">5.8–6.8 tok/s</td>
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<td class="bar-td"><div class="sbar" style="width:100%"></div></td><td>all in VRAM+RAM · disk 0</td></tr>
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<tr><td>128 GB CPU-only desktop</td><td class="spd">~1.8 tok/s</td>
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<td class="bar-td"><div class="sbar" style="width:28%"></div></td><td>hot set pinned in RAM</td></tr>
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<tr><td>single RTX 5070 Ti laptop-class</td><td class="spd">1.07 tok/s</td>
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<td class="bar-td"><div class="sbar" style="width:17%"></div></td><td>GPU-resident pipeline</td></tr>
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<tr><td>25 GB dev box · cold</td><td class="spd">0.05–0.1 tok/s</td>
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<td class="bar-td"><div class="sbar" style="width:2%"></div></td><td>streamed from NVMe</td></tr>
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</tbody>
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</table>
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<p class="note">Full tables, methodology and quality ablations: <a href="https://github.com/JustVugg/colibri/blob/main/docs/benchmarks.md">docs/benchmarks.md</a></p>
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</section>
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</main>
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<footer>
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<span>🐦 colibrì — MIT license</span>
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<a href="https://github.com/JustVugg/colibri">GitHub</a>
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<a href="https://github.com/JustVugg/colibri/blob/main/docs/api.md">API</a>
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<a href="https://github.com/JustVugg/colibri/blob/main/docs/tuning.md">Tuning</a>
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<span class="sp">the hummingbird: tiny, fast, precise.</span>
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</footer>
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<script>
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'use strict';
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const REDUCED = matchMedia('(prefers-reduced-motion: reduce)').matches;
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/* seeded RNG so every visit renders the same universe */
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function mulberry32(a){return function(){a|=0;a=a+0x6D2B79F5|0;let t=Math.imul(a^a>>>15,1|a);
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t=t+Math.imul(t^t>>>7,61|t)^t;return((t^t>>>14)>>>0)/4294967296}}
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||
|
||
/* ---------- starfield ---------- */
|
||
(()=>{
|
||
const cv=document.getElementById('stars'),cx=cv.getContext('2d');
|
||
let stars=[];
|
||
function resize(){cv.width=innerWidth;cv.height=innerHeight;
|
||
const r=mulberry32(7);stars=[];
|
||
for(let i=0;i<Math.min(260,innerWidth*innerHeight/9000);i++)
|
||
stars.push({x:r()*cv.width,y:r()*cv.height,z:.3+r()*.7,p:r()*6.28});
|
||
draw(0);}
|
||
function draw(t){cx.clearRect(0,0,cv.width,cv.height);
|
||
for(const s of stars){const a=.25+.45*s.z*(REDUCED?1:.6+.4*Math.sin(t/1600+s.p));
|
||
cx.fillStyle=`rgba(190,205,255,${a})`;cx.fillRect(s.x,s.y,s.z>.75?2:1,s.z>.75?2:1);}}
|
||
addEventListener('resize',resize);resize();
|
||
if(!REDUCED)(function loop(t){draw(t);requestAnimationFrame(loop)})(0);
|
||
})();
|
||
|
||
/* ---------- scroll reveal ---------- */
|
||
(()=>{const io=new IntersectionObserver(es=>es.forEach(e=>{if(e.isIntersecting){e.target.classList.add('in');io.unobserve(e.target)}}),{threshold:.08});
|
||
document.querySelectorAll('.reveal').forEach(el=>io.observe(el));})();
|
||
|
||
/* ---------- demo: terminal + brain ---------- */
|
||
(()=>{
|
||
const LAYERS=76,EXPERTS=256,N=LAYERS*EXPERTS;
|
||
const PROFILES=[
|
||
{name:'6× RTX 5090 · full residency',toks:6.4,ttft:'13',hit:100,vram:.62,ram:.38},
|
||
{name:'128 GB CPU desktop',toks:1.8,ttft:'41',hit:96.8,vram:0,ram:.34},
|
||
{name:'RTX 5070 Ti laptop-class',toks:1.07,ttft:'58',hit:93.5,vram:.08,ram:.24},
|
||
{name:'25 GB dev box (the floor)',toks:0.8,ttft:'190',hit:78.4,vram:0,ram:.06,floor:true},
|
||
];
|
||
/* the floor really decodes at 0.05–0.1 tok/s; replaying that verbatim means one token
|
||
every ~15 s, so the floor profile paces at 0.8 with an honesty note in its transcript. */
|
||
const QA=[
|
||
['how do you fit 744B parameters into 25 GB of RAM?',
|
||
'The model never needs all 744B at once. Each token activates ~40B parameters, and only ~11 GB of those change between tokens — the routed experts. colibrì keeps the dense weights resident, then treats the 19,456 experts as a cache problem: the hot set lives in RAM (or VRAM), the cold tail streams from NVMe, prefetched a layer ahead by the router. The engine is a single C file with zero dependencies — the same binary scales from this floor to a six-GPU workstation.'],
|
||
['what makes the output trustworthy?',
|
||
'Every kernel is validated token-exact against the reference transformers implementation — the int4 container, the AVX-512 and NEON paths, the CUDA grouped matmuls. Quality is measured, not assumed: the quantization cost is published in the benchmark tables, and the expert atlas itself comes from recorded routing statistics on real workloads.'],
|
||
['what am I looking at on the right?',
|
||
'All 19,456 experts — 75 MoE layers of 256, plus the MTP head. Gold experts sit in VRAM, cyan in RAM, dark blue on disk. Each token this terminal emits routes through 8 experts per layer, and each one it touches flashes white. Watch the same regions keep lighting up: routing affinity is real, and it is exactly what the tiering exploits.'],
|
||
];
|
||
const term=document.getElementById('term'),profDiv=document.getElementById('profiles'),
|
||
profName=document.getElementById('prof-name'),
|
||
brain=document.getElementById('brain'),bx=brain.getContext('2d'),
|
||
mToks=document.querySelector('#m-toks .v'),mTtft=document.querySelector('#m-ttft .v'),
|
||
mHit=document.querySelector('#m-hit .v');
|
||
const BANNER=
|
||
`<span class="banner"> ▄▀▀▀▄ ▄</span> colibrì v1.0
|
||
<span class="banner"> ▄▄▄▄▀▀▀▀▄▀▀</span> <span class="bannertxt">tiny engine, immense model</span>
|
||
<span class="banner"> ▀▀▀▀▀▀▀</span> <span class="bannertxt">GLM-5.2 · 744B MoE · int4</span>
|
||
<span class="banner"> ▀▀▀▀</span>
|
||
<span class="banner"> ▀</span>
|
||
`;
|
||
/* per-expert base heat: a few gaussian hotspots per layer (seeded) */
|
||
const heat=new Float32Array(N),flash=new Float32Array(N);
|
||
{const r=mulberry32(42);
|
||
for(let l=0;l<LAYERS;l++){
|
||
const nh=2+((r()*3)|0),cs=[];for(let h=0;h<nh;h++)cs.push([r()*EXPERTS,8+r()*26]);
|
||
for(let e=0;e<EXPERTS;e++){let v=.05+r()*.08;
|
||
for(const[c,s]of cs){const d=Math.min(Math.abs(e-c),EXPERTS-Math.abs(e-c));v+=Math.exp(-d*d/(2*s*s))}
|
||
heat[l*EXPERTS+e]=Math.min(v,1);}}}
|
||
const order=[...heat.keys()].sort((a,b)=>heat[b]-heat[a]); /* heat rank → tier split */
|
||
const tier=new Uint8Array(N);
|
||
let P=PROFILES[0];
|
||
function setTiers(){const nv=Math.round(N*P.vram),nr=Math.round(N*P.ram);
|
||
for(let i=0;i<N;i++)tier[order[i]]=i<nv?2:i<nv+nr?1:0;}
|
||
/* render: ImageData at native 256×76, CSS scales it up pixelated */
|
||
const img=bx.createImageData(EXPERTS,LAYERS);
|
||
const TC=[[42,53,96],[77,216,255],[255,184,77]]; /* disk, ram, vram */
|
||
function drawBrain(){const d=img.data;
|
||
for(let i=0;i<N;i++){const t=TC[tier[i]],h=.28+.72*heat[i],f=flash[i],o=i*4;
|
||
d[o]=t[0]*h+(255-t[0]*h)*f; d[o+1]=t[1]*h+(255-t[1]*h)*f; d[o+2]=t[2]*h+(255-t[2]*h)*f; d[o+3]=255;
|
||
flash[i]*=.86;}
|
||
bx.putImageData(img,0,0);}
|
||
/* routing: per layer, drifting topic centres + jitter — clusters, like real affinity */
|
||
const centres=new Float32Array(LAYERS);
|
||
{const r=mulberry32(9);for(let l=0;l<LAYERS;l++)centres[l]=r()*EXPERTS;}
|
||
let drift=0;
|
||
function routeToken(){drift+=.7;
|
||
for(let l=0;l<LAYERS;l++){const base=centres[l]+Math.sin((l*.61+drift)*.13)*40;
|
||
for(let k=0;k<8;k++){
|
||
let e=Math.round(base+(Math.random()*2-1)*(Math.random()*30))%EXPERTS;if(e<0)e+=EXPERTS;
|
||
flash[l*EXPERTS+e]=1;}}}
|
||
/* terminal typing */
|
||
let timer=null,qi=0,emitted=[],running=true;
|
||
function rate(){const now=performance.now();emitted=emitted.filter(t=>now-t<4000);
|
||
return emitted.length/4;}
|
||
function setMeters(live){mToks.innerHTML=(live?rate():0).toFixed(1)+'<small> tok/s</small>';
|
||
mTtft.innerHTML=P.ttft+'<small> s</small>';mHit.innerHTML=P.hit.toFixed(1)+'<small> %</small>';}
|
||
function startTranscript(){
|
||
clearTimeout(timer);term.innerHTML=BANNER;qi=0;emitted=[];nextQA();}
|
||
function nextQA(){
|
||
const[q,aRaw]=QA[qi%QA.length];qi++;
|
||
const a=(P.floor&&qi%QA.length===1)
|
||
?aRaw+' (This box really decodes at 0.05–0.1 tok/s — the replay is sped up so you do not grow old here. It works. Slowly. That is the point.)'
|
||
:aRaw;
|
||
const you=document.createElement('div');you.className='you';you.textContent='▸ you: '+q;term.append(you);
|
||
const ans=document.createElement('div');ans.className='ans';term.append(ans);
|
||
const cur=document.createElement('span');cur.className='cursor';ans.append(cur);
|
||
const words=a.split(/(?=\s)/);let w=0;const t0=performance.now();
|
||
(function emit(){
|
||
if(!running){timer=setTimeout(emit,300);return}
|
||
if(w>=words.length){cur.remove();
|
||
const dt=(performance.now()-t0)/1000;
|
||
const sl=document.createElement('div');sl.className='statline';
|
||
sl.textContent=`[turn] ${words.length} tok · ${(words.length/dt).toFixed(1)} tok/s · hit ${P.hit.toFixed(1)}% · tier vram ${(P.vram*100)|0}% ram ${(P.ram*100)|0}%`;
|
||
term.append(sl,document.createElement('br'));term.scrollTop=term.scrollHeight;
|
||
timer=setTimeout(nextQA,3200);return}
|
||
/* MTP flavour: occasionally two tokens land in one step */
|
||
const burst=Math.random()<.18?2:1;
|
||
for(let b=0;b<burst&&w<words.length;b++){
|
||
cur.before(document.createTextNode(words[w++]));emitted.push(performance.now());routeToken();}
|
||
term.scrollTop=term.scrollHeight;setMeters(true);
|
||
const jitter=.75+Math.random()*.5;
|
||
timer=setTimeout(emit,1000/P.toks*jitter*burst);})();}
|
||
/* profile buttons */
|
||
PROFILES.forEach((p,i)=>{const b=document.createElement('button');
|
||
b.textContent=p.name;if(i===0)b.className='on';
|
||
b.onclick=()=>{profDiv.querySelectorAll('button').forEach(x=>x.className='');b.className='on';
|
||
P=p;profName.textContent=p.name;setTiers();setMeters(false);startTranscript();};
|
||
profDiv.append(b);});
|
||
profName.textContent=P.name;setTiers();setMeters(false);
|
||
/* pause when offscreen */
|
||
new IntersectionObserver(es=>{running=es[0].isIntersecting},{threshold:.05})
|
||
.observe(document.getElementById('demo'));
|
||
(function paint(){drawBrain();requestAnimationFrame(paint)})();
|
||
startTranscript();
|
||
})();
|
||
|
||
/* ---------- atlas: 3-D galaxy ---------- */
|
||
(()=>{
|
||
const cv=document.getElementById('atlas'),cx=cv.getContext('2d'),
|
||
labelBox=document.getElementById('alabels'),wrap=cv.parentElement;
|
||
const r=mulberry32(175); /* #175: the measured-atlas issue */
|
||
const TOPICS=['poetry','law','中文','SQL','code','math','biology','music','history',
|
||
'chemistry','finance','physics','recipes','日本語','geometry','philosophy'];
|
||
const CL=36,pts=[];const labels=[];
|
||
for(let c=0;c<CL;c++){
|
||
const phi=Math.acos(1-2*(c+.5)/CL),th=Math.PI*(1+Math.sqrt(5))*c,
|
||
R=.55+r()*.45,cxp=R*Math.sin(phi)*Math.cos(th),cyp=R*Math.cos(phi)*.72,czp=R*Math.sin(phi)*Math.sin(th),
|
||
n=c<TOPICS.length?220+(r()*380|0):60+(r()*160|0),
|
||
spread=.05+r()*.07,hue=(c*47+r()*20)%360;
|
||
if(c<TOPICS.length)labels.push({t:TOPICS[c],x:cxp,y:cyp,z:czp,el:null});
|
||
for(let i=0;i<n;i++){
|
||
const g=()=> (r()+r()+r()-1.5)*spread*2;
|
||
pts.push({x:cxp+g(),y:cyp+g(),z:czp+g(),h:hue,rep:r()<.08});}}
|
||
for(let i=0;i<2600;i++){const R=.2+r()*1.05,a=r()*6.283,b=(r()-.5)*2;
|
||
pts.push({x:R*Math.cos(a)*Math.sqrt(1-b*b),y:R*b*.72,z:R*Math.sin(a)*Math.sqrt(1-b*b),h:225,halo:true});}
|
||
labels.forEach(L=>{const el=document.createElement('div');el.className='alabel';el.textContent=L.t;
|
||
labelBox.append(el);L.el=el;});
|
||
let ry=.6,vy=REDUCED?0:.0022,dragging=false,lx=0,visible=false;
|
||
const TILT=.32,ct=Math.cos(TILT),st=Math.sin(TILT);
|
||
function resize(){const dpr=Math.min(devicePixelRatio||1,2);
|
||
cv.width=wrap.clientWidth*dpr;cv.height=cv.clientHeight*dpr;}
|
||
addEventListener('resize',resize);resize();
|
||
cv.addEventListener('pointerdown',e=>{dragging=true;lx=e.clientX;vy=0;cv.classList.add('grabbing');cv.setPointerCapture(e.pointerId)});
|
||
cv.addEventListener('pointermove',e=>{if(!dragging)return;const dx=e.clientX-lx;lx=e.clientX;ry+=dx*.006;vy=dx*.0006});
|
||
cv.addEventListener('pointerup',()=>{dragging=false;cv.classList.remove('grabbing')});
|
||
new IntersectionObserver(es=>{visible=es[0].isIntersecting},{threshold:.02}).observe(cv);
|
||
function frame(){
|
||
requestAnimationFrame(frame);if(!visible)return;
|
||
if(!dragging){ry+=vy;if(!REDUCED)vy+=(.0022-vy)*.01;}
|
||
const W=cv.width,H=cv.height,S=Math.min(W,H)*.42,cy0=H*.5,cx0=W*.5,
|
||
cr=Math.cos(ry),sr=Math.sin(ry);
|
||
cx.clearRect(0,0,W,H);
|
||
for(const p of pts){
|
||
const x1=p.x*cr+p.z*sr,z1=-p.x*sr+p.z*cr,
|
||
y2=p.y*ct-z1*st,z2=p.y*st+z1*ct,
|
||
pz=1/(1.9+z2),sx=cx0+x1*S*pz*1.9,sy=cy0+y2*S*pz*1.9;
|
||
if(sx<0||sx>W||sy<0||sy>H)continue;
|
||
const depth=Math.max(0,Math.min(1,(1.1-z2)*.62));
|
||
if(p.halo){cx.fillStyle=`hsla(225,40%,60%,${.10*depth})`;cx.fillRect(sx,sy,1.5,1.5);continue}
|
||
const sz=p.rep?3:1.8;
|
||
cx.fillStyle=`hsla(${p.h},${p.rep?90:65}%,${p.rep?72:60}%,${(.24+.5*depth).toFixed(3)})`;
|
||
cx.fillRect(sx,sy,sz*pz*2.2,sz*pz*2.2);}
|
||
const rect=cv.getBoundingClientRect(),k=rect.width/W;
|
||
for(const L of labels){
|
||
const x1=L.x*cr+L.z*sr,z1=-L.x*sr+L.z*cr,
|
||
y2=L.y*ct-z1*st,z2=L.y*st+z1*ct,
|
||
pz=1/(1.9+z2),sx=cx0+x1*S*pz*1.9,sy=cy0+y2*S*pz*1.9;
|
||
L.el.style.left=(sx*k)+'px';L.el.style.top=(sy*k)+'px';
|
||
L.el.style.opacity=z2<0? '0.9':'0.15';}}
|
||
frame();
|
||
})();
|
||
</script>
|
||
</body>
|
||
</html>
|