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"""
Build a static interactive explorer for the single Xperience-10M sample episode.
The explorer is generated from committed/exported artifacts only. Raw MP4/HDF5
files are not embedded or redistributed.
"""
from __future__ import annotations
import argparse
import csv
import json
from datetime import datetime, timezone
from pathlib import Path
import numpy as np
TASK_DISPLAY = {
"timeline_action": "Current Action Recognition",
"timeline_subtask": "Current Subtask Recognition",
"transition_detection": "Action Transition Detection",
"next_action": "Next-Action Prediction",
"contact_prediction": "Contact State Prediction",
"object_relevance": "Relevant Object Prediction",
}
BLOCK_DISPLAY = {
"hand_left_joints": "Left Hand",
"hand_right_joints": "Right Hand",
"body_joints": "Body Joints",
"body_contacts": "Body Contacts",
"camera_translation": "Camera Translation",
"camera_rotation_matrix": "Camera Rotation",
"imu_accel_gyro": "IMU Accel/Gyro",
"depth_confidence": "Depth + Confidence",
"audio_fisheye_cam0_aac": "Audio",
"caption_objects_interaction_text": "Language Text",
"slam_point_cloud": "SLAM Point Cloud",
"calibration": "Calibration",
}
def parse_args() -> argparse.Namespace:
root = Path(__file__).resolve().parents[1]
parser = argparse.ArgumentParser(description="Build static single-episode explorer page.")
parser.add_argument("--workspace", type=Path, default=root)
parser.add_argument("--suite-dir", type=Path, default=root / "results/episode_task_suite")
parser.add_argument("--diagnostics-dir", type=Path, default=root / "results/single_episode_diagnostics")
parser.add_argument("--docs-dir", type=Path, default=root / "docs")
return parser.parse_args()
def read_csv(path: Path) -> list[dict]:
with path.open(newline="", encoding="utf-8") as fp:
return list(csv.DictReader(fp))
def read_json(path: Path):
return json.loads(path.read_text(encoding="utf-8"))
def write_json(path: Path, data: dict) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8")
def block_modality(name: str) -> str:
if name.startswith("video_"):
return "video"
if name.startswith("hand_") or name.startswith("body_"):
return "motion_capture"
if name.startswith("camera_") or name in {"slam_point_cloud", "calibration"}:
return "pose_slam"
if name.startswith("depth_"):
return "depth"
if name.startswith("imu_"):
return "inertial"
if name.startswith("audio_"):
return "audio"
if name.startswith("caption_"):
return "language"
return "other"
def load_predictions(suite_dir: Path) -> dict[str, dict[int, dict]]:
out: dict[str, dict[int, dict]] = {}
for task in TASK_DISPLAY:
path = suite_dir / task / "predictions.csv"
rows_by_window: dict[int, dict] = {}
if not path.exists():
out[task] = rows_by_window
continue
for row in read_csv(path):
if "window_index" not in row:
continue
idx = int(row["window_index"])
true_value = row.get("true_label") or row.get("true_objects") or row.get("true") or ""
pred_value = row.get("predicted_label") or row.get("predicted_objects") or row.get("predicted") or ""
if "correct" in row and row["correct"] != "":
correct = int(float(row["correct"]))
else:
correct = int(str(true_value) == str(pred_value))
rows_by_window[idx] = {
"true": true_value,
"predicted": pred_value,
"correct": correct,
"confidence": row.get("confidence", ""),
}
out[task] = rows_by_window
return out
def build_action_segments(windows: list[dict]) -> list[dict]:
segments = []
if not windows:
return segments
current = windows[0]["action_label"]
start = int(windows[0]["start_frame"])
start_idx = int(windows[0]["window_index"])
last = windows[0]
for row in windows[1:]:
if row["action_label"] != current:
segments.append({
"action": current,
"start_frame": start,
"end_frame": int(last["end_frame"]),
"start_window": start_idx,
"end_window": int(last["window_index"]),
})
current = row["action_label"]
start = int(row["start_frame"])
start_idx = int(row["window_index"])
last = row
segments.append({
"action": current,
"start_frame": start,
"end_frame": int(last["end_frame"]),
"start_window": start_idx,
"end_window": int(last["window_index"]),
})
return segments
def build_data(args: argparse.Namespace) -> dict:
suite_dir = args.suite_dir
diagnostics_dir = args.diagnostics_dir
windows = read_csv(suite_dir / "windows.csv")
manifest = read_json(suite_dir / "feature_manifest.json")
summary = read_json(suite_dir / "summary_report.json")
provenance = read_json(diagnostics_dir / "provenance.json")
object_rows = {int(r["window_index"]): r for r in read_csv(diagnostics_dir / "object_labels/window_object_labels.csv")}
ablation_rows = read_csv(diagnostics_dir / "modality_ablation/ablation_metrics.csv")
alignment_rows = read_csv(diagnostics_dir / "alignment_stress/alignment_shift_metrics.csv")
timeline_rows = read_csv(diagnostics_dir / "timeline_overlay/timeline_overlay.csv")
predictions = load_predictions(suite_dir)
X = np.load(suite_dir / "shared_windows.npz")["X"].astype(np.float32)
block_stats = {}
block_meta = []
for block in manifest:
name = block["name"]
start, end = int(block["start"]), int(block["end"])
values = X[:, start:end]
l2 = np.linalg.norm(values, axis=1)
mean_abs = np.mean(np.abs(values), axis=1)
max_l2 = float(max(l2.max(), 1e-8))
block_stats[name] = {
"l2": l2,
"mean_abs": mean_abs,
"relative": l2 / max_l2,
}
block_meta.append({
"name": name,
"display": BLOCK_DISPLAY.get(name, name.replace("_", " ").title()),
"modality": block_modality(name),
"start": start,
"end": end,
"dim": int(block["dim"]),
})
explorer_windows = []
for i, row in enumerate(windows):
idx = int(row["window_index"])
obj = object_rows.get(idx, {})
feature_stats = []
for block in block_meta:
s = block_stats[block["name"]]
feature_stats.append({
"name": block["name"],
"l2": round(float(s["l2"][i]), 6),
"mean_abs": round(float(s["mean_abs"][i]), 6),
"relative": round(float(s["relative"][i]), 6),
})
task_predictions = {}
for task, rows_by_window in predictions.items():
task_predictions[task] = rows_by_window.get(idx)
explorer_windows.append({
"window_index": idx,
"start_frame": int(row["start_frame"]),
"end_frame": int(row["end_frame"]),
"center_frame": int(row["center_frame"]),
"action": row["action_label"],
"subtask": row["subtask_label"],
"objects": [x for x in obj.get("objects", "").split("|") if x],
"feature_stats": feature_stats,
"predictions": task_predictions,
})
best_ablation = {}
for task in sorted({r["task"] for r in ablation_rows}):
computed = [r for r in ablation_rows if r["task"] == task and r["status"] == "computed" and r["score"]]
if not computed:
continue
best = max(computed, key=lambda r: float(r["score"]))
non_overlap = [r for r in computed if r.get("target_source_overlap") == "false"]
best_non_overlap = max(non_overlap, key=lambda r: float(r["score"])) if non_overlap else None
best_ablation[task] = {
"best": {
"modality_group": best["modality_group"],
"modality_display": best["modality_display"],
"score": float(best["score"]),
"primary_metric": best["primary_metric"],
"target_source_overlap": best["target_source_overlap"],
},
"best_non_overlap": None if best_non_overlap is None else {
"modality_group": best_non_overlap["modality_group"],
"modality_display": best_non_overlap["modality_display"],
"score": float(best_non_overlap["score"]),
"primary_metric": best_non_overlap["primary_metric"],
},
}
return {
"meta": {
"generated_at": datetime.now(timezone.utc).isoformat(),
"window_count": len(explorer_windows),
"feature_dim": int(X.shape[1]),
"object_label_rows": len(object_rows),
"object_vocab_count": len(read_json(diagnostics_dir / "object_labels/object_vocab.json")["vocab"]),
"timeline_prediction_rows": len(timeline_rows),
"source_policy": "Window-level labels, features, predictions, and diagnostics only. Raw Xperience-10M MP4/HDF5/RRD files are not embedded.",
"annotation_hash_recorded": any("annotation.hdf5" in key for key in provenance["input_file_hashes"]),
"summary": {
"num_windows": summary.get("num_windows"),
"feature_dim": summary.get("feature_dim"),
"window_frames": summary.get("window_frames"),
"stride_frames": summary.get("stride_frames"),
},
},
"tasks": TASK_DISPLAY,
"feature_blocks": block_meta,
"segments": build_action_segments(windows),
"windows": explorer_windows,
"ablation": {
"best_by_task": best_ablation,
"rows": ablation_rows,
},
"alignment": alignment_rows,
}
HTML_TEMPLATE = """<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Single-Episode Explorer | Ropedia Xperience-10M</title>
<meta name="description" content="Interactive window-level explorer for the Ropedia Xperience-10M single-episode diagnostics.">
<meta name="theme-color" content="#020502">
<link rel="icon" href="favicon.png" type="image/png" sizes="64x64">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter+Tight:wght@400;500;600;700&family=Space+Grotesk:wght@400;500;600;700&display=swap" rel="stylesheet">
<style>
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.wrap { width:min(var(--max), calc(100% - 42px)); margin:0 auto; }
header { position:sticky; top:0; z-index:10; background:rgba(2,5,2,.92); backdrop-filter:blur(16px); border-bottom:1px solid var(--soft); }
.nav { height:64px; display:flex; align-items:center; justify-content:space-between; gap:18px; }
.brand { display:flex; gap:11px; align-items:center; text-decoration:none; font-family:var(--font-ui); font-weight:700; }
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.nav-links a:hover { border-color:var(--green); color:var(--green); background:rgba(255,255,255,.08); }
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.stat strong { display:block; font-family:var(--font-ui); font-size:24px; line-height:1; }
.stat span { display:block; margin-top:6px; color:var(--muted); font-size:12px; }
main { padding:26px 0 70px; }
.shell { display:grid; grid-template-columns:330px minmax(0,1fr); gap:18px; align-items:start; }
.panel { border:1px solid rgba(204,255,160,.18); border-radius:8px; background:var(--card); box-shadow:0 18px 48px rgba(0,0,0,.32); }
.side { position:sticky; top:84px; padding:18px; }
label { display:block; color:var(--muted); font-size:12px; font-family:var(--font-btn); font-weight:700; margin:14px 0 7px; }
input[type=range] { width:100%; accent-color:var(--green); }
select, input[type=search] { width:100%; min-height:40px; border:1px solid var(--soft); border-radius:999px; background:#020802; color:var(--ink); padding:9px 12px; font:inherit; }
.button-row { display:grid; grid-template-columns:1fr 1fr; gap:8px; margin-top:12px; }
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button:hover { border-color:var(--green); color:var(--green); background:rgba(255,255,255,.08); }
.timeline { padding:18px; margin-bottom:18px; }
.timeline-strip { position:relative; height:60px; border:1px solid var(--soft); border-radius:8px; overflow:hidden; background:#030803; }
.segment { position:absolute; top:0; bottom:0; border-right:1px solid rgba(2,5,2,.45); opacity:.92; }
.marker { position:absolute; top:0; bottom:0; width:3px; background:var(--ink); box-shadow:0 0 0 2px rgba(2,5,2,.9), 0 0 18px rgba(204, 255, 160,.6); }
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.pred h3 { margin:0 0 8px; font-family:var(--font-ui); font-size:15px; }
.pred p { margin:4px 0; color:#cdd8c8; font-size:13px; overflow-wrap:anywhere; }
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.row strong { color:var(--green); font-variant-numeric:tabular-nums; }
.note { margin-top:12px; color:var(--muted); font-size:12px; line-height:1.55; }
@media (max-width: 980px) { .shell,.analysis-grid { grid-template-columns:1fr; } .side { position:static; } .pred-grid,.feature-grid,.stats { grid-template-columns:1fr; } .window-head { grid-template-columns:1fr; } .nav-links { display:none; } }
</style>
</head>
<body>
<header>
<div class="wrap nav">
<a class="brand" href="index.html"><img src="assets/brand/xperience10m-logo-mark-192.png" alt=""><span>Ropedia Xperience-10M</span></a>
<nav class="nav-links"><a href="index.html">Project</a><a href="single_episode_explorer.html">Explorer</a><a href="data/single_episode_explorer.json">Data JSON</a></nav>
</div>
</header>
<section class="hero">
<div class="wrap">
<h1>Single-Episode Research Explorer</h1>
<p>Inspect the exported Xperience-10M sample windows, real object labels, model predictions, feature-block statistics, and diagnostic scores from one aligned episode.</p>
<div class="stats">
<div class="stat"><strong id="statWindows">-</strong><span>windows</span></div>
<div class="stat"><strong id="statDim">-</strong><span>feature dimensions</span></div>
<div class="stat"><strong id="statObjects">-</strong><span>object labels</span></div>
<div class="stat"><strong id="statPreds">-</strong><span>prediction rows</span></div>
</div>
</div>
</section>
<main>
<div class="wrap shell">
<aside class="panel side">
<label for="windowRange">Window</label>
<input id="windowRange" type="range" min="0" max="0" value="0">
<div class="button-row"><button id="prevWindow" type="button">Previous</button><button id="nextWindow" type="button">Next</button></div>
<label for="taskSelect">Task Focus</label>
<select id="taskSelect"></select>
<label for="searchBox">Search Action or Object</label>
<input id="searchBox" type="search" placeholder="e.g. Pour coffee, kettle">
<div class="button-row"><button id="firstMatch" type="button">First Match</button><button id="firstPred" type="button">First Predicted</button></div>
<p class="note">The page uses window-level exported artifacts only. Raw video, raw HDF5, and RRD assets are not embedded.</p>
</aside>
<section class="content">
<div class="panel timeline">
<div class="timeline-strip" id="timelineStrip"></div>
<div class="timeline-meta"><span id="timelineLeft"></span><span id="timelineRight"></span></div>
</div>
<section class="panel window-panel">
<div class="window-head">
<div>
<h2 id="windowTitle">Window</h2>
<p id="windowSubtitle" class="subtle"></p>
<div class="chips" id="objectChips"></div>
</div>
<div class="frame-pill" id="framePill"></div>
</div>
<div class="grid pred-grid" id="predictionGrid"></div>
</section>
<section class="analysis-grid">
<div class="panel analysis">
<h3>Feature Blocks</h3>
<div class="grid feature-grid" id="featureGrid"></div>
</div>
<div class="panel analysis">
<h3>Diagnostics</h3>
<div class="rows" id="diagnosticRows"></div>
<p class="note" id="diagnosticNote"></p>
</div>
</section>
</section>
</div>
</main>
<script id="explorer-data" type="application/json">__DATA__</script>
<script>
const DATA = JSON.parse(document.getElementById("explorer-data").textContent);
function hasPrediction(windowRecord, taskKey) {
return taskKey === "all" ? Object.values(windowRecord.predictions).some(Boolean) : Boolean(windowRecord.predictions[taskKey]);
}
function defaultWindowIndex() {
let best = 0;
let bestCount = -1;
DATA.windows.forEach((w) => {
const count = Object.values(w.predictions).filter(Boolean).length;
if (count > bestCount) { best = w.window_index; bestCount = count; }
});
return best;
}
const state = { index: defaultWindowIndex(), task: "all" };
const range = document.getElementById("windowRange");
const taskSelect = document.getElementById("taskSelect");
const searchBox = document.getElementById("searchBox");
const colors = ["#5ccf7d", "#7ae5c3", "#9bdfff", "#d8f4a5", "#f0a45e", "#cba8ff", "#ff8f7a"];
document.getElementById("statWindows").textContent = DATA.meta.window_count;
document.getElementById("statDim").textContent = DATA.meta.feature_dim;
document.getElementById("statObjects").textContent = DATA.meta.object_vocab_count;
document.getElementById("statPreds").textContent = DATA.meta.timeline_prediction_rows;
range.max = DATA.windows.length - 1;
for (const [key, label] of Object.entries(DATA.tasks)) {
const option = document.createElement("option");
option.value = key;
option.textContent = label;
taskSelect.appendChild(option);
}
const allOption = document.createElement("option");
allOption.value = "all";
allOption.textContent = "All Prediction Cards";
taskSelect.insertBefore(allOption, taskSelect.firstChild);
taskSelect.value = state.task;
function pct(value, min, max) { return ((value - min) / Math.max(1, max - min)) * 100; }
function splitObjects(value) { return String(value || "").split("|").filter(Boolean); }
function renderTimeline() {
const strip = document.getElementById("timelineStrip");
strip.innerHTML = "";
const minFrame = DATA.windows[0].start_frame;
const maxFrame = DATA.windows[DATA.windows.length - 1].end_frame;
DATA.segments.forEach((seg, i) => {
const el = document.createElement("div");
el.className = "segment";
el.style.left = pct(seg.start_frame, minFrame, maxFrame) + "%";
el.style.width = Math.max(0.3, pct(seg.end_frame, minFrame, maxFrame) - pct(seg.start_frame, minFrame, maxFrame)) + "%";
el.style.background = colors[i % colors.length];
el.title = `${seg.action} (${seg.start_frame}-${seg.end_frame})`;
el.addEventListener("click", () => { state.index = seg.start_window; render(); });
strip.appendChild(el);
});
const marker = document.createElement("div");
marker.className = "marker";
marker.style.left = pct(DATA.windows[state.index].center_frame, minFrame, maxFrame) + "%";
strip.appendChild(marker);
document.getElementById("timelineLeft").textContent = `frame ${minFrame}`;
document.getElementById("timelineRight").textContent = `frame ${maxFrame}`;
}
function renderPredictions(w) {
const grid = document.getElementById("predictionGrid");
grid.innerHTML = "";
const taskEntries = Object.entries(DATA.tasks).filter(([key]) => state.task === "all" || key === state.task);
for (const [key, label] of taskEntries) {
const pred = w.predictions[key];
const card = document.createElement("article");
card.className = "pred";
let body = "";
if (!pred) {
body = `<p class="subtle">No held-out prediction row for this window.</p>`;
} else {
const status = pred.correct ? `<span class="ok">correct</span>` : `<span class="bad">mismatch</span>`;
body = `<p>${status}</p><p><strong>true</strong>: ${escapeHtml(pred.true || "")}</p><p><strong>pred</strong>: ${escapeHtml(pred.predicted || "")}</p>`;
if (pred.confidence) body += `<p><strong>confidence</strong>: ${Number(pred.confidence).toFixed(3)}</p>`;
}
card.innerHTML = `<h3>${escapeHtml(label)}</h3>${body}`;
grid.appendChild(card);
}
}
function renderFeatures(w) {
const grid = document.getElementById("featureGrid");
grid.innerHTML = "";
for (const stat of w.feature_stats) {
const block = DATA.feature_blocks.find((b) => b.name === stat.name);
const row = document.createElement("div");
row.className = "feature";
row.innerHTML = `<span class="feature-name">${escapeHtml(block.display)}</span><span class="bar"><span style="--w:${Math.round(stat.relative * 100)}"></span></span><span class="num">${stat.l2.toFixed(2)}</span>`;
grid.appendChild(row);
}
}
function renderDiagnostics() {
const rows = document.getElementById("diagnosticRows");
rows.innerHTML = "";
const task = state.task === "all" ? "object_relevance" : state.task;
const diag = DATA.ablation.best_by_task[task];
if (diag) {
rows.innerHTML += `<div class="row"><span>Best modality for ${escapeHtml(DATA.tasks[task] || task)}</span><strong>${escapeHtml(diag.best.modality_display)} ${diag.best.score.toFixed(3)}</strong></div>`;
if (diag.best_non_overlap) rows.innerHTML += `<div class="row"><span>Best non-overlap modality</span><strong>${escapeHtml(diag.best_non_overlap.modality_display)} ${diag.best_non_overlap.score.toFixed(3)}</strong></div>`;
}
const zeroRows = DATA.alignment.filter((r) => Number(r.shift_windows) === 0);
zeroRows.slice(0, 5).forEach((r) => {
rows.innerHTML += `<div class="row"><span>${escapeHtml(r.query_display)} zero-shift retrieval MRR</span><strong>${Number(r.mrr).toFixed(3)}</strong></div>`;
});
document.getElementById("diagnosticNote").textContent = DATA.meta.source_policy;
}
function renderWindow() {
const w = DATA.windows[state.index];
range.value = state.index;
document.getElementById("windowTitle").textContent = `Window ${w.window_index}: ${w.action || "unlabeled action"}`;
document.getElementById("windowSubtitle").textContent = w.subtask || "No subtask label";
document.getElementById("framePill").textContent = `frames ${w.start_frame}-${w.end_frame}`;
const chips = document.getElementById("objectChips");
chips.innerHTML = "";
(w.objects.length ? w.objects : ["no object label"]).forEach((obj) => {
const chip = document.createElement("span");
chip.className = "chip";
chip.textContent = obj;
chips.appendChild(chip);
});
renderPredictions(w);
renderFeatures(w);
renderDiagnostics();
}
function render() { renderTimeline(); renderWindow(); }
function escapeHtml(s) { return String(s).replace(/[&<>"']/g, (c) => ({ "&":"&", "<":"<", ">":">", '"':""", "'":"'" }[c])); }
range.addEventListener("input", () => { state.index = Number(range.value); render(); });
taskSelect.addEventListener("change", () => { state.task = taskSelect.value; render(); });
document.getElementById("prevWindow").addEventListener("click", () => { state.index = Math.max(0, state.index - 1); render(); });
document.getElementById("nextWindow").addEventListener("click", () => { state.index = Math.min(DATA.windows.length - 1, state.index + 1); render(); });
document.getElementById("firstPred").addEventListener("click", () => {
const found = DATA.windows.find((w) => hasPrediction(w, state.task));
if (found) { state.index = found.window_index; render(); }
});
document.getElementById("firstMatch").addEventListener("click", () => {
const q = searchBox.value.trim().toLowerCase();
if (!q) return;
const found = DATA.windows.find((w) => [w.action, w.subtask, ...w.objects].join(" ").toLowerCase().includes(q));
if (found) { state.index = found.window_index; render(); }
});
render();
</script>
</body>
</html>
"""
def write_html(path: Path, data: dict) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
payload = json.dumps(data, ensure_ascii=False).replace("</script", "<\\/script")
path.write_text(HTML_TEMPLATE.replace("__DATA__", payload), encoding="utf-8")
def main() -> None:
args = parse_args()
data = build_data(args)
write_json(args.docs_dir / "data/single_episode_explorer.json", data)
write_html(args.docs_dir / "single_episode_explorer.html", data)
print(f"Wrote {args.docs_dir / 'data/single_episode_explorer.json'}")
print(f"Wrote {args.docs_dir / 'single_episode_explorer.html'}")
if __name__ == "__main__":
main()
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