Umut CANandClaude Opus 5.5
7aaead43d9
Equipment tags found exactly once in each drawing are matched (a tag found twice - a plan copy, a legend - is reported ambiguous and takes no part; two plan copies resolve to the cluster that matches the most tags). For every pair of matched tags at least min_pair_mm apart on the reference, the ratio of their distances is taken in millimetres by each drawing's inferred unit; the median, the quartiles, the share within +/-10 % of the median, the same per room of the reference, and the verdict: to_scale (>= 80 %, with its factor), schematic (<= 50 %), partly, or insufficient under three matched tags. server.py grows SECTION 26 to two tools with drawing_scale_check (core, read-only, tagged analysis/query); alias record, inventory, README release snapshot 243, frozen group sizes (analysis 14). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FKFStjEFzpGyLq5bMpWxTd
273 lines
11 KiB
Python
273 lines
11 KiB
Python
"""drawing_scale_check: is this drawing to scale against that one?
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A P&ID and a layout of one plant carry the same equipment tags. If the P&ID
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were drawn to scale, the distance between two tanks on it would be the distance
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on the layout times one factor, for every pair. On a schematic it is not - and
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a pipe length measured on a schematic is a length of drawn line, not of pipe.
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The check, in the order it runs:
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1. **Scope.** Model-space tags are read (`describe.tag_occurrences`) and the
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drawings split into clusters (`clusters.find_clusters`, outliers left out).
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Every pair of scopes - the whole drawing or one of its eight largest
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clusters, on each side - is tried, and the pair that matches the most tags
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wins (ties: the whole drawing first, then the larger cluster). Two copies
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of one plan side by side therefore resolve to one copy instead of making
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every tag ambiguous.
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2. **Match.** Only a tag found exactly once in each scope is matched. A tag
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found more than once in either (a plan copy, a legend, a second sheet) is
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``ambiguous`` and takes no part: picking one of its positions would be a
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guess.
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3. **Pairs.** For every two matched tags whose distance on the reference
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drawing ``b`` is at least ``min_pair_mm``, the ratio of their distance on
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``a`` to that on ``b`` is taken, both converted to millimetres by each
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drawing's inferred unit (`units.infer_units`).
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4. **Statistics.** The median ratio, the quartiles and their range, and the
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share of pairs within +/-10 % of the median - overall, and per room of the
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reference drawing (the labelled faces `describe.find_rooms` finds) or, for
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tags in no room, per cluster.
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5. **Verdict.** ``to_scale`` when at least 80 % of the pairs are within the
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band (``factor`` is then the median: a drawing at a different but uniform
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scale is recognised); ``schematic`` at 50 % or less; ``partly`` in between;
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``insufficient`` with fewer than three matched tags or no pair above the
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floor.
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A tag label is not an equipment centre: the band and the floor are there to
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absorb the offset between the two, and the result says so. Pure; neither
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drawing is touched.
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"""
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from __future__ import annotations
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import math
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import statistics
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from itertools import combinations
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from engineering.arch.faces import face_containing
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from engineering.understand.describe import FACE_TOL_FRACTION, find_rooms, prepare, tag_occurrences
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from engineering.understand.snapshot import Snapshot
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from engineering.understand.units import infer_units
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__all__ = ["MIN_TAGS", "SCHEMATIC_SHARE", "TO_SCALE_SHARE", "WITHIN", "match_tags", "scale_check"]
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#: The band around the median a pair must fall in to count as consistent.
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WITHIN = 0.10
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TO_SCALE_SHARE = 0.80
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SCHEMATIC_SHARE = 0.50
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MIN_TAGS = 3
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#: Clusters per drawing tried as a scope, largest first.
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MAX_SCOPE_CLUSTERS = 8
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#: Pair rows listed in the result, farthest from the median first.
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PAIR_ROW_CAP = 200
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NOTE = (
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"Tag labels are not equipment centres; the +/-10 % band and the min_pair_mm floor absorb "
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"the offset between a label and what it labels."
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)
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def _side(snap: Snapshot) -> dict:
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prep = prepare(snap)
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prep["tags"] = [o for o in tag_occurrences(snap, prep=prep) if o["space"] == "Model"]
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return prep
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def _scoped(side: dict, cluster: str | None, name: str) -> list[dict]:
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if cluster is None:
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return side["tags"]
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ids = [c["id"] for c in side["clusters"]]
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if cluster not in ids:
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raise ValueError(f"{name}: no cluster {cluster!r}; the drawing has {ids}")
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return [o for o in side["tags"] if o["cluster"] == cluster]
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def _match(side_a: dict, side_b: dict, cluster_a: str | None, cluster_b: str | None) -> dict:
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occ_a = _scoped(side_a, cluster_a, "cluster_a")
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occ_b = _scoped(side_b, cluster_b, "cluster_b")
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count_a: dict[str, list[dict]] = {}
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count_b: dict[str, list[dict]] = {}
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for o in occ_a:
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count_a.setdefault(o["tag"], []).append(o)
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for o in occ_b:
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count_b.setdefault(o["tag"], []).append(o)
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matched = {
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tag: (tuple(count_a[tag][0]["at"]), tuple(count_b[tag][0]["at"]))
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for tag in sorted(set(count_a) & set(count_b))
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if len(count_a[tag]) == 1 and len(count_b[tag]) == 1
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}
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ambiguous = sorted(
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tag
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for tag in set(count_a) | set(count_b)
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if len(count_a.get(tag, ())) > 1 or len(count_b.get(tag, ())) > 1
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)
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return {
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"matched": matched,
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"ambiguous": ambiguous,
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"occurrences": {
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tag: {"a": len(count_a.get(tag, ())), "b": len(count_b.get(tag, ()))}
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for tag in ambiguous
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},
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"only_a": sorted(t for t in count_a if t not in count_b and len(count_a[t]) == 1),
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"only_b": sorted(t for t in count_b if t not in count_a and len(count_b[t]) == 1),
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"cluster_a": cluster_a,
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"cluster_b": cluster_b,
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}
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def match_tags(a: Snapshot, b: Snapshot, *, cluster_a=None, cluster_b=None) -> dict:
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"""``{"matched": {tag: (pa, pb)}, "ambiguous": [tag, ...], ...}`` within the
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given scopes (a cluster id such as ``"C1"``, or None for the whole model
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space). ``pa`` / ``pb`` are the tag's positions in drawing units. Also
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``occurrences`` (per ambiguous tag, how often each side has it),
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``only_a`` / ``only_b`` and the scopes used. An unknown cluster id is
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refused with the ids the drawing has."""
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return _match(_side(a), _side(b), cluster_a, cluster_b)
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def _stats(ratios: list[float]) -> dict:
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if not ratios:
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return {"median": None, "q1": None, "q3": None, "iqr": None, "within_10pct": None}
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median = statistics.median(ratios)
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if len(ratios) >= 2:
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q1, _q2, q3 = statistics.quantiles(ratios, n=4, method="inclusive")
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else:
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q1 = q3 = ratios[0]
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within = sum(1 for r in ratios if abs(r - median) <= WITHIN * median) / len(ratios)
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return {"median": median, "q1": q1, "q3": q3, "iqr": q3 - q1, "within_10pct": within}
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def _verdict(tag_count: int, pair_count: int, stats: dict) -> str:
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if tag_count < MIN_TAGS or pair_count == 0 or not stats["median"] or stats["median"] <= 0:
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return "insufficient"
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if stats["within_10pct"] >= TO_SCALE_SHARE:
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return "to_scale"
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if stats["within_10pct"] <= SCHEMATIC_SHARE:
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return "schematic"
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return "partly"
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def _summary(tags: list[str], rows: list[dict]) -> dict:
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stats = _stats([r["ratio"] for r in rows])
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verdict = _verdict(len(tags), len(rows), stats)
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return {
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"verdict": verdict,
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"factor": stats["median"] if verdict == "to_scale" else None,
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**stats,
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"pair_count": len(rows),
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}
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def scale_check(a: Snapshot, b: Snapshot, *, min_pair_mm=2000.0) -> dict:
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"""Is drawing ``a`` to scale against the reference drawing ``b``?
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Returns ``{"verdict", "factor", "median", "q1", "q3", "iqr", "within_10pct",
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"pair_count", "pairs", "pairs_truncated", "pairs_below_floor", "matched",
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"ambiguous", "occurrences", "only_a", "only_b", "groups", "scope", "units",
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"min_pair_mm", "thresholds", "notes"}``. ``pairs`` lists at most 200 rows,
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farthest from the median first; ``matched`` is ``[{"tag", "a": [x, y],
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"b": [x, y]}]`` in drawing units. Refused before anything is computed: a
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``min_pair_mm`` that is not a positive finite number.
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"""
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try:
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floor = float(min_pair_mm)
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except (TypeError, ValueError):
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raise ValueError(f"min_pair_mm: expected a number, got {min_pair_mm!r}") from None
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if not math.isfinite(floor) or floor <= 0.0:
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raise ValueError(f"min_pair_mm: must be a positive number of millimetres, got {floor:g}")
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side_a, side_b = _side(a), _side(b)
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best = None
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for ca in [None] + [c["id"] for c in side_a["clusters"][:MAX_SCOPE_CLUSTERS]]:
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for cb in [None] + [c["id"] for c in side_b["clusters"][:MAX_SCOPE_CLUSTERS]]:
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m = _match(side_a, side_b, ca, cb)
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if best is None or len(m["matched"]) > len(best["matched"]):
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best = m
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tol_b = side_b["diag"] * FACE_TOL_FRACTION if side_b["diag"] > 0 else 1.0
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rooms_b = find_rooms(side_b["kept"], tol=tol_b, membership=side_b["membership"])
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units = {}
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notes = [NOTE]
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for key, snap, side, rooms in (
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("a", a, side_a, None),
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("b", b, side_b, rooms_b),
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):
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areas = [row["face"]["area"] for row, _f in rooms["labelled"]] if rooms else []
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u = infer_units(snap, records=side["kept"], room_areas=areas)
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units[key] = {
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"unit": u["inferred"],
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"mm_per_unit": u["mm_per_unit"],
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"declared": u["declared"],
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}
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if u["mm_per_unit"] is None:
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notes.append(
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f"drawing {key}: no unit could be settled; its drawing units are taken as mm"
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)
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fa = units["a"]["mm_per_unit"] or 1.0
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fb = units["b"]["mm_per_unit"] or 1.0
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matched = best["matched"]
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tags = sorted(matched)
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rows: list[dict] = []
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below = 0
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for ti, tj in combinations(tags, 2):
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d_b = math.dist(matched[ti][1], matched[tj][1]) * fb
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if d_b < floor:
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below += 1
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continue
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d_a = math.dist(matched[ti][0], matched[tj][0]) * fa
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rows.append({"tags": [ti, tj], "a_mm": d_a, "b_mm": d_b, "ratio": d_a / d_b})
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overall = _summary(tags, rows)
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faces = [face for _row, face in rooms_b["labelled"]]
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room_of = {id(face): row for row, face in rooms_b["labelled"]}
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groups: dict[str, dict] = {}
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for tag in tags:
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pb = matched[tag][1]
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face = face_containing(faces, pb) if faces else None
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if face is not None:
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row = room_of[id(face)]
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key = " ".join(p for p in ("room", row["number"], row["name"]) if p)
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kind = "room"
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else:
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cluster = next(
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(o["cluster"] for o in side_b["tags"] if o["tag"] == tag and tuple(o["at"]) == pb),
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None,
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)
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key, kind = (cluster or "unclustered"), "cluster"
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groups.setdefault(key, {"group": key, "kind": kind, "tags": []})["tags"].append(tag)
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group_rows = []
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for key in sorted(groups):
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g = groups[key]
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if len(g["tags"]) < 2:
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continue
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members = set(g["tags"])
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inside = [r for r in rows if r["tags"][0] in members and r["tags"][1] in members]
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group_rows.append({**g, **_summary(g["tags"], inside)})
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median = overall["median"]
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listed = sorted(
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rows,
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key=lambda r: (-(abs(r["ratio"] - median) if median else 0.0), r["tags"]),
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)[:PAIR_ROW_CAP]
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return {
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**overall,
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"pairs": listed,
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"pairs_truncated": len(rows) > PAIR_ROW_CAP,
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"pairs_below_floor": below,
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"matched": [{"tag": t, "a": list(matched[t][0]), "b": list(matched[t][1])} for t in tags],
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"ambiguous": best["ambiguous"],
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"occurrences": best["occurrences"],
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"only_a": best["only_a"],
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"only_b": best["only_b"],
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"groups": group_rows,
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"scope": {"cluster_a": best["cluster_a"], "cluster_b": best["cluster_b"]},
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"units": units,
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"min_pair_mm": floor,
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"thresholds": {
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"within": WITHIN,
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"to_scale_share": TO_SCALE_SHARE,
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"schematic_share": SCHEMATIC_SHARE,
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"min_tags": MIN_TAGS,
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},
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"notes": notes,
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}
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