Initial commit

2026-06-29 15:11:48 -07:00
commit 66dfdcc58d
404 changed files with 45970 additions and 0 deletions
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+#!/usr/bin/env python3
+"""Classify width-varying nodes in a DENSE capture by their sizing law, using the full
+sample curve. Run after a dense clone: python3 scripts/analyze-dense.py <run/.clone> """
+import json, sys, statistics, glob, os
+run = sys.argv[1] if len(sys.argv) > 1 else "output-dense/sample/.clone"
+caps = {}
+for f in sorted(glob.glob(os.path.join(run, "source/capture/dom-*.json"))):
+    vp = int(os.path.basename(f)[4:-5])
+    caps[vp] = json.load(open(f))["root"]
+VPS = sorted(caps)
+print("sample widths:", VPS)
+
+def flat(n, a):
+    if isinstance(n, dict):
+        a.append(n)
+        for c in (n.get("children") or []): flat(c, a)
+F = {vp: [] for vp in VPS}
+for vp in VPS: flat(caps[vp], F[vp])
+def wp(n, p, m):
+    m[id(n)] = p
+    for c in (n.get("children") or []): wp(c, n, m)
+PM = {vp: {} for vp in VPS}
+for vp in VPS: wp(caps[vp], None, PM[vp])
+def pf(v):
+    try: return float(str(v).replace("px", ""))
+    except: return 0.0
+
+n = min(len(F[vp]) for vp in VPS)
+cats = {"fills_container": 0, "prop_container": 0, "prop_viewport": 0, "clamped_maxw": 0,
+        "shrink_recoverable": 0, "real_breakpoint": 0, "fixed": 0, "unknown": 0}
+for i in range(n):
+    nd = {vp: F[vp][i] for vp in VPS}
+    if not all(nd[vp].get("visible") for vp in VPS): continue
+    w = {vp: (nd[vp].get("bbox") or {}).get("width", 0) for vp in VPS}
+    if max(w.values()) - min(w.values()) <= 2: continue  # constant → no band
+    # container content width per vp
+    cw = {}
+    for vp in VPS:
+        p = PM[vp].get(id(nd[vp])); pb = (p or {}).get("bbox"); pcs = (p or {}).get("computed") or {}
+        base = pb["width"] if pb else vp
+        cw[vp] = base - pf(pcs.get("paddingLeft")) - pf(pcs.get("paddingRight"))
+    rc = [w[vp] / cw[vp] for vp in VPS if cw[vp] > 0]
+    rv = [w[vp] / vp for vp in VPS]
+    def cv(xs):
+        m = statistics.mean(xs); return statistics.pstdev(xs) / m if m else 9
+    wl = [w[vp] for vp in VPS]
+    if rc and cv(rc) < 0.02 and abs(statistics.mean(rc) - 1) < 0.02: cats["fills_container"] += 1
+    elif rc and cv(rc) < 0.03: cats["prop_container"] += 1
+    elif cv(rv) < 0.03: cats["prop_viewport"] += 1
+    elif wl[-1] == wl[-2] and wl[0] < wl[-1]:  # plateau at the widest → clamp or recoverable shrink
+        # constant above a knee, smaller below
+        cats["clamped_maxw"] += 1
+    elif wl[-1] < wl[-2]:  # still shrinking even at the widest → natural beyond range
+        cats["shrink_recoverable"] += 1
+    else:
+        # piecewise? count distinct plateaus
+        cats["unknown"] += 1
+print("\nwidth-varying nodes by inferred sizing law (dense):")
+for k, v in cats.items(): print(f"  {k:20} {v}")
+print(f"  TOTAL varying: {sum(cats.values())}")