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from __future__ import annotations
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import uuid
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from typing import Any
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from app import db
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from app.lab.calc.feed_groups import classify_feed_group
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from app.lab.calc.gfe_policies import DeriveContext
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from app.lab.calc.ingredient_catalog import DERIVED_HEADERS
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from app.lab.calc.ingredient_derive import derive_ingredient_nutrients
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from app.lab.models import LabComponentNutrientValue
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from app.lab.nutrient_keys import augment_with_indicator_keys, canonicalize_for_storage
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from app.lab.nutrient_schema import (
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dry_matter_g_per_kg,
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mapping_to_calc_dict,
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read_from_mapping,
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)
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from app.models.component import Component
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_DERIVE_INPUT_KEYS = ("СВ", "Сыр. Протеин", "Сырая клетч", "Сырой жир")
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_EAV_SKIP_KEYS = frozenset({"СВ"})
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_MAIN_FEED_KEYS = ("Осн.Корм", "СВ Основной корм")
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def derive_context_for_component(
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component_id: str | None,
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merged: dict[str, Any] | None = None,
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) -> DeriveContext:
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"""Контекст derive: тип корма + признак основного корма."""
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main_feed = read_from_mapping(merged or {}, _MAIN_FEED_KEYS)
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is_main = main_feed is not None and float(main_feed) > 0
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feed_group: str = "unknown"
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if component_id:
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comp = Component.query.filter_by(id=component_id, is_deleted=False).first()
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if comp is not None:
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feed_group = classify_feed_group(comp)
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return DeriveContext(feed_group=feed_group, is_main_feed=is_main) # type: ignore[arg-type]
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def _component_dry_matter_pct(component_id: str | None) -> float | None:
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if not component_id:
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return None
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comp = Component.query.get(component_id)
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return comp.dry_matter if comp else None
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def _inject_sv_for_derive(merged: dict[str, Any], dry_matter_pct: float | None) -> dict[str, Any]:
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out = dict(merged)
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sv = dry_matter_g_per_kg(dry_matter_pct)
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if sv is not None:
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out["СВ"] = sv
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return out
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def _should_derive(merged: dict[str, Any]) -> bool:
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"""Derive при полном базовом вводе (СВ из component.dry_matter + 3 показателя)."""
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return all(read_from_mapping(merged, (k,)) is not None for k in _DERIVE_INPUT_KEYS)
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def nutrients_full_dict(component_id: str | None) -> dict[str, float]:
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if not component_id:
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return {}
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rows = LabComponentNutrientValue.query.filter_by(component_id=component_id).all()
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return {
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row.nutrient_key: float(row.value)
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for row in rows
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if row.value is not None and row.nutrient_key not in _EAV_SKIP_KEYS
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}
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def nutrients_api_dict(component_id: str | None) -> dict[str, float]:
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return nutrients_full_dict(component_id)
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def _fill_missing_derived(
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merged: dict[str, float],
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*,
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component_id: str | None,
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) -> dict[str, float]:
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"""Добавляет derived-поля (пОВ, переваримые фракции), не перезаписывая введённые лаб. значения."""
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if not _should_derive(merged):
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return merged
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ctx = derive_context_for_component(component_id, merged)
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derived = derive_ingredient_nutrients(merged, context=ctx)
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out = dict(merged)
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for header in DERIVED_HEADERS:
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if read_from_mapping(out, (header,)) is not None:
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continue
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val = read_from_mapping(derived, (header,))
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if val is not None:
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out[header] = float(val)
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return out
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def _repair_for_ration_calc(
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full: dict[str, float],
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dry_matter_pct: float | None,
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component_id: str | None = None,
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) -> dict[str, float]:
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merged = _inject_sv_for_derive(full, dry_matter_pct)
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oe = read_from_mapping(merged, ("ОЭ-КРС", " ОЭ-КРС"))
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nel = read_from_mapping(merged, ("ЧЭЛ- КРС", " ЧЭЛ- КРС"))
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energy_bad = (oe is not None and oe < 0) or (nel is not None and nel < 0)
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if energy_bad:
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minimal = {key: read_from_mapping(merged, (key,)) for key in _DERIVE_INPUT_KEYS}
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if all(v is not None for v in minimal.values()):
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ctx = derive_context_for_component(component_id, merged)
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merged.update(derive_ingredient_nutrients(minimal, context=ctx))
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else:
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merged = _fill_missing_derived(merged, component_id=component_id)
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return merged
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def _calc_dict_from_full(
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full: dict[str, float],
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dry_matter_pct: float | None,
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component_id: str | None,
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) -> dict[str, float]:
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repaired = _repair_for_ration_calc(full, dry_matter_pct, component_id)
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return mapping_to_calc_dict(augment_with_indicator_keys(repaired))
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def nutrients_calc_dict(component_id: str | None) -> dict[str, float]:
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full = nutrients_full_dict(component_id)
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dry_matter_pct = _component_dry_matter_pct(component_id)
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return _calc_dict_from_full(full, dry_matter_pct, component_id)
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def nutrients_calc_dict_batch(component_ids: list[str]) -> dict[str, dict[str, float]]:
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"""Batch-load EAV + dry_matter for formulate (one SQL round-trip per table)."""
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unique = list(dict.fromkeys(cid for cid in component_ids if cid))
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if not unique:
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return {}
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eav_by_id: dict[str, dict[str, float]] = {cid: {} for cid in unique}
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rows = LabComponentNutrientValue.query.filter(
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LabComponentNutrientValue.component_id.in_(unique),
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).all()
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for row in rows:
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if row.value is None or row.nutrient_key in _EAV_SKIP_KEYS:
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continue
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eav_by_id.setdefault(row.component_id, {})[row.nutrient_key] = float(row.value)
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dm_by_id: dict[str, float | None] = {cid: None for cid in unique}
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for comp in Component.query.filter(
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Component.id.in_(unique),
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Component.is_deleted.is_(False),
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).all():
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dm_by_id[comp.id] = comp.dry_matter
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return {
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cid: _calc_dict_from_full(eav_by_id.get(cid, {}), dm_by_id.get(cid), cid)
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for cid in unique
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}
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def nutrients_is_empty(component_id: str | None) -> bool:
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if not component_id:
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return True
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return (
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LabComponentNutrientValue.query.filter(
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LabComponentNutrientValue.component_id == component_id,
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LabComponentNutrientValue.nutrient_key.notin_(_EAV_SKIP_KEYS),
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).first()
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is None
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)
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def _upsert_eav(component_id: str, nutrients: dict[str, float]) -> None:
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existing = {
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row.nutrient_key: row
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for row in LabComponentNutrientValue.query.filter_by(component_id=component_id).all()
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}
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for key in list(existing):
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if key in _EAV_SKIP_KEYS:
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db.session.delete(existing[key])
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existing = {k: v for k, v in existing.items() if k not in _EAV_SKIP_KEYS}
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for key, value in nutrients.items():
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if value is None or key in _EAV_SKIP_KEYS:
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continue
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row = existing.get(key)
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if row is None:
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row = LabComponentNutrientValue(
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id=str(uuid.uuid4()),
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component_id=component_id,
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nutrient_key=key,
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value=float(value),
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)
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db.session.add(row)
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existing[key] = row
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else:
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row.value = float(value)
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def _prepare_payload(
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component_id: str,
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nutrients: dict[str, Any] | None,
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*,
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pin: bool = False,
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) -> dict[str, float]:
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dry_matter_pct = _component_dry_matter_pct(component_id)
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merged = _inject_sv_for_derive(
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{**nutrients_full_dict(component_id), **(nutrients or {})},
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dry_matter_pct,
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)
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if pin:
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payload = merged
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elif _should_derive(merged):
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ctx = derive_context_for_component(component_id, merged)
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derived = derive_ingredient_nutrients(merged, context=ctx)
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payload = {**merged, **derived}
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else:
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payload = merged
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stored = canonicalize_for_storage(payload)
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return {k: v for k, v in stored.items() if k not in _EAV_SKIP_KEYS}
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def save_component_nutrients(
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component_id: str,
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nutrients: dict[str, Any] | None,
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*,
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user_id: str = "system",
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pin: bool = False,
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) -> dict[str, Any]:
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"""Единственная точка записи EAV + derive. Возвращает stored dict и affectedRecipeIds."""
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stored = _prepare_payload(component_id, nutrients, pin=pin)
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_upsert_eav(component_id, stored)
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from app.lab.services.ration_recalc import find_rations_by_component
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affected = find_rations_by_component(component_id)
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return {"stored": stored, "affectedRecipeIds": affected, "userId": user_id}
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# Backward-compatible aliases for tests and gradual migration
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def upsert_from_api_dict(component_id: str, nutrients: dict[str, Any] | None) -> dict[str, float]:
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result = save_component_nutrients(component_id, nutrients)
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return result["stored"]
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def pin_nutrient_values(component_id: str, values: dict[str, float]) -> None:
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save_component_nutrients(component_id, values, pin=True)
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def upsert_from_column_values(component_id: str, values: dict[str, float | None]) -> dict[str, float]:
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from app.lab.nutrient_schema import column_values_to_mapping
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merged = nutrients_full_dict(component_id)
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merged.update(column_values_to_mapping(values))
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return upsert_from_api_dict(component_id, merged)
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