merge: track H group U
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FKFStjEFzpGyLq5bMpWxTd
This commit is contained in:
Umut CAN
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"""Separate bodies of model-space content: sheets, plan copies, details.
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A foreign model space is often several drawings side by side - two copies of
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one plan, a detail beside it, a legend. Every reader after this one must know
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which body a tag or a room belongs to, or it matches a tag in one copy against
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the same tag in the other.
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The algorithm, in the order it runs:
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1. **Raster.** The box of the records is divided into square cells of
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``cell`` drawing units (default :data:`CELL_FRACTION` of its diagonal, so
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the grid never exceeds about 50 x 50 cells). Every record marks the cells
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its box covers.
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2. **Connect.** Occupied cells that touch, corners included, are one
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component; a record belongs to the component of its cells. Two bodies stay
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apart when an empty cell separates them - a gap of two cells is always
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enough.
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3. **Absorb.** Components whose boxes intersect are merged, repeatedly: a tag
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floating in the middle of a room lies inside the plan's box and belongs to
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the plan, not to a cluster of its own.
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Ids are ``C1``, ``C2``, ... by entity count, largest first (ties left to right,
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then bottom to top). Outliers are the caller's to exclude: one entity thousands of
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kilometres away would otherwise set the cell size. Pure; the drawing is never touched.
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"""
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from __future__ import annotations
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import math
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from collections.abc import Iterable, Sequence
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from engineering.understand.snapshot import EntityRecord
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from engineering.understand.units import entity_box
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__all__ = ["CELL_FRACTION", "find_clusters"]
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#: Cell side as a fraction of the content's diagonal.
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CELL_FRACTION = 0.02
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#: Labels listed per cluster.
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LABELS_PER_CLUSTER = 5
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_TEXT_TYPES = frozenset({"TEXT", "MTEXT", "MULTILEADER"})
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def _intersect(a, b) -> bool:
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return a[0] <= b[2] and b[0] <= a[2] and a[1] <= b[3] and b[1] <= a[3]
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def _merge(a, b):
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return (min(a[0], b[0]), min(a[1], b[1]), max(a[2], b[2]), max(a[3], b[3]))
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def _root(parent: list[int], i: int) -> int:
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"""Union-find root with path halving."""
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while parent[i] != i:
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parent[i] = parent[parent[i]]
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i = parent[i]
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return i
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def _absorb(bodies: list[tuple[tuple, list[int]]]) -> list[tuple[tuple, list[int]]]:
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"""Merge bodies whose boxes intersect until none do.
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One pass is a sweep over boxes sorted by their left edge (a pair can only
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intersect while the next left edge is inside the current box) feeding a
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union-find; a merged box can reach a body it did not touch before, so
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passes repeat until one merges nothing.
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"""
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while True:
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order = sorted(range(len(bodies)), key=lambda i: bodies[i][0][0])
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parent = list(range(len(bodies)))
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joined = False
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for n, i in enumerate(order):
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box = bodies[i][0]
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for j in order[n + 1 :]:
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if bodies[j][0][0] > box[2]:
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break
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ri, rj = _root(parent, i), _root(parent, j)
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if ri != rj and _intersect(box, bodies[j][0]):
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parent[rj] = ri
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joined = True
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if not joined:
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return bodies
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merged: dict[int, tuple[tuple, list[int]]] = {}
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for i, (box, members) in enumerate(bodies):
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r = _root(parent, i)
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if r in merged:
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merged[r] = (_merge(merged[r][0], box), merged[r][1] + members)
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else:
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merged[r] = (box, list(members))
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bodies = list(merged.values())
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def find_clusters(
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records: Sequence[EntityRecord],
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*,
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exclude: Iterable[str] = (),
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cell: float | None = None,
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) -> tuple[list[dict], dict[str, str]]:
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"""``(clusters, membership)``: one row per body of content, and handle -> id.
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A row is ``{"id", "box": [x0, y0, x1, y1], "size": [w, h], "entities",
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"layers": [the three commonest], "labels": [the tallest texts]}``.
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``exclude`` names handles to leave out (the outliers). A record without a
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box (no geometry the snapshot could read) belongs to no cluster.
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"""
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skip = set(exclude)
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items = [(rec, box) for rec in records if rec.handle not in skip and (box := entity_box(rec))]
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if not items:
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return [], {}
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x0 = min(b[0] for _r, b in items)
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y0 = min(b[1] for _r, b in items)
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x1 = max(b[2] for _r, b in items)
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y1 = max(b[3] for _r, b in items)
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if cell is None:
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diag = math.hypot(x1 - x0, y1 - y0)
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cell = diag * CELL_FRACTION if diag > 0.0 else 1.0
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if not cell > 0.0:
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raise ValueError(f"cell: must be greater than zero, got {cell!r}")
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def span(lo: float, hi: float, origin: float) -> range:
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return range(
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int(math.floor((lo - origin) / cell)), int(math.floor((hi - origin) / cell)) + 1
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)
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owner: dict[tuple[int, int], list[int]] = {}
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first_cell: list[tuple[int, int]] = []
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for k, (_rec, b) in enumerate(items):
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cols, rows = span(b[0], b[2], x0), span(b[1], b[3], y0)
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first_cell.append((cols[0], rows[0]))
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for i in cols:
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for j in rows:
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owner.setdefault((i, j), []).append(k)
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component: dict[tuple[int, int], int] = {}
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count = 0
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for start in owner:
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if start in component:
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continue
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component[start] = count
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stack = [start]
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while stack:
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ci, cj = stack.pop()
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for di in (-1, 0, 1):
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for dj in (-1, 0, 1):
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nxt = (ci + di, cj + dj)
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if nxt in owner and nxt not in component:
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component[nxt] = count
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stack.append(nxt)
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count += 1
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groups: dict[int, list[int]] = {}
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for k in range(len(items)):
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groups.setdefault(component[first_cell[k]], []).append(k)
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bodies = []
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for members in groups.values():
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box = items[members[0]][1]
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for k in members[1:]:
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box = _merge(box, items[k][1])
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bodies.append((box, members))
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bodies = _absorb(bodies)
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bodies.sort(key=lambda body: (-len(body[1]), body[0][0], body[0][1]))
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clusters: list[dict] = []
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membership: dict[str, str] = {}
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for n, (box, members) in enumerate(bodies, start=1):
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cid = f"C{n}"
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layers: dict[str, int] = {}
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texts = []
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for k in members:
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rec = items[k][0]
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membership[rec.handle] = cid
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layers[rec.layer] = layers.get(rec.layer, 0) + 1
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if rec.type in _TEXT_TYPES and rec.text and rec.text.strip():
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texts.append((-(rec.height or 0.0), rec.text.strip()))
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labels: list[str] = []
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for _h, text in sorted(texts):
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if text not in labels:
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labels.append(text)
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if len(labels) == LABELS_PER_CLUSTER:
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break
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clusters.append(
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{
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"id": cid,
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"box": list(box),
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"size": [box[2] - box[0], box[3] - box[1]],
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"entities": len(members),
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"layers": [
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name for name, _c in sorted(layers.items(), key=lambda kv: (-kv[1], kv[0]))[:3]
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],
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"labels": labels,
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}
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)
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return clusters, membership
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"""drawing_understand: one read of a foreign drawing, every claim with its evidence.
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`describe` assembles the report from the pure readers - units and extents
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(`units`), bodies of content (`clusters`), and here: layers and what they
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carry, equipment tags, rooms, text languages, blocks, layouts and the title
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block. Each claim carries its evidence (what was measured or matched) and a
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confidence; nothing is corrected silently, and the drawing is never modified
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(the snapshot is a frozen copy).
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Confidences, in one place:
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* a layer name matching the vocabulary: `vocab.classify_layer`'s own (0.9 for
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a direct keyword); the texts on an unnamed layer voting instead:
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:data:`TEXT_VOTE_CONFIDENCE` when most of them agree, :data:`WEAK_VOTE_CONFIDENCE`
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otherwise;
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* a tag read from a block attribute :data:`ATTRIBUTE_TAG_CONFIDENCE`, from a
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text :data:`TEXT_TAG_CONFIDENCE`;
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* a room label that sits in a face of the wall layers
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:data:`ROOM_FACE_CONFIDENCE`, a label alone :data:`ROOM_LABEL_CONFIDENCE`;
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* a title block whose name says so :data:`TITLE_NAME_CONFIDENCE`, one chosen by
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its attribute count alone :data:`TITLE_ATTRIBUTE_CONFIDENCE`;
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* character classes are exact: a text either holds a Cyrillic letter or not.
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"""
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from __future__ import annotations
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import math
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import re
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from collections import Counter
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from collections.abc import Sequence
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from engineering.arch.faces import Face, face_containing, planar_faces
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from engineering.pid.graph import ATTRIBUTE_TAGS, KEYWORDS
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from engineering.understand.clusters import find_clusters
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from engineering.understand.labels import parse_tag
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from engineering.understand.snapshot import EntityRecord, Snapshot, length_of
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from engineering.understand.units import infer_units, robust_extents, wall_segments
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from engineering.understand.vocab import classify_layer, room_label
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__all__ = [
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"describe",
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"find_rooms",
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"prepare",
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"tag_occurrences",
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"text_language",
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]
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TEXT_VOTE_CONFIDENCE = 0.6
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WEAK_VOTE_CONFIDENCE = 0.4
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ATTRIBUTE_TAG_CONFIDENCE = 0.95
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TEXT_TAG_CONFIDENCE = 0.8
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ROOM_FACE_CONFIDENCE = 0.9
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ROOM_LABEL_CONFIDENCE = 0.6
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TITLE_NAME_CONFIDENCE = 0.9
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TITLE_ATTRIBUTE_CONFIDENCE = 0.6
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#: The face finder pairs every wall segment with every other; above this many
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#: the rooms are listed without faces and a warning says so.
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FACE_SEGMENT_CAP = 3000
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#: Endpoints closer than this fraction of the robust diagonal are one corner
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#: (1 mm on a 50 m plan drawn in millimetres).
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FACE_TOL_FRACTION = 2e-5
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#: The same tag lettered twice closer than this fraction of the robust diagonal
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#: (an attribute and a text beside one symbol) is one occurrence.
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TAG_MERGE_FRACTION = 0.01
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#: Rows listed for named blocks, most inserted first.
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BLOCK_ROW_CAP = 100
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#: Block-name words that mark a title block (EN / NL / DE / TR / RU).
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TITLE_WORDS = ("TITLE", "TITEL", "STEMPEL", "SCHRIFTFELD", "ANTET", "ШТАМП")
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_TEXT_TYPES = frozenset({"TEXT", "MTEXT", "MULTILEADER"})
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_TOKEN_RE = re.compile(r"[A-Z]+")
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_CYRILLIC = re.compile(r"[\u0400-\u04FF]") # the Cyrillic block
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#: Letters only Turkish (of the five languages) writes; ç, ö and ü are shared
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#: with German and French and are not evidence on their own.
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_TURKISH = re.compile(r"[ĞğİıŞş]")
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_LATIN = re.compile(r"[A-Za-z\u00C0-\u024F]") # ASCII and Latin-1 to Latin Extended-B
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def _anchor(rec: EntityRecord) -> tuple[float, float] | None:
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if rec.points:
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return (float(rec.points[0][0]), float(rec.points[0][1]))
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if rec.bbox is not None:
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b = rec.bbox
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return ((b[0] + b[2]) / 2.0, (b[1] + b[3]) / 2.0)
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return None
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def prepare(snap: Snapshot) -> dict:
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"""The shared first pass: model-space records, extents and outliers, the
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records kept, clusters and membership. `describe` and `scale.scale_check`
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both start here, so they agree on which entity is an outlier and which
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cluster a tag sits in."""
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model = [r for r in snap.records if r.space == "Model"]
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extents = robust_extents(model)
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outliers = {o["handle"] for o in extents["outliers"]}
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kept = [r for r in model if r.handle not in outliers]
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clusters, membership = find_clusters(kept)
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robust = extents["robust"]
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diag = math.hypot(robust[2] - robust[0], robust[3] - robust[1]) if robust else 0.0
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return {
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"model": model,
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"extents": extents,
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"kept": kept,
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"clusters": clusters,
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"membership": membership,
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"diag": diag,
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}
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def tag_occurrences(snap: Snapshot, *, prep: dict | None = None) -> list[dict]:
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"""Every equipment tag on the drawing, one row per occurrence.
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A tag is read with `labels.parse_tag` from a TEXT / MTEXT / MULTILEADER,
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or from an INSERT attribute named like a tag (`pid.graph.ATTRIBUTE_TAGS`).
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Two readings of one tag in one space closer than
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:data:`TAG_MERGE_FRACTION` of the diagonal are one occurrence (the
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attribute's position and confidence win). Rows carry ``tag``, ``handle``,
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``at``, ``space``, ``layer``, ``cluster`` (model space only), ``source``
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and ``confidence``.
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"""
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prep = prep or prepare(snap)
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radius = prep["diag"] * TAG_MERGE_FRACTION
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found: list[dict] = []
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for rec in snap.records:
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at = _anchor(rec)
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if at is None:
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continue
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readings = []
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if rec.type in _TEXT_TYPES and rec.text:
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readings.append((parse_tag(rec.text), "text", TEXT_TAG_CONFIDENCE))
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elif rec.type == "INSERT":
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for name, value in rec.attribs:
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if name.upper() in ATTRIBUTE_TAGS and value:
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readings.append((parse_tag(value), "attribute", ATTRIBUTE_TAG_CONFIDENCE))
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for tag, source, confidence in readings:
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if not tag:
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continue
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found.append(
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{
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"tag": tag,
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"handle": rec.handle,
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"at": [at[0], at[1]],
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"space": rec.space,
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"layer": rec.layer,
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"cluster": prep["membership"].get(rec.handle) if rec.space == "Model" else None,
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"source": source,
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"confidence": confidence,
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}
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)
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found.sort(key=lambda o: (-o["confidence"], o["tag"], o["handle"]))
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kept: list[dict] = []
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for occ in found:
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twin = next(
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(
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k
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for k in kept
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if k["tag"] == occ["tag"]
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and k["space"] == occ["space"]
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and math.dist(k["at"], occ["at"]) <= radius
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),
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None,
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)
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if twin is None:
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kept.append(occ)
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kept.sort(key=lambda o: (o["tag"], o["space"], o["at"][0], o["at"][1]))
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return kept
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def find_rooms(records: Sequence[EntityRecord], *, tol: float, membership: dict) -> dict:
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"""Room labels and the faces of the wall layers they sit in.
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A label is any text `vocab.room_label` recognises. Wall layers are the
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ones `vocab.classify_layer` files under ``architecture``; their straight
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segments go through `engineering.arch.faces.planar_faces` (arcs are left
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out and counted). ``labelled`` pairs each room row with its `Face` for a
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caller that needs the geometry; ``rooms`` is JSON-ready.
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"""
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labels = []
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for rec in records:
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if rec.type in _TEXT_TYPES and rec.text:
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parsed = room_label(rec.text)
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at = _anchor(rec)
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if parsed and at is not None:
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labels.append((rec, parsed, at))
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segments, bulged = wall_segments(records)
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faces: tuple[Face, ...] = ()
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skipped = None
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if len(segments) > FACE_SEGMENT_CAP:
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skipped = (
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f"{len(segments)} wall segments exceed the {FACE_SEGMENT_CAP}-segment cap of the "
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"face finder; rooms are listed by label only"
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)
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elif segments:
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faces = planar_faces(segments, tol=tol)
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rooms: list[dict] = []
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labelled: list[tuple[dict, Face]] = []
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used: set[int] = set()
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for rec, parsed, at in labels:
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face = face_containing(faces, at) if faces else None
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row = {
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"number": parsed.get("number"),
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"name": parsed.get("name"),
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"language": parsed.get("language"),
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"text": rec.text,
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"handle": rec.handle,
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"at": [at[0], at[1]],
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"cluster": membership.get(rec.handle),
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"face": None,
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"source": "label",
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"confidence": ROOM_LABEL_CONFIDENCE,
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}
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if face is not None:
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used.add(id(face))
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row["face"] = {
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"area": face.area,
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"polygon": [[p[0], p[1]] for p in face.loop],
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"centroid": [face.centroid[0], face.centroid[1]],
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}
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row["source"] = "label+face"
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row["confidence"] = ROOM_FACE_CONFIDENCE
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labelled.append((row, face))
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rooms.append(row)
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rooms.sort(key=lambda r: (r["cluster"] or "", r["number"] or "", r["name"] or "", r["at"]))
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return {
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"rooms": rooms,
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"labelled": labelled,
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"faces": len(faces),
|
||||
"faces_unlabelled": sum(1 for f in faces if id(f) not in used),
|
||||
"wall_segments": len(segments),
|
||||
"arcs_skipped": bulged,
|
||||
"skipped": skipped,
|
||||
}
|
||||
|
||||
|
||||
def text_language(text: str) -> str:
|
||||
"""``cyrillic`` | ``turkish`` | ``latin`` | ``none`` by character class, in
|
||||
that precedence (a Russian label with a Latin tag in it is Cyrillic)."""
|
||||
if _CYRILLIC.search(text):
|
||||
return "cyrillic"
|
||||
if _TURKISH.search(text):
|
||||
return "turkish"
|
||||
if _LATIN.search(text):
|
||||
return "latin"
|
||||
return "none"
|
||||
|
||||
|
||||
def _layers(snap: Snapshot, unit_mm: float | None) -> list[dict]:
|
||||
counts: Counter = Counter()
|
||||
lengths: dict[str, float] = {}
|
||||
texts: dict[str, list[str]] = {}
|
||||
for rec in snap.records:
|
||||
counts[rec.layer] += 1
|
||||
lengths[rec.layer] = lengths.get(rec.layer, 0.0) + length_of(rec)
|
||||
if rec.type in _TEXT_TYPES and rec.text:
|
||||
texts.setdefault(rec.layer, []).append(rec.text)
|
||||
rows = []
|
||||
for name in sorted(set(snap.layers) | set(counts)):
|
||||
props = snap.layers.get(name, {})
|
||||
c = classify_layer(name)
|
||||
row = {
|
||||
"name": name,
|
||||
"entities": counts.get(name, 0),
|
||||
"length": lengths.get(name, 0.0),
|
||||
"length_m": lengths.get(name, 0.0) * unit_mm / 1000.0 if unit_mm else None,
|
||||
"color": props.get("color"),
|
||||
"linetype": props.get("linetype"),
|
||||
"discipline": c.get("discipline", "unknown"),
|
||||
"service": c.get("service", "unknown"),
|
||||
"keyword": c.get("keyword"),
|
||||
"language": c.get("language"),
|
||||
"confidence": c.get("confidence", 0.0),
|
||||
"evidence": f"layer name keyword {c.get('keyword')!r}" if c.get("keyword") else None,
|
||||
}
|
||||
if row["discipline"] == "unknown" and row["service"] == "unknown":
|
||||
votes: Counter = Counter()
|
||||
said = texts.get(name, [])
|
||||
for text in said:
|
||||
t = classify_layer(text)
|
||||
if (
|
||||
t.get("discipline", "unknown") != "unknown"
|
||||
or t.get("service", "unknown") != "unknown"
|
||||
):
|
||||
votes[(t.get("discipline", "unknown"), t.get("service", "unknown"))] += 1
|
||||
if votes:
|
||||
(discipline, service), n = sorted(votes.items(), key=lambda kv: (-kv[1], kv[0]))[0]
|
||||
row.update(
|
||||
discipline=discipline,
|
||||
service=service,
|
||||
confidence=(
|
||||
TEXT_VOTE_CONFIDENCE if n * 2 > len(said) else WEAK_VOTE_CONFIDENCE
|
||||
),
|
||||
evidence=f"{n} of {len(said)} texts on the layer read as {service} / {discipline}",
|
||||
)
|
||||
rows.append(row)
|
||||
rows.sort(key=lambda r: (-r["entities"], r["name"]))
|
||||
return rows
|
||||
|
||||
|
||||
def _block_kind(name: str) -> str | None:
|
||||
for token in _TOKEN_RE.findall(name.upper()):
|
||||
kind = KEYWORDS.get(token)
|
||||
if kind:
|
||||
return kind
|
||||
return None
|
||||
|
||||
|
||||
def _blocks(snap: Snapshot) -> dict:
|
||||
inserts = [r for r in snap.records if r.type == "INSERT" and r.block]
|
||||
named = Counter(r.block for r in inserts if not r.block.startswith("*"))
|
||||
anonymous = Counter(r.block for r in inserts if r.block.startswith("*"))
|
||||
rows = [
|
||||
{
|
||||
"name": name,
|
||||
"inserts": n,
|
||||
"kind": _block_kind(name),
|
||||
"discipline": classify_layer(name).get("discipline", "unknown"),
|
||||
}
|
||||
for name, n in sorted(named.items(), key=lambda kv: (-kv[1], kv[0]))
|
||||
]
|
||||
kinds = Counter(row["kind"] for row in rows if row["kind"])
|
||||
return {
|
||||
"inserts": len(inserts),
|
||||
"named": rows[:BLOCK_ROW_CAP],
|
||||
"named_total": len(rows),
|
||||
"anonymous": {"names": len(anonymous), "inserts": sum(anonymous.values())},
|
||||
"kinds": dict(sorted(kinds.items())),
|
||||
}
|
||||
|
||||
|
||||
def _title_block(snap: Snapshot) -> dict:
|
||||
best = None
|
||||
for rec in snap.records:
|
||||
if rec.type != "INSERT" or len(rec.attribs) < 3:
|
||||
continue
|
||||
upper = (rec.block or "").upper()
|
||||
named = any(word in upper for word in TITLE_WORDS)
|
||||
key = (named, rec.space != "Model", len(rec.attribs))
|
||||
if best is None or key > best[0]:
|
||||
best = (key, rec, named)
|
||||
if best is None:
|
||||
return {"found": False, "reason": "no block reference with three or more attributes"}
|
||||
_key, rec, named = best
|
||||
return {
|
||||
"found": True,
|
||||
"handle": rec.handle,
|
||||
"block": rec.block,
|
||||
"space": rec.space,
|
||||
"attributes": {tag: value for tag, value in rec.attribs},
|
||||
"confidence": TITLE_NAME_CONFIDENCE if named else TITLE_ATTRIBUTE_CONFIDENCE,
|
||||
"evidence": (
|
||||
f"block name {rec.block!r} reads as a title block"
|
||||
if named
|
||||
else f"the block reference with the most attributes ({len(rec.attribs)})"
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def _languages(snap: Snapshot, layer_rows: list[dict]) -> dict:
|
||||
by_class: Counter = Counter()
|
||||
samples: dict[str, list[str]] = {}
|
||||
for rec in snap.records:
|
||||
if rec.type in _TEXT_TYPES and rec.text:
|
||||
lang = text_language(rec.text)
|
||||
by_class[lang] += 1
|
||||
if len(samples.setdefault(lang, [])) < 3:
|
||||
samples[lang].append(rec.handle)
|
||||
total = sum(by_class.values())
|
||||
layer_langs = Counter(row["language"] for row in layer_rows if row.get("language"))
|
||||
return {
|
||||
"texts": total,
|
||||
"by_class": {k: by_class.get(k, 0) for k in ("latin", "turkish", "cyrillic", "none")},
|
||||
"share": {
|
||||
k: (by_class.get(k, 0) / total if total else 0.0)
|
||||
for k in ("latin", "turkish", "cyrillic", "none")
|
||||
},
|
||||
"samples": samples,
|
||||
"layer_name_languages": dict(sorted(layer_langs.items())),
|
||||
"confidence": 1.0,
|
||||
}
|
||||
|
||||
|
||||
def describe(snap: Snapshot) -> dict:
|
||||
"""The whole report; see the module docstring for the confidences."""
|
||||
prep = prepare(snap)
|
||||
warnings: list[str] = []
|
||||
tol = prep["diag"] * FACE_TOL_FRACTION if prep["diag"] > 0 else 1.0
|
||||
rooms = find_rooms(prep["kept"], tol=tol, membership=prep["membership"])
|
||||
units = infer_units(
|
||||
snap,
|
||||
records=prep["kept"],
|
||||
room_areas=[row["face"]["area"] for row, _face in rooms["labelled"]],
|
||||
)
|
||||
if units["warning"]:
|
||||
warnings.append(units["warning"])
|
||||
extents = dict(prep["extents"])
|
||||
extents["declared"] = (
|
||||
{"min": list(snap.extmin), "max": list(snap.extmax)}
|
||||
if snap.extmin is not None and snap.extmax is not None
|
||||
else None
|
||||
)
|
||||
if extents["outliers"]:
|
||||
handles = ", ".join(o["handle"] for o in extents["outliers"][:5])
|
||||
stretch = extents["stretch"]
|
||||
warnings.append(
|
||||
f"{len(extents['outliers'])} entit{'y' if len(extents['outliers']) == 1 else 'ies'} "
|
||||
f"far outside the content stretch the extents"
|
||||
+ (f" {stretch:.3g}x" if stretch else "")
|
||||
+ f": {handles}"
|
||||
)
|
||||
if rooms["skipped"]:
|
||||
warnings.append(rooms["skipped"])
|
||||
|
||||
occurrences = tag_occurrences(snap, prep=prep)
|
||||
by_tag: dict[str, list[dict]] = {}
|
||||
for occ in occurrences:
|
||||
by_tag.setdefault(occ["tag"], []).append(occ)
|
||||
tags = [
|
||||
{
|
||||
"tag": tag,
|
||||
"count": len(rows),
|
||||
"ambiguous": len(rows) > 1,
|
||||
"confidence": max(r["confidence"] for r in rows),
|
||||
"occurrences": [
|
||||
{k: r[k] for k in ("handle", "at", "space", "cluster", "layer", "source")}
|
||||
for r in rows
|
||||
],
|
||||
}
|
||||
for tag, rows in sorted(by_tag.items())
|
||||
]
|
||||
repeated = [t["tag"] for t in tags if t["ambiguous"]]
|
||||
if repeated:
|
||||
warnings.append(
|
||||
f"{len(repeated)} tag(s) appear more than once (plan copies, legends, second "
|
||||
f"sheets): {', '.join(repeated[:5])}"
|
||||
)
|
||||
|
||||
layer_rows = _layers(snap, units["mm_per_unit"])
|
||||
layout_names = list(snap.layouts)
|
||||
if "Model" not in layout_names:
|
||||
layout_names.insert(0, "Model")
|
||||
space_counts = Counter(r.space for r in snap.records)
|
||||
return {
|
||||
"source": snap.source,
|
||||
"entities": len(snap.records),
|
||||
"units": units,
|
||||
"extents": extents,
|
||||
"clusters": prep["clusters"],
|
||||
"layers": layer_rows,
|
||||
"equipment_tags": tags,
|
||||
"rooms": rooms["rooms"],
|
||||
"room_faces": {
|
||||
"faces": rooms["faces"],
|
||||
"unlabelled": rooms["faces_unlabelled"],
|
||||
"wall_segments": rooms["wall_segments"],
|
||||
"arcs_skipped": rooms["arcs_skipped"],
|
||||
"tolerance": tol,
|
||||
},
|
||||
"languages": _languages(snap, layer_rows),
|
||||
"blocks": _blocks(snap),
|
||||
"layouts": [{"name": n, "entities": space_counts.get(n, 0)} for n in layout_names],
|
||||
"title_block": _title_block(snap),
|
||||
"warnings": warnings,
|
||||
}
|
||||
@@ -0,0 +1,272 @@
|
||||
"""drawing_scale_check: is this drawing to scale against that one?
|
||||
|
||||
A P&ID and a layout of one plant carry the same equipment tags. If the P&ID
|
||||
were drawn to scale, the distance between two tanks on it would be the distance
|
||||
on the layout times one factor, for every pair. On a schematic it is not - and
|
||||
a pipe length measured on a schematic is a length of drawn line, not of pipe.
|
||||
|
||||
The check, in the order it runs:
|
||||
|
||||
1. **Scope.** Model-space tags are read (`describe.tag_occurrences`) and the
|
||||
drawings split into clusters (`clusters.find_clusters`, outliers left out).
|
||||
Every pair of scopes - the whole drawing or one of its eight largest
|
||||
clusters, on each side - is tried, and the pair that matches the most tags
|
||||
wins (ties: the whole drawing first, then the larger cluster). Two copies
|
||||
of one plan side by side therefore resolve to one copy instead of making
|
||||
every tag ambiguous.
|
||||
2. **Match.** Only a tag found exactly once in each scope is matched. A tag
|
||||
found more than once in either (a plan copy, a legend, a second sheet) is
|
||||
``ambiguous`` and takes no part: picking one of its positions would be a
|
||||
guess.
|
||||
3. **Pairs.** For every two matched tags whose distance on the reference
|
||||
drawing ``b`` is at least ``min_pair_mm``, the ratio of their distance on
|
||||
``a`` to that on ``b`` is taken, both converted to millimetres by each
|
||||
drawing's inferred unit (`units.infer_units`).
|
||||
4. **Statistics.** The median ratio, the quartiles and their range, and the
|
||||
share of pairs within +/-10 % of the median - overall, and per room of the
|
||||
reference drawing (the labelled faces `describe.find_rooms` finds) or, for
|
||||
tags in no room, per cluster.
|
||||
5. **Verdict.** ``to_scale`` when at least 80 % of the pairs are within the
|
||||
band (``factor`` is then the median: a drawing at a different but uniform
|
||||
scale is recognised); ``schematic`` at 50 % or less; ``partly`` in between;
|
||||
``insufficient`` with fewer than three matched tags or no pair above the
|
||||
floor.
|
||||
|
||||
A tag label is not an equipment centre: the band and the floor are there to
|
||||
absorb the offset between the two, and the result says so. Pure; neither
|
||||
drawing is touched.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
import statistics
|
||||
from itertools import combinations
|
||||
|
||||
from engineering.arch.faces import face_containing
|
||||
from engineering.understand.describe import FACE_TOL_FRACTION, find_rooms, prepare, tag_occurrences
|
||||
from engineering.understand.snapshot import Snapshot
|
||||
from engineering.understand.units import infer_units
|
||||
|
||||
__all__ = ["MIN_TAGS", "SCHEMATIC_SHARE", "TO_SCALE_SHARE", "WITHIN", "match_tags", "scale_check"]
|
||||
|
||||
#: The band around the median a pair must fall in to count as consistent.
|
||||
WITHIN = 0.10
|
||||
TO_SCALE_SHARE = 0.80
|
||||
SCHEMATIC_SHARE = 0.50
|
||||
MIN_TAGS = 3
|
||||
#: Clusters per drawing tried as a scope, largest first.
|
||||
MAX_SCOPE_CLUSTERS = 8
|
||||
#: Pair rows listed in the result, farthest from the median first.
|
||||
PAIR_ROW_CAP = 200
|
||||
NOTE = (
|
||||
"Tag labels are not equipment centres; the +/-10 % band and the min_pair_mm floor absorb "
|
||||
"the offset between a label and what it labels."
|
||||
)
|
||||
|
||||
|
||||
def _side(snap: Snapshot) -> dict:
|
||||
prep = prepare(snap)
|
||||
prep["tags"] = [o for o in tag_occurrences(snap, prep=prep) if o["space"] == "Model"]
|
||||
return prep
|
||||
|
||||
|
||||
def _scoped(side: dict, cluster: str | None, name: str) -> list[dict]:
|
||||
if cluster is None:
|
||||
return side["tags"]
|
||||
ids = [c["id"] for c in side["clusters"]]
|
||||
if cluster not in ids:
|
||||
raise ValueError(f"{name}: no cluster {cluster!r}; the drawing has {ids}")
|
||||
return [o for o in side["tags"] if o["cluster"] == cluster]
|
||||
|
||||
|
||||
def _match(side_a: dict, side_b: dict, cluster_a: str | None, cluster_b: str | None) -> dict:
|
||||
occ_a = _scoped(side_a, cluster_a, "cluster_a")
|
||||
occ_b = _scoped(side_b, cluster_b, "cluster_b")
|
||||
count_a: dict[str, list[dict]] = {}
|
||||
count_b: dict[str, list[dict]] = {}
|
||||
for o in occ_a:
|
||||
count_a.setdefault(o["tag"], []).append(o)
|
||||
for o in occ_b:
|
||||
count_b.setdefault(o["tag"], []).append(o)
|
||||
matched = {
|
||||
tag: (tuple(count_a[tag][0]["at"]), tuple(count_b[tag][0]["at"]))
|
||||
for tag in sorted(set(count_a) & set(count_b))
|
||||
if len(count_a[tag]) == 1 and len(count_b[tag]) == 1
|
||||
}
|
||||
ambiguous = sorted(
|
||||
tag
|
||||
for tag in set(count_a) | set(count_b)
|
||||
if len(count_a.get(tag, ())) > 1 or len(count_b.get(tag, ())) > 1
|
||||
)
|
||||
return {
|
||||
"matched": matched,
|
||||
"ambiguous": ambiguous,
|
||||
"occurrences": {
|
||||
tag: {"a": len(count_a.get(tag, ())), "b": len(count_b.get(tag, ()))}
|
||||
for tag in ambiguous
|
||||
},
|
||||
"only_a": sorted(t for t in count_a if t not in count_b and len(count_a[t]) == 1),
|
||||
"only_b": sorted(t for t in count_b if t not in count_a and len(count_b[t]) == 1),
|
||||
"cluster_a": cluster_a,
|
||||
"cluster_b": cluster_b,
|
||||
}
|
||||
|
||||
|
||||
def match_tags(a: Snapshot, b: Snapshot, *, cluster_a=None, cluster_b=None) -> dict:
|
||||
"""``{"matched": {tag: (pa, pb)}, "ambiguous": [tag, ...], ...}`` within the
|
||||
given scopes (a cluster id such as ``"C1"``, or None for the whole model
|
||||
space). ``pa`` / ``pb`` are the tag's positions in drawing units. Also
|
||||
``occurrences`` (per ambiguous tag, how often each side has it),
|
||||
``only_a`` / ``only_b`` and the scopes used. An unknown cluster id is
|
||||
refused with the ids the drawing has."""
|
||||
return _match(_side(a), _side(b), cluster_a, cluster_b)
|
||||
|
||||
|
||||
def _stats(ratios: list[float]) -> dict:
|
||||
if not ratios:
|
||||
return {"median": None, "q1": None, "q3": None, "iqr": None, "within_10pct": None}
|
||||
median = statistics.median(ratios)
|
||||
if len(ratios) >= 2:
|
||||
q1, _q2, q3 = statistics.quantiles(ratios, n=4, method="inclusive")
|
||||
else:
|
||||
q1 = q3 = ratios[0]
|
||||
within = sum(1 for r in ratios if abs(r - median) <= WITHIN * median) / len(ratios)
|
||||
return {"median": median, "q1": q1, "q3": q3, "iqr": q3 - q1, "within_10pct": within}
|
||||
|
||||
|
||||
def _verdict(tag_count: int, pair_count: int, stats: dict) -> str:
|
||||
if tag_count < MIN_TAGS or pair_count == 0 or not stats["median"] or stats["median"] <= 0:
|
||||
return "insufficient"
|
||||
if stats["within_10pct"] >= TO_SCALE_SHARE:
|
||||
return "to_scale"
|
||||
if stats["within_10pct"] <= SCHEMATIC_SHARE:
|
||||
return "schematic"
|
||||
return "partly"
|
||||
|
||||
|
||||
def _summary(tags: list[str], rows: list[dict]) -> dict:
|
||||
stats = _stats([r["ratio"] for r in rows])
|
||||
verdict = _verdict(len(tags), len(rows), stats)
|
||||
return {
|
||||
"verdict": verdict,
|
||||
"factor": stats["median"] if verdict == "to_scale" else None,
|
||||
**stats,
|
||||
"pair_count": len(rows),
|
||||
}
|
||||
|
||||
|
||||
def scale_check(a: Snapshot, b: Snapshot, *, min_pair_mm=2000.0) -> dict:
|
||||
"""Is drawing ``a`` to scale against the reference drawing ``b``?
|
||||
|
||||
Returns ``{"verdict", "factor", "median", "q1", "q3", "iqr", "within_10pct",
|
||||
"pair_count", "pairs", "pairs_truncated", "pairs_below_floor", "matched",
|
||||
"ambiguous", "occurrences", "only_a", "only_b", "groups", "scope", "units",
|
||||
"min_pair_mm", "thresholds", "notes"}``. ``pairs`` lists at most 200 rows,
|
||||
farthest from the median first; ``matched`` is ``[{"tag", "a": [x, y],
|
||||
"b": [x, y]}]`` in drawing units. Refused before anything is computed: a
|
||||
``min_pair_mm`` that is not a positive finite number.
|
||||
"""
|
||||
try:
|
||||
floor = float(min_pair_mm)
|
||||
except (TypeError, ValueError):
|
||||
raise ValueError(f"min_pair_mm: expected a number, got {min_pair_mm!r}") from None
|
||||
if not math.isfinite(floor) or floor <= 0.0:
|
||||
raise ValueError(f"min_pair_mm: must be a positive number of millimetres, got {floor:g}")
|
||||
side_a, side_b = _side(a), _side(b)
|
||||
|
||||
best = None
|
||||
for ca in [None] + [c["id"] for c in side_a["clusters"][:MAX_SCOPE_CLUSTERS]]:
|
||||
for cb in [None] + [c["id"] for c in side_b["clusters"][:MAX_SCOPE_CLUSTERS]]:
|
||||
m = _match(side_a, side_b, ca, cb)
|
||||
if best is None or len(m["matched"]) > len(best["matched"]):
|
||||
best = m
|
||||
|
||||
tol_b = side_b["diag"] * FACE_TOL_FRACTION if side_b["diag"] > 0 else 1.0
|
||||
rooms_b = find_rooms(side_b["kept"], tol=tol_b, membership=side_b["membership"])
|
||||
units = {}
|
||||
notes = [NOTE]
|
||||
for key, snap, side, rooms in (
|
||||
("a", a, side_a, None),
|
||||
("b", b, side_b, rooms_b),
|
||||
):
|
||||
areas = [row["face"]["area"] for row, _f in rooms["labelled"]] if rooms else []
|
||||
u = infer_units(snap, records=side["kept"], room_areas=areas)
|
||||
units[key] = {
|
||||
"unit": u["inferred"],
|
||||
"mm_per_unit": u["mm_per_unit"],
|
||||
"declared": u["declared"],
|
||||
}
|
||||
if u["mm_per_unit"] is None:
|
||||
notes.append(
|
||||
f"drawing {key}: no unit could be settled; its drawing units are taken as mm"
|
||||
)
|
||||
fa = units["a"]["mm_per_unit"] or 1.0
|
||||
fb = units["b"]["mm_per_unit"] or 1.0
|
||||
|
||||
matched = best["matched"]
|
||||
tags = sorted(matched)
|
||||
rows: list[dict] = []
|
||||
below = 0
|
||||
for ti, tj in combinations(tags, 2):
|
||||
d_b = math.dist(matched[ti][1], matched[tj][1]) * fb
|
||||
if d_b < floor:
|
||||
below += 1
|
||||
continue
|
||||
d_a = math.dist(matched[ti][0], matched[tj][0]) * fa
|
||||
rows.append({"tags": [ti, tj], "a_mm": d_a, "b_mm": d_b, "ratio": d_a / d_b})
|
||||
overall = _summary(tags, rows)
|
||||
|
||||
faces = [face for _row, face in rooms_b["labelled"]]
|
||||
room_of = {id(face): row for row, face in rooms_b["labelled"]}
|
||||
groups: dict[str, dict] = {}
|
||||
for tag in tags:
|
||||
pb = matched[tag][1]
|
||||
face = face_containing(faces, pb) if faces else None
|
||||
if face is not None:
|
||||
row = room_of[id(face)]
|
||||
key = " ".join(p for p in ("room", row["number"], row["name"]) if p)
|
||||
kind = "room"
|
||||
else:
|
||||
cluster = next(
|
||||
(o["cluster"] for o in side_b["tags"] if o["tag"] == tag and tuple(o["at"]) == pb),
|
||||
None,
|
||||
)
|
||||
key, kind = (cluster or "unclustered"), "cluster"
|
||||
groups.setdefault(key, {"group": key, "kind": kind, "tags": []})["tags"].append(tag)
|
||||
group_rows = []
|
||||
for key in sorted(groups):
|
||||
g = groups[key]
|
||||
if len(g["tags"]) < 2:
|
||||
continue
|
||||
members = set(g["tags"])
|
||||
inside = [r for r in rows if r["tags"][0] in members and r["tags"][1] in members]
|
||||
group_rows.append({**g, **_summary(g["tags"], inside)})
|
||||
|
||||
median = overall["median"]
|
||||
listed = sorted(
|
||||
rows,
|
||||
key=lambda r: (-(abs(r["ratio"] - median) if median else 0.0), r["tags"]),
|
||||
)[:PAIR_ROW_CAP]
|
||||
return {
|
||||
**overall,
|
||||
"pairs": listed,
|
||||
"pairs_truncated": len(rows) > PAIR_ROW_CAP,
|
||||
"pairs_below_floor": below,
|
||||
"matched": [{"tag": t, "a": list(matched[t][0]), "b": list(matched[t][1])} for t in tags],
|
||||
"ambiguous": best["ambiguous"],
|
||||
"occurrences": best["occurrences"],
|
||||
"only_a": best["only_a"],
|
||||
"only_b": best["only_b"],
|
||||
"groups": group_rows,
|
||||
"scope": {"cluster_a": best["cluster_a"], "cluster_b": best["cluster_b"]},
|
||||
"units": units,
|
||||
"min_pair_mm": floor,
|
||||
"thresholds": {
|
||||
"within": WITHIN,
|
||||
"to_scale_share": TO_SCALE_SHARE,
|
||||
"schematic_share": SCHEMATIC_SHARE,
|
||||
"min_tags": MIN_TAGS,
|
||||
},
|
||||
"notes": notes,
|
||||
}
|
||||
@@ -0,0 +1,399 @@
|
||||
"""Declared versus inferred drawing units, and extents that one stray entity cannot stretch.
|
||||
|
||||
`$INSUNITS` is a claim the drawing makes about itself, and a foreign drawing
|
||||
can make it wrongly: a layout that declares inches over millimetre geometry
|
||||
reads 25.4 times too large if the header is believed. This module never corrects
|
||||
the claim silently. It measures what the geometry implies and reports both,
|
||||
with the numbers that decided it.
|
||||
|
||||
**Evidence.** Each item is one median measured in drawing units and the band a
|
||||
real drawing puts it in, in metres. A candidate unit is *plausible* for an item
|
||||
when the measurement, converted by that unit, lands in the band. An item that
|
||||
every candidate or no candidate satisfies says nothing and is dropped as
|
||||
inconclusive.
|
||||
|
||||
======================== ====================== ====== ====================================
|
||||
item band weight basis
|
||||
======================== ====================== ====== ====================================
|
||||
``room_area`` 1 m² … 5 000 m² 1.0 a labelled room's face (this reader's
|
||||
heuristic)
|
||||
``wall_thickness`` 0.05 m … 1.0 m 1.0 nearest parallel wall line (heuristic)
|
||||
``door_leaf`` 0.5 m … 1.5 m 1.0 radius of a quarter-circle arc, a door
|
||||
swing (heuristic)
|
||||
``text_height`` 1.8 mm … 4.0 m 0.5 ISO 3098-0 lettering heights 1.8-20 mm
|
||||
at ISO 5455 scales 1:1 to 1:200 (the
|
||||
1:200 ceiling is this reader's choice
|
||||
for plant drawings)
|
||||
``overall_size`` 0.01 m … 2 000 m 0.25 the larger side of the robust extents
|
||||
(heuristic: a plant or a building)
|
||||
======================== ====================== ====== ====================================
|
||||
|
||||
**Decision.** A candidate scores the weights of the items it is plausible for;
|
||||
the header is evidence too and adds :data:`HEADER_WEIGHT` to the declared unit.
|
||||
The highest score wins and a tie goes to the declared unit, then to the order of
|
||||
:data:`PREFERENCE`. So the header is overruled only when the geometry
|
||||
outweighs it - one weak item (the overall size) never does.
|
||||
|
||||
**Extents.** The body of the drawing is grown from its middle: the seed is
|
||||
the quartile box of the entity centres (Q1..Q3 on each axis), and every
|
||||
entity whose box lies within :data:`OUTLIER_REACH` body diagonals of the body
|
||||
joins it, pass after pass, until a pass adds nothing. What never joins is an
|
||||
outlier - an entity farther from everything else than the drawing is large.
|
||||
The robust extents are the body's box. This is this reader's heuristic, not
|
||||
a statistical standard: a per-axis fence on centres (Tukey's) flags the edge
|
||||
walls of a plan whose centres crowd its middle, and a quantile range is
|
||||
dragged by the outlier itself in a small drawing. Fewer than four entities
|
||||
have no outliers.
|
||||
|
||||
Pure: records in, dicts out. The drawing is never touched.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
import statistics
|
||||
from collections.abc import Iterable, Sequence
|
||||
|
||||
from ezdxf import units as ezdxf_units
|
||||
|
||||
from engineering.understand.snapshot import EntityRecord, Snapshot
|
||||
from engineering.understand.vocab import classify_layer
|
||||
|
||||
__all__ = [
|
||||
"BANDS",
|
||||
"CANDIDATES",
|
||||
"HEADER_WEIGHT",
|
||||
"MM_PER_UNIT",
|
||||
"PREFERENCE",
|
||||
"entity_box",
|
||||
"infer_units",
|
||||
"robust_extents",
|
||||
"wall_segments",
|
||||
]
|
||||
|
||||
#: Millimetres per drawing unit, exact by definition: the SI prefixes, and the
|
||||
#: inch (25.4 mm) and foot (304.8 mm) of the 1959 international yard and pound
|
||||
#: agreement. ezdxf's own ``METER_FACTOR`` is rounded (39.37007874 in/m), so it
|
||||
#: is not used for conversion.
|
||||
MM_PER_UNIT: dict[str, float] = {"mm": 1.0, "cm": 10.0, "m": 1000.0, "in": 25.4, "ft": 304.8}
|
||||
CANDIDATES: tuple[str, ...] = ("mm", "cm", "m", "in", "ft")
|
||||
#: Tie-break after the declared unit.
|
||||
PREFERENCE: tuple[str, ...] = ("mm", "m", "cm", "in", "ft")
|
||||
#: What the header's own claim weighs against the geometry.
|
||||
HEADER_WEIGHT = 0.5
|
||||
#: (low, high, weight, power): the band in metres (m² for power 2).
|
||||
BANDS: dict[str, tuple[float, float, float, int]] = {
|
||||
"room_area": (1.0, 5000.0, 1.0, 2),
|
||||
"wall_thickness": (0.05, 1.0, 1.0, 1),
|
||||
"door_leaf": (0.5, 1.5, 1.0, 1),
|
||||
"text_height": (0.0018, 4.0, 0.5, 1),
|
||||
"overall_size": (0.01, 2000.0, 0.25, 1),
|
||||
}
|
||||
#: How far (in body diagonals) an entity may lie from the body and still join it.
|
||||
OUTLIER_REACH = 1.0
|
||||
#: A door swing is a quarter circle; the sweep may be drawn a little off.
|
||||
DOOR_SWEEP = (85.0, 95.0)
|
||||
#: Wall-thickness pairing reads at most this many wall segments.
|
||||
WALL_SEGMENT_CAP = 3000
|
||||
|
||||
_TEXT_TYPES = frozenset({"TEXT", "MTEXT"})
|
||||
_POLY_TYPES = frozenset({"LWPOLYLINE", "POLYLINE"})
|
||||
|
||||
|
||||
def entity_box(rec: EntityRecord) -> tuple[float, float, float, float] | None:
|
||||
"""The record's WCS box: its own ``bbox``, else its points (a circle or arc
|
||||
grown by its radius about its centre), else None."""
|
||||
if rec.bbox is not None:
|
||||
return tuple(float(v) for v in rec.bbox)
|
||||
if not rec.points:
|
||||
return None
|
||||
xs = [float(p[0]) for p in rec.points]
|
||||
ys = [float(p[1]) for p in rec.points]
|
||||
grow = float(rec.radius) if rec.type in ("CIRCLE", "ARC") and rec.radius else 0.0
|
||||
return (min(xs) - grow, min(ys) - grow, max(xs) + grow, max(ys) + grow)
|
||||
|
||||
|
||||
def _union(boxes: Iterable[tuple[float, float, float, float]]):
|
||||
boxes = list(boxes)
|
||||
if not boxes:
|
||||
return None
|
||||
return (
|
||||
min(b[0] for b in boxes),
|
||||
min(b[1] for b in boxes),
|
||||
max(b[2] for b in boxes),
|
||||
max(b[3] for b in boxes),
|
||||
)
|
||||
|
||||
|
||||
def _diag(box) -> float:
|
||||
return math.hypot(box[2] - box[0], box[3] - box[1]) if box else 0.0
|
||||
|
||||
|
||||
def _box_gap(a, b) -> float:
|
||||
"""Distance between two boxes; 0 when they touch or overlap."""
|
||||
dx = max(a[0] - b[2], b[0] - a[2], 0.0)
|
||||
dy = max(a[1] - b[3], b[1] - a[3], 0.0)
|
||||
return math.hypot(dx, dy)
|
||||
|
||||
|
||||
def _grow_body(boxes: Sequence[tuple[float, float, float, float]]) -> set[int]:
|
||||
"""Indices of the boxes that never join the body grown from the centre."""
|
||||
if len(boxes) < 4:
|
||||
return set()
|
||||
xs = [(b[0] + b[2]) / 2.0 for b in boxes]
|
||||
ys = [(b[1] + b[3]) / 2.0 for b in boxes]
|
||||
qx = statistics.quantiles(xs, n=4, method="inclusive")
|
||||
qy = statistics.quantiles(ys, n=4, method="inclusive")
|
||||
body = (qx[0], qy[0], qx[2], qy[2])
|
||||
floor = statistics.median(_diag(b) for b in boxes)
|
||||
pending = set(range(len(boxes)))
|
||||
while pending:
|
||||
reach = OUTLIER_REACH * max(_diag(body), floor)
|
||||
joined = [i for i in pending if _box_gap(boxes[i], body) <= reach]
|
||||
if not joined:
|
||||
break
|
||||
for i in joined:
|
||||
body = _union([body, boxes[i]])
|
||||
pending.difference_update(joined)
|
||||
return pending
|
||||
|
||||
|
||||
def _distance_to_box(x: float, y: float, box) -> float:
|
||||
dx = max(box[0] - x, 0.0, x - box[2])
|
||||
dy = max(box[1] - y, 0.0, y - box[3])
|
||||
return math.hypot(dx, dy)
|
||||
|
||||
|
||||
def robust_extents(records: Sequence[EntityRecord]) -> dict:
|
||||
"""Computed and robust extents of ``records`` and the outliers between them.
|
||||
|
||||
``outliers`` are sorted farthest first, each with its handle, type, layer,
|
||||
centre and its distance from the robust box. ``stretch`` is the computed
|
||||
diagonal over the robust one (None when the robust box is a point).
|
||||
"""
|
||||
items = [(rec, box) for rec in records if (box := entity_box(rec)) is not None]
|
||||
centres = [((b[0] + b[2]) / 2.0, (b[1] + b[3]) / 2.0) for _rec, b in items]
|
||||
skip = _grow_body([b for _rec, b in items])
|
||||
outside = sorted(skip)
|
||||
computed = _union(b for _rec, b in items)
|
||||
robust = _union(b for i, (_rec, b) in enumerate(items) if i not in skip)
|
||||
outliers = []
|
||||
for i in outside:
|
||||
rec, _box = items[i]
|
||||
cx, cy = centres[i]
|
||||
outliers.append(
|
||||
{
|
||||
"handle": rec.handle,
|
||||
"type": rec.type,
|
||||
"layer": rec.layer,
|
||||
"at": [cx, cy],
|
||||
"distance": _distance_to_box(cx, cy, robust) if robust else None,
|
||||
"confidence": 0.9,
|
||||
}
|
||||
)
|
||||
outliers.sort(key=lambda o: (-(o["distance"] or 0.0), o["handle"]))
|
||||
robust_diag = _diag(robust)
|
||||
return {
|
||||
"computed": list(computed) if computed else None,
|
||||
"robust": list(robust) if robust else None,
|
||||
"outliers": outliers,
|
||||
"stretch": (_diag(computed) / robust_diag) if robust_diag > 0.0 else None,
|
||||
"method": (
|
||||
"body grown from the quartile box of entity centres; an entity farther from it "
|
||||
f"than {OUTLIER_REACH:g} body diagonal(s) never joins and is an outlier"
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def wall_segments(
|
||||
records: Sequence[EntityRecord], *, is_wall=None
|
||||
) -> tuple[list[tuple[tuple[float, float], tuple[float, float]]], int]:
|
||||
"""Straight segments of LINE and polyline records on wall layers, plus the
|
||||
number of bulged (arc) polyline segments left out.
|
||||
|
||||
``is_wall(layer)`` defaults to `vocab.classify_layer` naming the
|
||||
``architecture`` discipline.
|
||||
"""
|
||||
cache: dict[str, bool] = {}
|
||||
|
||||
def wall(layer: str) -> bool:
|
||||
if layer not in cache:
|
||||
if is_wall is not None:
|
||||
cache[layer] = bool(is_wall(layer))
|
||||
else:
|
||||
cache[layer] = classify_layer(layer).get("discipline") == "architecture"
|
||||
return cache[layer]
|
||||
|
||||
segments: list[tuple[tuple[float, float], tuple[float, float]]] = []
|
||||
bulged = 0
|
||||
for rec in records:
|
||||
if not wall(rec.layer):
|
||||
continue
|
||||
if rec.type == "LINE" and len(rec.points) >= 2:
|
||||
segments.append((tuple(rec.points[0]), tuple(rec.points[1])))
|
||||
elif rec.type in _POLY_TYPES and len(rec.points) >= 2:
|
||||
pts = list(rec.points)
|
||||
count = len(pts) if rec.closed else len(pts) - 1
|
||||
for i in range(count):
|
||||
a, b = pts[i], pts[(i + 1) % len(pts)]
|
||||
bulge = rec.bulges[i] if i < len(rec.bulges) else 0.0
|
||||
if abs(bulge) > 1e-12:
|
||||
bulged += 1
|
||||
continue
|
||||
if a != b:
|
||||
segments.append((tuple(a), tuple(b)))
|
||||
return segments, bulged
|
||||
|
||||
|
||||
def _wall_thickness(segments) -> tuple[float | None, int]:
|
||||
"""Median distance from an axis-parallel wall segment to its nearest parallel
|
||||
neighbour on either side that overlaps it; (None, 0) without a pair.
|
||||
|
||||
Both sides, because the inner face of a double-line wall has the room on
|
||||
one side and its own outer face on the other: looking one way only reads
|
||||
half the faces as room widths and the median lands on neither."""
|
||||
horizontal: list[tuple[float, float, float]] = [] # (y, x0, x1)
|
||||
vertical: list[tuple[float, float, float]] = [] # (x, y0, y1)
|
||||
for (ax, ay), (bx, by) in segments[:WALL_SEGMENT_CAP]:
|
||||
length = math.hypot(bx - ax, by - ay)
|
||||
if length <= 0.0:
|
||||
continue
|
||||
if abs(by - ay) <= 1e-9 * length:
|
||||
horizontal.append((ay, min(ax, bx), max(ax, bx)))
|
||||
elif abs(bx - ax) <= 1e-9 * length:
|
||||
vertical.append((ax, min(ay, by), max(ay, by)))
|
||||
gaps: list[float] = []
|
||||
for group in (horizontal, vertical):
|
||||
group.sort()
|
||||
for i, (c, lo, hi) in enumerate(group):
|
||||
best = None
|
||||
for step in (1, -1):
|
||||
j = i + step
|
||||
while 0 <= j < len(group):
|
||||
c2, lo2, hi2 = group[j]
|
||||
d = abs(c2 - c)
|
||||
if best is not None and d >= best:
|
||||
break
|
||||
if d > 0.0 and lo2 < hi and lo < hi2:
|
||||
best = d
|
||||
j += step
|
||||
if best is not None:
|
||||
gaps.append(best)
|
||||
if not gaps:
|
||||
return None, 0
|
||||
return statistics.median(gaps), len(gaps)
|
||||
|
||||
|
||||
def _declared(code: int) -> dict:
|
||||
try:
|
||||
name = ezdxf_units.decode(int(code))
|
||||
label = ezdxf_units.unit_name(int(code))
|
||||
except (IndexError, ValueError, TypeError):
|
||||
name, label = None, None
|
||||
return {"code": int(code), "name": name, "label": label or "Unitless"}
|
||||
|
||||
|
||||
def infer_units(
|
||||
snap: Snapshot,
|
||||
*,
|
||||
records: Sequence[EntityRecord] | None = None,
|
||||
room_areas: Sequence[float] = (),
|
||||
) -> dict:
|
||||
"""The unit the geometry implies, against the one ``$INSUNITS`` declares.
|
||||
|
||||
``records`` defaults to the snapshot's model-space records; pass the ones
|
||||
left after the outliers are removed. ``room_areas`` are labelled room faces
|
||||
in square drawing units. Every item of ``evidence`` carries its median,
|
||||
its sample count, what it is in metres under each candidate, the
|
||||
candidates it is plausible for and whether it was informative.
|
||||
"""
|
||||
recs = [r for r in snap.records if r.space == "Model"] if records is None else list(records)
|
||||
measured: dict[str, tuple[float, int]] = {}
|
||||
|
||||
heights = [float(r.height) for r in recs if r.type in _TEXT_TYPES and r.height and r.height > 0]
|
||||
if heights:
|
||||
measured["text_height"] = (statistics.median(heights), len(heights))
|
||||
radii = []
|
||||
for r in recs:
|
||||
if r.type == "ARC" and r.radius and r.angles:
|
||||
sweep = (float(r.angles[1]) - float(r.angles[0])) % 360.0
|
||||
if DOOR_SWEEP[0] <= sweep <= DOOR_SWEEP[1]:
|
||||
radii.append(float(r.radius))
|
||||
if radii:
|
||||
measured["door_leaf"] = (statistics.median(radii), len(radii))
|
||||
thickness, pairs = _wall_thickness(wall_segments(recs)[0])
|
||||
if thickness is not None:
|
||||
measured["wall_thickness"] = (thickness, pairs)
|
||||
areas = [float(a) for a in room_areas if a and a > 0]
|
||||
if areas:
|
||||
measured["room_area"] = (statistics.median(areas), len(areas))
|
||||
box = robust_extents(recs)["robust"]
|
||||
if box:
|
||||
side = max(box[2] - box[0], box[3] - box[1])
|
||||
if side > 0:
|
||||
measured["overall_size"] = (side, len(recs))
|
||||
|
||||
evidence = []
|
||||
for kind, (value, count) in measured.items():
|
||||
low, high, weight, power = BANDS[kind]
|
||||
physical = {u: value * (MM_PER_UNIT[u] / 1000.0) ** power for u in CANDIDATES}
|
||||
plausible = [u for u in CANDIDATES if low <= physical[u] <= high]
|
||||
evidence.append(
|
||||
{
|
||||
"kind": kind,
|
||||
"value": value,
|
||||
"count": count,
|
||||
"band": [low, high],
|
||||
"band_unit": "m2" if power == 2 else "m",
|
||||
"weight": weight,
|
||||
"as": physical,
|
||||
"plausible": plausible,
|
||||
"informative": 0 < len(plausible) < len(CANDIDATES),
|
||||
}
|
||||
)
|
||||
|
||||
declared = _declared(snap.insunits)
|
||||
name = declared["name"]
|
||||
informative = [e for e in evidence if e["informative"]]
|
||||
scores = {u: sum(e["weight"] for e in informative if u in e["plausible"]) for u in CANDIDATES}
|
||||
if name in scores:
|
||||
scores[name] += HEADER_WEIGHT
|
||||
if informative:
|
||||
best = max(scores.values())
|
||||
order = ([name] if name in scores else []) + [u for u in PREFERENCE if u != name]
|
||||
inferred = next(u for u in order if scores[u] == best)
|
||||
total = sum(e["weight"] for e in informative) + (HEADER_WEIGHT if name in scores else 0.0)
|
||||
confidence = round(0.9 * scores[inferred] / total, 2)
|
||||
elif name in MM_PER_UNIT:
|
||||
inferred, confidence = name, 0.4
|
||||
else:
|
||||
inferred, confidence = None, 0.0
|
||||
|
||||
warning = None
|
||||
if inferred is not None and inferred != name:
|
||||
support = [e for e in informative if inferred in e["plausible"]]
|
||||
detail = "; ".join(
|
||||
f"{e['kind']} {e['value']:g} = {e['as'][inferred]:.4g} {e['band_unit']} as {inferred}"
|
||||
+ (f", {e['as'][name]:.4g} {e['band_unit']} as {name}" if name in MM_PER_UNIT else "")
|
||||
for e in support
|
||||
)
|
||||
warning = (
|
||||
f"INSUNITS declares {declared['label']} ({declared['code']}) but the geometry reads "
|
||||
f"as {inferred}: {detail}. Lengths are measured in {inferred}; the header is not "
|
||||
"changed."
|
||||
)
|
||||
elif inferred is None:
|
||||
warning = (
|
||||
f"INSUNITS declares {declared['label']} ({declared['code']}) and no measurement "
|
||||
"settles the unit; lengths stay in drawing units."
|
||||
)
|
||||
return {
|
||||
"declared": declared,
|
||||
"inferred": inferred,
|
||||
"mm_per_unit": MM_PER_UNIT.get(inferred) if inferred else None,
|
||||
"agree": inferred is not None and inferred == name,
|
||||
"confidence": confidence,
|
||||
"scores": scores,
|
||||
"evidence": evidence,
|
||||
"warning": warning,
|
||||
}
|
||||
Reference in New Issue
Block a user