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b00f6ac512
Registers 5 protein-related placeholders with complete metadata: - protein_ziel_low: Lower protein target (1.6 g/kg × latest weight) - protein_ziel_high: Upper protein target (2.2 g/kg × latest weight) - protein_g_per_kg: Protein intake per kg body weight - protein_days_in_target: Days in protein range (format: 5/7) - protein_adequacy_28d: Protein adequacy score (0-100) All placeholders with evidence-based tagging: - 22 metadata fields per placeholder - CODE_DERIVED: Technical fields from source inspection - DRAFT_DERIVED: Semantic fields from canonical requirements - UNRESOLVED: Fields requiring clarification - TO_VERIFY: Assumptions needing verification Critical issues documented in known_limitations: - protein_g_per_kg: Weight basis inconsistency (protein 7d avg / weight latest) - protein_adequacy_28d: Score logic explicitly documented (1.4-1.6-2.2 thresholds) Registry now contains 9 placeholders total (4 Part A + 5 Part B). Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02) Change Plan: .claude/task/rework_0b_placeholder/NUTRITION_PART_B_CHANGE_PLAN.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
430 lines
21 KiB
Python
430 lines
21 KiB
Python
"""
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Nutrition Part B Placeholder Registrations
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Registers the 5 protein-specific metrics in the central placeholder registry:
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- protein_ziel_low
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- protein_ziel_high
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- protein_g_per_kg
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- protein_days_in_target
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- protein_adequacy_28d
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Evidence-based metadata with clear tagging of source.
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Includes documentation of open points (weight basis inconsistency, score logic).
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"""
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from placeholder_registry import (
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PlaceholderMetadata,
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MissingValuePolicy,
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EvidenceType,
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OutputType,
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PlaceholderType,
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register_placeholder
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)
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def register_nutrition_part_b():
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"""
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Register Part B protein placeholders.
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Metadata sources:
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- code-derived: extracted from actual code
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- draft-derived: from canonical requirements draft
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- mixed: combination of code and draft
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- unresolved: not explicitly documented
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- to_verify: claimed but not verified
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"""
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# ── protein_ziel_low ──────────────────────────────────────────────────────
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low_metadata = PlaceholderMetadata(
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key="protein_ziel_low",
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category="Ernährung",
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description="Unteres Proteinziel (1.6 g/kg)",
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# Technical
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="get_protein_ziel_low",
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data_layer_module="backend/data_layer/nutrition_metrics.py",
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data_layer_function="get_protein_targets_data",
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source_tables=["weight_log"],
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# Semantic
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semantic_contract=(
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"Liefert die untere Proteinziel-Grenze basierend auf aktuellem "
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"Körpergewicht (1.6 g/kg). Ziel für Muskelerhalt in Maintenance-Phasen."
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),
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business_meaning="Maintenance-Ziel für Muskelerhalt",
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unit="g/day",
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time_window="snapshot",
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output_type=OutputType.NUMERIC,
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placeholder_type=PlaceholderType.INTERPRETED,
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format_hint="Ganzzahl",
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example_output="128",
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# Quality
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confidence_logic="Binary: weight vorhanden/nicht vorhanden",
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missing_value_policy=MissingValuePolicy(
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available=False,
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value_raw=None,
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missing_reason="no_data",
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legacy_display="nicht verfügbar"
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),
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known_limitations=(
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"Basiert auf single-point weight (latest entry); "
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"anfällig für Gewichts-Outlier (z.B. nach Refeed-Tag)"
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),
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# Architecture
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layer_1_decision="Data Layer (nutrition_metrics.get_protein_targets_data)",
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layer_2a_decision="Placeholder Resolver (formatting only)",
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layer_2b_reuse_possible=None, # to_verify
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architecture_alignment="Phase 0c Multi-Layer Architecture conform",
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issue_53_alignment="Layer separation established"
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)
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# Evidence
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low_metadata.set_evidence("key", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("category", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("description", EvidenceType.MIXED)
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low_metadata.set_evidence("resolver_module", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("resolver_function", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("data_layer_module", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("data_layer_function", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("source_tables", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("semantic_contract", EvidenceType.DRAFT_DERIVED)
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low_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
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low_metadata.set_evidence("unit", EvidenceType.MIXED) # implicit in code, confirmed by draft
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low_metadata.set_evidence("time_window", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("output_type", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("placeholder_type", EvidenceType.MIXED)
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low_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("confidence_logic", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("missing_value_policy", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("known_limitations", EvidenceType.MIXED)
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low_metadata.set_evidence("layer_1_decision", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("layer_2a_decision", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("layer_2b_reuse_possible", EvidenceType.TO_VERIFY)
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low_metadata.set_evidence("architecture_alignment", EvidenceType.CODE_DERIVED)
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low_metadata.set_evidence("issue_53_alignment", EvidenceType.MIXED)
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register_placeholder(low_metadata)
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# ── protein_ziel_high ─────────────────────────────────────────────────────
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high_metadata = PlaceholderMetadata(
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key="protein_ziel_high",
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category="Ernährung",
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description="Oberes Proteinziel (2.2 g/kg)",
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# Technical (same as protein_ziel_low)
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="get_protein_ziel_high",
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data_layer_module="backend/data_layer/nutrition_metrics.py",
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data_layer_function="get_protein_targets_data",
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source_tables=["weight_log"],
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# Semantic
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semantic_contract=(
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"Liefert die obere Proteinziel-Grenze basierend auf aktuellem "
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"Körpergewicht (2.2 g/kg). Ziel für Muskelaufbau in hypertrophen Phasen."
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),
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business_meaning="Muskelaufbau-Ziel für hypertrophe Phasen",
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unit="g/day",
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time_window="snapshot",
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output_type=OutputType.NUMERIC,
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placeholder_type=PlaceholderType.INTERPRETED,
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format_hint="Ganzzahl",
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example_output="176",
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# Quality
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confidence_logic="Binary: weight vorhanden/nicht vorhanden",
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missing_value_policy=MissingValuePolicy(
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available=False,
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value_raw=None,
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missing_reason="no_data",
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legacy_display="nicht verfügbar"
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),
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known_limitations=(
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"Basiert auf single-point weight (latest entry); "
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"anfällig für Gewichts-Outlier"
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),
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# Architecture
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layer_1_decision="Data Layer (nutrition_metrics.get_protein_targets_data)",
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layer_2a_decision="Placeholder Resolver (formatting only)",
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layer_2b_reuse_possible=None,
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architecture_alignment="Phase 0c Multi-Layer Architecture conform",
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issue_53_alignment="Layer separation established"
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)
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# Evidence (identical to protein_ziel_low)
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high_metadata.set_evidence("key", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("category", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("description", EvidenceType.MIXED)
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high_metadata.set_evidence("resolver_module", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("resolver_function", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("data_layer_module", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("data_layer_function", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("source_tables", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("semantic_contract", EvidenceType.DRAFT_DERIVED)
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high_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
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high_metadata.set_evidence("unit", EvidenceType.MIXED)
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high_metadata.set_evidence("time_window", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("output_type", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("placeholder_type", EvidenceType.MIXED)
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high_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("confidence_logic", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("missing_value_policy", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("known_limitations", EvidenceType.MIXED)
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high_metadata.set_evidence("layer_1_decision", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("layer_2a_decision", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("layer_2b_reuse_possible", EvidenceType.TO_VERIFY)
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high_metadata.set_evidence("architecture_alignment", EvidenceType.CODE_DERIVED)
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high_metadata.set_evidence("issue_53_alignment", EvidenceType.MIXED)
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register_placeholder(high_metadata)
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# ── protein_g_per_kg ──────────────────────────────────────────────────────
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gpk_metadata = PlaceholderMetadata(
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key="protein_g_per_kg",
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category="Ernährung",
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description="Protein g/kg Körpergewicht",
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# Technical
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="_safe_float",
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data_layer_module="backend/data_layer/nutrition_metrics.py",
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data_layer_function="calculate_protein_g_per_kg",
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source_tables=["nutrition_log", "weight_log"],
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# Semantic
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semantic_contract=(
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"Liefert die durchschnittliche Proteinzufuhr relativ zum Körpergewicht. "
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"Berechnung: protein_7d_avg / latest_weight. "
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"WICHTIG: Protein ist geglättet (7d), Gewicht ist single-point."
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),
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business_meaning="Zentraler Zielindikator für Muskelerhalt und Aufbau",
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unit="g/kg/day",
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time_window="mixed", # protein 7d, weight snapshot
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output_type=OutputType.NUMERIC,
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placeholder_type=PlaceholderType.INTERPRETED,
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format_hint="Dezimalzahl (1-2 Stellen)",
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example_output="1.95",
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# Quality
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confidence_logic="Minimum von protein_confidence und weight_availability",
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missing_value_policy=MissingValuePolicy(
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available=False,
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value_raw=None,
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missing_reason="insufficient_data",
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legacy_display="nicht verfügbar"
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),
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known_limitations=(
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"KRITISCHE INKONSISTENZ: Protein ist geglättet (7d average), "
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"Gewicht ist single-point (latest). Anfällig für Gewichts-Outlier. "
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"Ein Refeed-Tag kann den Wert stark verfälschen, obwohl Protein-Intake stabil ist."
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),
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# Architecture
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layer_1_decision="Data Layer (nutrition_metrics.calculate_protein_g_per_kg)",
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layer_2a_decision="Placeholder Resolver (_safe_float wrapper)",
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layer_2b_reuse_possible=None,
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architecture_alignment="Phase 0c Multi-Layer Architecture conform",
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issue_53_alignment="Layer separation established"
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)
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# Evidence
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gpk_metadata.set_evidence("key", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("category", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("description", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("resolver_module", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("resolver_function", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("data_layer_module", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("data_layer_function", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("source_tables", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("semantic_contract", EvidenceType.MIXED) # code + explicit documentation
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gpk_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
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gpk_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("time_window", EvidenceType.CODE_DERIVED) # explicitly documented as mixed
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gpk_metadata.set_evidence("output_type", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("placeholder_type", EvidenceType.MIXED)
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gpk_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("confidence_logic", EvidenceType.UNRESOLVED) # not explicitly documented
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gpk_metadata.set_evidence("missing_value_policy", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("known_limitations", EvidenceType.CODE_DERIVED) # identified from code analysis
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gpk_metadata.set_evidence("layer_1_decision", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("layer_2a_decision", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("layer_2b_reuse_possible", EvidenceType.TO_VERIFY)
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gpk_metadata.set_evidence("architecture_alignment", EvidenceType.CODE_DERIVED)
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gpk_metadata.set_evidence("issue_53_alignment", EvidenceType.MIXED)
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register_placeholder(gpk_metadata)
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# ── protein_days_in_target ────────────────────────────────────────────────
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days_metadata = PlaceholderMetadata(
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key="protein_days_in_target",
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category="Ernährung",
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description="Tage im Protein-Zielbereich (7d)",
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# Technical
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="_safe_str",
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data_layer_module="backend/data_layer/nutrition_metrics.py",
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data_layer_function="calculate_protein_days_in_target",
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source_tables=["nutrition_log", "weight_log"],
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# Semantic
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semantic_contract=(
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"Liefert Anzahl Tage im Protein-Zielbereich relativ zu Gesamttagen. "
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"Target-Range: 1.6-2.2 g/kg (hardcoded). "
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"Format: 'X/Y' (z.B. '5/7' = 5 von 7 Tagen im Ziel)."
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),
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business_meaning="Adhärenz-Indikator für Proteinversorgung",
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unit="days_ratio",
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time_window="7d",
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output_type=OutputType.STRING,
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placeholder_type=PlaceholderType.INTERPRETED,
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format_hint="String format 'X/Y' (e.g. '5/7')",
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example_output="5/7",
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# Quality
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confidence_logic="Abhängig von nutrition_log Datenabdeckung",
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missing_value_policy=MissingValuePolicy(
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available=False,
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value_raw=None,
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missing_reason="no_data",
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legacy_display="nicht verfügbar"
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),
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known_limitations=(
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"Target-Range 1.6-2.2 g/kg fest kodiert (default parameters), "
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"nicht konfigurierbar. Keine Integration mit Goal-System."
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),
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# Architecture
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layer_1_decision="Data Layer (nutrition_metrics.calculate_protein_days_in_target)",
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layer_2a_decision="Placeholder Resolver (_safe_str wrapper)",
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layer_2b_reuse_possible=None,
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architecture_alignment="Phase 0c Multi-Layer Architecture conform",
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issue_53_alignment="Layer separation established"
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)
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# Evidence
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days_metadata.set_evidence("key", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("category", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("description", EvidenceType.MIXED)
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days_metadata.set_evidence("resolver_module", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("resolver_function", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("data_layer_module", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("data_layer_function", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("source_tables", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("semantic_contract", EvidenceType.MIXED)
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days_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
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days_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("time_window", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("output_type", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("placeholder_type", EvidenceType.MIXED)
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days_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("confidence_logic", EvidenceType.UNRESOLVED)
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days_metadata.set_evidence("missing_value_policy", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("known_limitations", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("layer_1_decision", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("layer_2a_decision", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("layer_2b_reuse_possible", EvidenceType.TO_VERIFY)
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days_metadata.set_evidence("architecture_alignment", EvidenceType.CODE_DERIVED)
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days_metadata.set_evidence("issue_53_alignment", EvidenceType.MIXED)
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register_placeholder(days_metadata)
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# ── protein_adequacy_28d ──────────────────────────────────────────────────
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adequacy_metadata = PlaceholderMetadata(
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key="protein_adequacy_28d",
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category="Ernährung",
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description="Protein Adequacy Score (0-100)",
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# Technical
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="_safe_int",
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data_layer_module="backend/data_layer/nutrition_metrics.py",
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data_layer_function="calculate_protein_adequacy_28d",
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source_tables=["nutrition_log", "weight_log"],
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# Semantic
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semantic_contract=(
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"Liefert standardisierten Angemessenheitswert der Proteinversorgung "
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"über 28 Tage relativ zu definierten Protein-Zielbereichen (1.6-2.2 g/kg). "
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"Score-Logik: "
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"- Days in target [1.6-2.2]: 100 points; "
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"- Days slightly below [1.4-1.6]: partial points (linear interpolation); "
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"- Days far below (<1.4): 0 points; "
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"- Days above (>2.2): 100 points (no penalty). "
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"Final score: average over 28d."
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),
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business_meaning="Verdichteter Zielerreichungsindikator für Proteinversorgung",
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unit="score",
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time_window="28d",
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output_type=OutputType.NUMERIC,
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placeholder_type=PlaceholderType.SCORE,
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format_hint="Integer 0-100, höher = besser",
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example_output="82",
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# Quality
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confidence_logic="Abgeleitet aus Datenabdeckung über 28d",
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missing_value_policy=MissingValuePolicy(
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available=False,
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value_raw=None,
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missing_reason="insufficient_data",
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legacy_display="nicht verfügbar"
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),
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known_limitations=(
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"Score muss transparent erklärt werden; ohne Skalen-Dokumentation "
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"interpretationsanfällig. Scoring-Schwellen [1.4, 1.6, 2.2] nicht explizit "
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"im Code dokumentiert, nur in Logik implementiert."
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),
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# Architecture
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layer_1_decision="Data Layer (nutrition_metrics.calculate_protein_adequacy_28d)",
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layer_2a_decision="Placeholder Resolver (_safe_int wrapper)",
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layer_2b_reuse_possible=None,
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architecture_alignment="Phase 0c Multi-Layer Architecture conform",
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issue_53_alignment="Layer separation established"
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)
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# Evidence
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adequacy_metadata.set_evidence("key", EvidenceType.CODE_DERIVED)
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adequacy_metadata.set_evidence("category", EvidenceType.CODE_DERIVED)
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adequacy_metadata.set_evidence("description", EvidenceType.CODE_DERIVED)
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adequacy_metadata.set_evidence("resolver_module", EvidenceType.CODE_DERIVED)
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adequacy_metadata.set_evidence("resolver_function", EvidenceType.CODE_DERIVED)
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|
adequacy_metadata.set_evidence("data_layer_module", EvidenceType.CODE_DERIVED)
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adequacy_metadata.set_evidence("data_layer_function", EvidenceType.CODE_DERIVED)
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|
adequacy_metadata.set_evidence("source_tables", EvidenceType.CODE_DERIVED)
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adequacy_metadata.set_evidence("semantic_contract", EvidenceType.MIXED) # code + explicit documentation
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|
adequacy_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
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|
adequacy_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)
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|
adequacy_metadata.set_evidence("time_window", EvidenceType.CODE_DERIVED)
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|
adequacy_metadata.set_evidence("output_type", EvidenceType.CODE_DERIVED)
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|
adequacy_metadata.set_evidence("placeholder_type", EvidenceType.MIXED)
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|
adequacy_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
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|
adequacy_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
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|
adequacy_metadata.set_evidence("confidence_logic", EvidenceType.UNRESOLVED)
|
|
adequacy_metadata.set_evidence("missing_value_policy", EvidenceType.CODE_DERIVED)
|
|
adequacy_metadata.set_evidence("known_limitations", EvidenceType.MIXED) # code analysis + draft
|
|
adequacy_metadata.set_evidence("layer_1_decision", EvidenceType.CODE_DERIVED)
|
|
adequacy_metadata.set_evidence("layer_2a_decision", EvidenceType.CODE_DERIVED)
|
|
adequacy_metadata.set_evidence("layer_2b_reuse_possible", EvidenceType.TO_VERIFY)
|
|
adequacy_metadata.set_evidence("architecture_alignment", EvidenceType.CODE_DERIVED)
|
|
adequacy_metadata.set_evidence("issue_53_alignment", EvidenceType.MIXED)
|
|
|
|
register_placeholder(adequacy_metadata)
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# Auto-register on import
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|
register_nutrition_part_b()
|