chore(webui): remove timeline animation page

The animated t-SNE 'embedding landscape' added no real analytical value and
its cumulative-by-month logic was inherently confusing (ancient drafts
appearing late in the animation). Removed entirely rather than maintained.

Drops the /timeline route, template, nav link, data builder
(get_timeline_animation_data / _compute_timeline_animation_data) and its test.
The Overview mini-timeline and /api/timeline (separate features) are untouched.

src/webui/data/__init__.py

@@ -69,7 +69,6 @@ from webui.data.analysis import (  # noqa: F401
     get_timeline_data,
     get_similarity_graph,
     get_idea_clusters,
-    get_timeline_animation_data,
     get_monitor_status,
     get_citation_graph,
     get_landscape_tsne,

src/webui/data/analysis.py

@@ -502,81 +502,6 @@ def _compute_idea_clusters(db: Database) -> dict:
         "empty": False,
     }
 
-def get_timeline_animation_data(db: Database) -> dict:
-    """Timeline animation (cached for 5 min)."""
-    return _cached("timeline_animation", lambda: _compute_timeline_animation_data(db))
-
-def _compute_timeline_animation_data(db: Database) -> dict:
-    """Compute t-SNE on all drafts, return points with month info + category_monthly.
-
-    t-SNE is computed once on ALL drafts so coordinates are stable across
-    animation frames.  Each point carries a ``month`` field (YYYY-MM) so the
-    front-end can build cumulative animation frames.
-    """
-
-
-    embeddings = db.all_embeddings()
-    if len(embeddings) < 5:
-        return {"points": [], "months": [], "category_monthly": {}}
-
-    pairs = db.drafts_with_ratings(limit=1000)
-    rating_map = {d.name: r for d, r in pairs}
-    draft_map = {d.name: d for d, _ in pairs}
-
-    # Filter to drafts that have both embeddings and ratings
-    names = [n for n in embeddings if n in rating_map]
-    if len(names) < 5:
-        return {"points": [], "months": [], "category_monthly": {}}
-
-    matrix = np.array([embeddings[n] for n in names])
-
-    try:
-        tsne = TSNE(n_components=2, perplexity=min(30, len(names) - 1),
-                     random_state=42, max_iter=500)
-        coords = tsne.fit_transform(matrix)
-    except Exception:
-        return {"points": [], "months": [], "category_monthly": {}}
-
-    # Build points with month
-    points = []
-    month_set: set[str] = set()
-    category_monthly: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
-
-    for i, name in enumerate(names):
-        r = rating_map[name]
-        d = draft_map.get(name)
-        month = _extract_month(d.time if d else None)
-        if month == "unknown":
-            continue  # Undated docs (e.g. ISO/ETSI) can't be placed on a temporal animation
-        cat = r.categories[0] if r.categories else "Other"
-        month_set.add(month)
-        category_monthly[month][cat] += 1
-        points.append({
-            "name": name,
-            "title": d.title if d else name,
-            "x": round(float(coords[i, 0]), 3),
-            "y": round(float(coords[i, 1]), 3),
-            "category": cat,
-            "score": round(r.composite_score, 2),
-            "month": month,
-        })
-
-    # Deliver points in chronological order so the front-end's cumulative
-    # filter (p.month <= frame) is append-only.  Otherwise new points get
-    # inserted mid-array and Plotly's index-based frame transition animates
-    # existing markers flying to other drafts' coordinates ("jumping points").
-    points.sort(key=lambda p: (p["month"], p["name"]))
-
-    months = sorted(month_set)
-    # Convert defaultdict to plain dict for JSON
-    cat_monthly_plain = {m: dict(cats) for m, cats in category_monthly.items()}
-
-    return {
-        "points": points,
-        "months": months,
-        "category_monthly": cat_monthly_plain,
-    }
-
 def get_monitor_status(db: Database) -> MonitorStatus:
     """Return monitoring status data for dashboard."""
     runs = db.get_monitor_runs(limit=20)