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Определение специфических рекомендаций (матрица профилей §8):
1. Ж/д слой (закрыт мёртвый railway ×2.5 у РАС):
- /api/v1/water/{case_id} отдаёт railway=rail как LineString
(без service/industrial/military веток), кэш общий v2;
- railway_warning «перекрыть/проверить немедленно» по профилям;
- SearchMap: Polyline слой ж/д (тёмно-красный), счётчики 💧/🚂.
2. cant_swim → профиль не_умеет_плавать (water ×3.0, без изменения
радиуса, critical_warning «обследовать водоёмы НЕМЕДЛЕННО»):
- раньше чекбокс влиял только на текст, в скоринге был пробел;
- derive в analyze._derive_profiles — работает и для closed_cases.
3. unmodeled_profiles: ДЦП/слабое зрение/слух — честная пометка
«вне поведенческой модели» с пояснением (vector_tasks B12:
профили без аналога не выдавать за учтённые); блок на фронте
в карточке здоровья.
Площадь воды: сферический эксцесс, проверен на квадрате 53° (744017 м²
vs 743272 точного). Тесты: 202 passed (новый test_cant_swim_profile).
121 lines
3.4 KiB
Python
121 lines
3.4 KiB
Python
# util/topological.py
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# Copyright (C) 2005-2026 the SQLAlchemy authors and contributors
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# <see AUTHORS file>
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#
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# This module is part of SQLAlchemy and is released under
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# the MIT License: https://www.opensource.org/licenses/mit-license.php
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"""Topological sorting algorithms."""
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from __future__ import annotations
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from typing import Any
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from typing import Collection
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from typing import DefaultDict
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from typing import Iterable
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from typing import Iterator
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from typing import Sequence
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from typing import Set
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from typing import Tuple
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from typing import TypeVar
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from .. import util
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from ..exc import CircularDependencyError
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_T = TypeVar("_T", bound=Any)
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__all__ = ["sort", "sort_as_subsets", "find_cycles"]
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def sort_as_subsets(
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tuples: Collection[Tuple[_T, _T]], allitems: Collection[_T]
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) -> Iterator[Sequence[_T]]:
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edges: DefaultDict[_T, Set[_T]] = util.defaultdict(set)
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for parent, child in tuples:
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edges[child].add(parent)
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todo = list(allitems)
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todo_set = set(allitems)
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while todo_set:
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output = []
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for node in todo:
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if todo_set.isdisjoint(edges[node]):
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output.append(node)
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if not output:
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raise CircularDependencyError(
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"Circular dependency detected.",
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find_cycles(tuples, allitems),
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_gen_edges(edges),
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)
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todo_set.difference_update(output)
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todo = [t for t in todo if t in todo_set]
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yield output
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def sort(
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tuples: Collection[Tuple[_T, _T]],
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allitems: Collection[_T],
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deterministic_order: bool = True,
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) -> Iterator[_T]:
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"""sort the given list of items by dependency.
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'tuples' is a list of tuples representing a partial ordering.
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deterministic_order is no longer used, the order is now always
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deterministic given the order of "allitems". the flag is there
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for backwards compatibility with Alembic.
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"""
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for set_ in sort_as_subsets(tuples, allitems):
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yield from set_
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def find_cycles(
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tuples: Iterable[Tuple[_T, _T]], allitems: Iterable[_T]
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) -> Set[_T]:
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# adapted from:
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# https://neopythonic.blogspot.com/2009/01/detecting-cycles-in-directed-graph.html
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edges: DefaultDict[_T, Set[_T]] = util.defaultdict(set)
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for parent, child in tuples:
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edges[parent].add(child)
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nodes_to_test = set(edges)
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output = set()
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# we'd like to find all nodes that are
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# involved in cycles, so we do the full
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# pass through the whole thing for each
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# node in the original list.
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# we can go just through parent edge nodes.
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# if a node is only a child and never a parent,
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# by definition it can't be part of a cycle. same
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# if it's not in the edges at all.
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for node in nodes_to_test:
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stack = [node]
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todo = nodes_to_test.difference(stack)
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while stack:
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top = stack[-1]
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for node in edges[top]:
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if node in stack:
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cyc = stack[stack.index(node) :]
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todo.difference_update(cyc)
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output.update(cyc)
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if node in todo:
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stack.append(node)
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todo.remove(node)
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break
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else:
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stack.pop()
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return output
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def _gen_edges(edges: DefaultDict[_T, Set[_T]]) -> Set[Tuple[_T, _T]]:
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return {(right, left) for left in edges for right in edges[left]}
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