Sadmin
f20080305d
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).
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
import json
|
|
from functools import lru_cache
|
|
from pathlib import Path
|
|
from typing import TYPE_CHECKING, Any, Callable, Optional, Type, TypeVar, Union
|
|
|
|
from pydantic.v1.parse import Protocol, load_file, load_str_bytes
|
|
from pydantic.v1.types import StrBytes
|
|
from pydantic.v1.typing import display_as_type
|
|
|
|
__all__ = ('parse_file_as', 'parse_obj_as', 'parse_raw_as', 'schema_of', 'schema_json_of')
|
|
|
|
NameFactory = Union[str, Callable[[Type[Any]], str]]
|
|
|
|
if TYPE_CHECKING:
|
|
from pydantic.v1.typing import DictStrAny
|
|
|
|
|
|
def _generate_parsing_type_name(type_: Any) -> str:
|
|
return f'ParsingModel[{display_as_type(type_)}]'
|
|
|
|
|
|
@lru_cache(maxsize=2048)
|
|
def _get_parsing_type(type_: Any, *, type_name: Optional[NameFactory] = None) -> Any:
|
|
from pydantic.v1.main import create_model
|
|
|
|
if type_name is None:
|
|
type_name = _generate_parsing_type_name
|
|
if not isinstance(type_name, str):
|
|
type_name = type_name(type_)
|
|
return create_model(type_name, __root__=(type_, ...))
|
|
|
|
|
|
T = TypeVar('T')
|
|
|
|
|
|
def parse_obj_as(type_: Type[T], obj: Any, *, type_name: Optional[NameFactory] = None) -> T:
|
|
model_type = _get_parsing_type(type_, type_name=type_name) # type: ignore[arg-type]
|
|
return model_type(__root__=obj).__root__
|
|
|
|
|
|
def parse_file_as(
|
|
type_: Type[T],
|
|
path: Union[str, Path],
|
|
*,
|
|
content_type: str = None,
|
|
encoding: str = 'utf8',
|
|
proto: Protocol = None,
|
|
allow_pickle: bool = False,
|
|
json_loads: Callable[[str], Any] = json.loads,
|
|
type_name: Optional[NameFactory] = None,
|
|
) -> T:
|
|
obj = load_file(
|
|
path,
|
|
proto=proto,
|
|
content_type=content_type,
|
|
encoding=encoding,
|
|
allow_pickle=allow_pickle,
|
|
json_loads=json_loads,
|
|
)
|
|
return parse_obj_as(type_, obj, type_name=type_name)
|
|
|
|
|
|
def parse_raw_as(
|
|
type_: Type[T],
|
|
b: StrBytes,
|
|
*,
|
|
content_type: str = None,
|
|
encoding: str = 'utf8',
|
|
proto: Protocol = None,
|
|
allow_pickle: bool = False,
|
|
json_loads: Callable[[str], Any] = json.loads,
|
|
type_name: Optional[NameFactory] = None,
|
|
) -> T:
|
|
obj = load_str_bytes(
|
|
b,
|
|
proto=proto,
|
|
content_type=content_type,
|
|
encoding=encoding,
|
|
allow_pickle=allow_pickle,
|
|
json_loads=json_loads,
|
|
)
|
|
return parse_obj_as(type_, obj, type_name=type_name)
|
|
|
|
|
|
def schema_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_kwargs: Any) -> 'DictStrAny':
|
|
"""Generate a JSON schema (as dict) for the passed model or dynamically generated one"""
|
|
return _get_parsing_type(type_, type_name=title).schema(**schema_kwargs)
|
|
|
|
|
|
def schema_json_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_json_kwargs: Any) -> str:
|
|
"""Generate a JSON schema (as JSON) for the passed model or dynamically generated one"""
|
|
return _get_parsing_type(type_, type_name=title).schema_json(**schema_json_kwargs)
|