Интегрирован wesp в сайт

2026-07-17 12:57:18 +03:00
parent 5dfa06ddbe
commit 355c0ef9f1
883 changed files with 194576 additions and 177 deletions
@@ -0,0 +1,216 @@
+"""Native derive формул zootech «База сырья» — WESP GfE 2001 engine."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from app.lab.calc.gfe_policies import (
+    DCAB_CL,
+    DCAB_K,
+    DCAB_NA,
+    DCAB_S,
+    DEFAULT_CP_DIGEST_PCT,
+    DEFAULT_FAT_DIGEST_PCT,
+    DEFAULT_FIBER_DIGEST_PCT,
+    DEFAULT_INSOLUBLE_PROTEIN_PCT,
+    DEFAULT_NFE_DIGEST_PCT,
+    DEFAULT_PROTEIN_FRACTION_PCT,
+    DeriveContext,
+    GE_CP,
+    GE_FAT,
+    GE_FIBER,
+    GE_NFE,
+    ME_CP,
+    ME_FAT,
+    ME_FIBER,
+    ME_OR_RESIDUE,
+    NEL_BASE,
+    NEL_Q_COEFF,
+    NEL_Q_REF_PCT,
+    USP_FAT_THRESHOLD_G_PER_KG_DM,
+    default_om_digestibility_pct,
+)
+from app.lab.calc.ingredient_catalog import (
+    DISPLAY_SYNC,
+    HEADER_TO_LETTER,
+    INGREDIENT_HEADERS,
+    LETTER_TO_HEADER,
+)
+
+
+def _parse_num(value: Any) -> float | None:
+    if value is None or value == "":
+        return None
+    try:
+        n = float(value)
+    except (TypeError, ValueError):
+        return None
+    return None if n != n else n
+
+
+def _normalize_key(value: str) -> str:
+    return " ".join((value or "").split()).strip().lower()
+
+
+def _v(cells: dict[str, float], letter: str, default: float = 0.0) -> float:
+    return cells.get(letter, default)
+
+
+def _if_pos(test: float, when_true, when_false: float = 0.0) -> float:
+    """Excel IF(test>0, …) — ветка when_true не вычисляется при test<=0."""
+    if test > 0:
+        return when_true() if callable(when_true) else when_true
+    return when_false
+
+
+def dict_to_cells(data: dict[str, Any] | None) -> dict[str, float]:
+    """Словарь {заголовок: значение} → {буква колонки: значение}."""
+    cells: dict[str, float] = {}
+    if not data:
+        return cells
+    norm_index = {_normalize_key(h): h for h in INGREDIENT_HEADERS}
+    for raw_key, raw_val in data.items():
+        n = _parse_num(raw_val)
+        if n is None:
+            continue
+        nk = _normalize_key(str(raw_key))
+        header = norm_index.get(nk)
+        if header is None:
+            continue
+        letter = HEADER_TO_LETTER.get(header)
+        if letter:
+            cells[letter] = n
+    return cells
+
+
+def cells_to_dict(cells: dict[str, float]) -> dict[str, float]:
+    out: dict[str, float] = {}
+    for letter, value in cells.items():
+        header = LETTER_TO_HEADER.get(letter)
+        if header and header not in ("№", "Наименование", "Цена 1 кг"):
+            out[header] = value
+    return out
+
+
+def derive_cells(
+    cells: dict[str, float],
+    *,
+    context: DeriveContext | None = None,
+) -> dict[str, float]:
+    """Пересчёт derived-колонок по цепочке формул row 6 «База сырья»."""
+    c = dict(cells)
+    ctx = context or DeriveContext.infer_from_cells(c)
+    omd_default = default_om_digestibility_pct(ctx)
+
+    c["AE"] = DCAB_NA * _v(c, "AA") + DCAB_K * _v(c, "AB") - DCAB_CL * _v(c, "AC") - DCAB_S * _v(c, "AD")
+    c["AG"] = _v(c, "AJ") * _v(c, "AI") / 100.0
+    c["AF"] = _v(c, "AI") - c["AG"] + _v(c, "AH")
+    c["AX"] = _v(c, "D") - _v(c, "F") - _v(c, "K") - _v(c, "M") - _v(c, "AW")
+    c["BN"] = _if_pos(_v(c, "E"), lambda: _v(c, "D") / _v(c, "E") * 1000.0)
+    c["BP"] = _if_pos(
+        _v(c, "BO"),
+        lambda: (_v(c, "D") - _v(c, "AW")) * _v(c, "BO") / 100.0,
+        (_v(c, "D") - _v(c, "AW")) * omd_default / 100.0,
+    )
+    c["BR"] = _if_pos(
+        _v(c, "BQ"), lambda: _v(c, "F") * _v(c, "BQ") / 100.0, _v(c, "F") * DEFAULT_CP_DIGEST_PCT / 100.0
+    )
+    c["BT"] = _if_pos(
+        _v(c, "BS"), lambda: _v(c, "M") * _v(c, "BS") / 100.0, _v(c, "M") * DEFAULT_FAT_DIGEST_PCT / 100.0
+    )
+    c["BV"] = _if_pos(
+        _v(c, "BU"), lambda: _v(c, "K") * _v(c, "BU") / 100.0, _v(c, "K") * DEFAULT_FIBER_DIGEST_PCT / 100.0
+    )
+    c["BX"] = _if_pos(
+        _v(c, "BW"), lambda: c["AX"] * _v(c, "BW") / 100.0, c["AX"] * DEFAULT_NFE_DIGEST_PCT / 100.0
+    )
+    c["BY"] = GE_CP * _v(c, "F") + GE_FAT * _v(c, "M") + GE_FIBER * _v(c, "K") + GE_NFE * c["AX"]
+    c["BZ"] = (
+        ME_FAT * c["BT"]
+        + ME_FIBER * c["BV"]
+        + ME_OR_RESIDUE * (c["BP"] - c["BT"] - c["BV"])
+        + ME_CP * _v(c, "F")
+    )
+    c["CA"] = _if_pos(
+        c["BY"],
+        lambda: (NEL_BASE * (1.0 + NEL_Q_COEFF * (c["BZ"] / c["BY"] * 100.0 - NEL_Q_REF_PCT)) * c["BZ"]),
+    )
+    c["CF"] = _if_pos(
+        _v(c, "CE"),
+        lambda: _v(c, "F") * _v(c, "CE") / 100.0,
+        _v(c, "F") * DEFAULT_INSOLUBLE_PROTEIN_PCT / 100.0,
+    )
+    c["CG"] = _if_pos(_v(c, "D"), lambda: _v(c, "M") * 1000.0 / _v(c, "D"))
+    c["CH"] = _if_pos(_v(c, "D"), lambda: c["CF"] * 1000.0 / _v(c, "D"))
+    c["CI"] = _if_pos(_v(c, "D"), lambda: _v(c, "F") * 1000.0 / _v(c, "D"))
+    c["CJ"] = _if_pos(_v(c, "D"), lambda: c["BP"] / _v(c, "D"))
+    c["CK"] = _if_pos(_v(c, "D"), lambda: c["BT"] / _v(c, "D"))
+    c["CL"] = _if_pos(c["CI"], lambda: (187.7 - 115.4 * c["CH"] / c["CI"]) * c["CJ"] + 1.03 * c["CH"])
+    c["CM"] = _if_pos(c["CI"], lambda: (196.1 - 127.5 * c["CH"] / c["CI"]) * (c["CJ"] - c["CK"]) + 1.03 * c["CH"])
+    co = _v(c, "CO")
+    if c["CG"] < USP_FAT_THRESHOLD_G_PER_KG_DM + 0.01:
+        c["CN"] = c["CL"]
+    elif c["CG"] > USP_FAT_THRESHOLD_G_PER_KG_DM:
+        c["CN"] = c["CM"]
+    else:
+        c["CN"] = 0.0
+    if co < 1.01:
+        c["CP"] = c["CN"] * _v(c, "D") / 1000.0
+    elif co > 1.0:
+        c["CP"] = co
+    else:
+        c["CP"] = 0.0
+    c["CQ"] = (_v(c, "F") - c["CP"]) / 6.25
+    c["CX"] = _if_pos(
+        _v(c, "CT"),
+        lambda: _v(c, "F") * _v(c, "CT") / 100.0,
+        _v(c, "F") * DEFAULT_PROTEIN_FRACTION_PCT / 100.0,
+    )
+    nel = c["CA"]
+    c["CY"] = nel * _v(c, "CW")
+    f_val = _v(c, "F")
+    bo = _v(c, "BO")
+    if f_val == 0.0:
+        c["CZ"] = 0.0
+        c["DA"] = 0.0
+        c["DB"] = 0.0
+        c["DC"] = 0.0
+        c["DD"] = 0.0
+        c["DE"] = 0.0
+    else:
+        c["DA"] = c["CX"] * (_v(c, "AY") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.071 * 0.8
+        c["CZ"] = _if_pos(
+            bo,
+            lambda: c["CX"] * (_v(c, "AZ") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.018 * 0.8,
+        )
+        c["DB"] = _if_pos(
+            bo,
+            lambda: c["CX"] * (_v(c, "BA") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.044 * 0.8,
+        )
+        c["DC"] = _if_pos(
+            _v(c, "BD"),
+            lambda: c["CX"] * (_v(c, "BD") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.063 * 0.8,
+        )
+        c["DD"] = _if_pos(
+            _v(c, "BC"),
+            lambda: c["CX"] * (_v(c, "BC") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.049 * 0.8,
+        )
+        c["DE"] = _if_pos(
+            _v(c, "BE"),
+            lambda: c["CX"] * (_v(c, "BE") / 10.0) / (f_val / 10.0) * (bo / 100.0) + c["CY"] * 0.048 * 0.8,
+        )
+
+    for display, source in DISPLAY_SYNC:
+        c[display] = c[source]
+
+    return c
+
+
+def derive_ingredient_nutrients(
+    data: dict[str, Any] | None,
+    *,
+    context: DeriveContext | None = None,
+) -> dict[str, float]:
+    """Полный набор показателей: входные + пересчитанные derived."""
+    cells = dict_to_cells(data)
+    return cells_to_dict(derive_cells(cells, context=context))