"""Deterministic initial nutrition calculations.""" from __future__ import annotations from datetime import date, timedelta from decimal import Decimal, ROUND_CEILING, ROUND_HALF_UP from pydantic import Field, field_serializer, model_validator from checkin_cli.nutrition_onboarding_models import ( ActivityCategory, EquationSexBasis, GoalType, NutritionOnboardingBaseline, StrictFrozenModel, _canonical_decimal, ) class CalculationDeclinedError(ValueError): """Raised when the customer declines the selected calculation basis.""" class UnsafeGoalTrajectoryError(ValueError): """Raised when the requested weekly trajectory exceeds safety bounds.""" _ACTIVITY_FACTORS = { ActivityCategory.SEDENTARY: Decimal("1.200"), ActivityCategory.LIGHT: Decimal("1.375"), ActivityCategory.MODERATE: Decimal("1.550"), ActivityCategory.VERY_ACTIVE: Decimal("1.725"), ActivityCategory.EXTRA_ACTIVE: Decimal("1.900"), } def activity_factor(category: ActivityCategory) -> Decimal: return _ACTIVITY_FACTORS[category] def mifflin_st_jeor_bmr( *, weight_kg: Decimal, height_cm: Decimal, adult_age: int, equation_sex_basis: EquationSexBasis, ) -> Decimal | None: if equation_sex_basis is EquationSexBasis.DECLINE: return None constant = Decimal("5") if equation_sex_basis is EquationSexBasis.MALE else Decimal("-161") return ( Decimal("10") * weight_kg + Decimal("6.25") * height_cm - Decimal("5") * adult_age + constant ).quantize(Decimal("1"), rounding=ROUND_HALF_UP) class WeeklyNutritionTarget(StrictFrozenModel): week: int = Field(ge=1, le=12) calories_kcal: int = Field(ge=1500, le=4500) protein_g: int = Field(ge=120, le=250) carbohydrate_g: int = Field(ge=0) fat_g: int = Field(ge=40, le=150) @model_validator(mode="after") def validate_energy(self) -> "WeeklyNutritionTarget": calculated = 4 * self.protein_g + 4 * self.carbohydrate_g + 9 * self.fat_g if calculated != self.calories_kcal: raise ValueError("macronutrients do not reconcile with calories") return self class InitialNutritionPlan(StrictFrozenModel): schema_version: str = "1.0" method_id: str method_version: str energy_density_method: str = "7700_kcal_per_kg_v1" bmr_kcal: Decimal tdee_kcal: Decimal starts_on: date requested_trajectory_within_guardrail: bool recommended_target_date: date | None weeks: tuple[WeeklyNutritionTarget, ...] projected_weights_kg: tuple[Decimal, ...] projection_disclaimer: str @model_validator(mode="after") def validate_weeks(self) -> "InitialNutritionPlan": if len(self.weeks) != 12: raise ValueError("plan requires exactly 12 weeks") if tuple(row.week for row in self.weeks) != tuple(range(1, 13)): raise ValueError("week numbers must be 1 through 12") if len(self.projected_weights_kg) != 13: raise ValueError("projection requires 13 boundary weights") return self @field_serializer("bmr_kcal", "tdee_kcal") def serialize_decimal(self, value: Decimal) -> str: return _canonical_decimal(value) @field_serializer("projected_weights_kg") def serialize_weights(self, value: tuple[Decimal, ...]) -> list[str]: return [_canonical_decimal(item) for item in value] def _round_to_twenty(value: Decimal) -> int: return int((value / Decimal("20")).quantize(Decimal("1"), rounding=ROUND_HALF_UP) * 20) def _safe_weekly_change( baseline: NutritionOnboardingBaseline, starts_on: date, ) -> tuple[Decimal, bool, date | None]: if baseline.goal_type is GoalType.MAINTAIN: return Decimal("0"), True, None assert baseline.target_weight_kg is not None assert baseline.target_date is not None days = (baseline.target_date - starts_on).days if days < 1: raise UnsafeGoalTrajectoryError("unsafe goal trajectory") weeks = Decimal(days) / Decimal("7") weekly = (baseline.target_weight_kg - baseline.weight_kg) / weeks rate = abs(weekly / baseline.weight_kg) limit = Decimal("0.01") if baseline.goal_type is GoalType.LOSS else Decimal("0.005") direction_invalid = ( baseline.goal_type is GoalType.LOSS and weekly >= 0 ) or ( baseline.goal_type is GoalType.GAIN and weekly <= 0 ) if direction_invalid: raise UnsafeGoalTrajectoryError("unsafe goal trajectory") if rate <= limit: return weekly, True, None direction = ( Decimal("-1") if baseline.goal_type is GoalType.LOSS else Decimal("1") ) safe_days = ( abs(baseline.target_weight_kg - baseline.weight_kg) / (baseline.weight_kg * limit) * Decimal("7") ).to_integral_value(rounding=ROUND_CEILING) return ( direction * baseline.weight_kg * limit, False, starts_on + timedelta(days=int(safe_days)), ) def _macros(calories: int, weight_kg: Decimal) -> tuple[int, int, int]: protein = min(250, max(120, int((weight_kg * 2).quantize(Decimal("1"), rounding=ROUND_HALF_UP)))) fat = min(150, max(40, int(weight_kg.quantize(Decimal("1"), rounding=ROUND_HALF_UP)))) while fat >= 40: remainder = calories - 4 * protein - 9 * fat if remainder >= 0 and remainder % 4 == 0: return protein, remainder // 4, fat fat -= 1 raise ValueError("calorie target cannot be reconciled within macro bounds") def generate_initial_plan( baseline: NutritionOnboardingBaseline, *, starts_on: date, ) -> InitialNutritionPlan: bmr = mifflin_st_jeor_bmr( weight_kg=baseline.weight_kg, height_cm=baseline.height_cm, adult_age=baseline.adult_age, equation_sex_basis=baseline.equation_sex_basis, ) if bmr is None: raise CalculationDeclinedError("equation sex basis was declined") tdee = (bmr * activity_factor(baseline.activity_category)).quantize( Decimal("1"), rounding=ROUND_HALF_UP, ) ( weekly_change, requested_trajectory_within_guardrail, recommended_target_date, ) = _safe_weekly_change(baseline, starts_on) projections = tuple( (baseline.weight_kg + weekly_change * week).quantize(Decimal("0.01"), rounding=ROUND_HALF_UP) for week in range(13) ) rows: list[WeeklyNutritionTarget] = [] for week in range(1, 13): projected_weight = projections[week - 1] projected_bmr = bmr + Decimal("10") * (projected_weight - baseline.weight_kg) projected_tdee = projected_bmr * activity_factor(baseline.activity_category) energy_delta = weekly_change * Decimal("7700") / Decimal("7") calories = _round_to_twenty(projected_tdee + energy_delta) if not 1500 <= calories <= 4500: raise UnsafeGoalTrajectoryError("unsafe goal trajectory") protein, carbs, fat = _macros(calories, projected_weight) rows.append( WeeklyNutritionTarget( week=week, calories_kcal=calories, protein_g=protein, carbohydrate_g=carbs, fat_g=fat, ) ) return InitialNutritionPlan( method_id="mifflin_st_jeor_1990", method_version="1.0", bmr_kcal=bmr, tdee_kcal=tdee, starts_on=starts_on, requested_trajectory_within_guardrail=( requested_trajectory_within_guardrail ), recommended_target_date=recommended_target_date, weeks=tuple(rows), projected_weights_kg=projections, projection_disclaimer=( "This deterministic 12-week projection is not a medical diagnosis " "or a guarantee of body-weight change." ), )