Lars
2ea5f905c4
- Introduced functions to retrieve profile name, age, height, and gender for better placeholder resolution. - Added functions for displaying current date and time period labels (last 7, 30, and 90 days). - Updated PLACEHOLDER_MAP to utilize new functions for improved readability and maintainability. - Enhanced placeholder registrations in __init__.py to include new modules for sleep, vital metrics, and profile time periods. These changes enhance the flexibility and functionality of the placeholder system, allowing for more dynamic content generation.
97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
"""Registry: Korrelations- und Treiber-Metriken (Data Layer correlations)."""
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from placeholder_registry import (
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PlaceholderMetadata,
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MissingValuePolicy,
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OutputType,
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PlaceholderType,
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register_placeholder,
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)
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from ._evidence import tag_standard_evidence
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CAT = "Korrelationen"
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MVP = lambda reason, disp: MissingValuePolicy(
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available=False, value_raw=None, missing_reason=reason, legacy_display=disp
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)
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def register_korrelationen():
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for key, dl_fn, desc, tables, sem in [
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(
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"correlation_energy_weight_lag",
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"calculate_lag_correlation",
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"JSON: Lag-Korrelation Energiebilanz ↔ Gewicht",
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["nutrition_log", "weight_log"],
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"correlations.calculate_lag_correlation(pid, 'energy', 'weight')",
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),
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(
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"correlation_protein_lbm",
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"calculate_lag_correlation",
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"JSON: Lag-Korrelation Protein ↔ Magermasse",
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["nutrition_log", "weight_log", "caliper_log"],
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"correlations.calculate_lag_correlation(pid, 'protein', 'lbm')",
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),
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(
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"correlation_load_hrv",
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"calculate_lag_correlation",
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"JSON: Lag-Korrelation Trainingslast ↔ HRV",
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["activity_log", "vitals_baseline"],
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"correlations.calculate_lag_correlation(pid, 'training_load', 'hrv')",
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),
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(
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"correlation_load_rhr",
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"calculate_lag_correlation",
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"JSON: Lag-Korrelation Trainingslast ↔ Ruhepuls",
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["activity_log", "vitals_baseline"],
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"correlations.calculate_lag_correlation(pid, 'training_load', 'rhr')",
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),
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(
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"plateau_detected",
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"calculate_plateau_detected",
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"JSON: Platten-Erkennung (Gewicht/Körper)",
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["weight_log", "caliper_log"],
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"correlations.calculate_plateau_detected",
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),
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(
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"top_drivers",
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"calculate_top_drivers",
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"JSON: Top Treiber für Ziel-/Score-Variablen",
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["weight_log", "nutrition_log", "activity_log", "vitals_baseline", "sleep_log"],
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"correlations.calculate_top_drivers",
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),
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]:
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m = PlaceholderMetadata(
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key=key,
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category=CAT,
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description=desc,
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="_safe_json",
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data_layer_module="backend/data_layer/correlations.py",
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data_layer_function=dl_fn,
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source_tables=tables,
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semantic_contract=sem,
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business_meaning="Strukturierte Korrelationsausgabe für KI",
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unit="JSON",
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time_window="funktionsintern",
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output_type=OutputType.JSON,
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placeholder_type=PlaceholderType.RAW_DATA,
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format_hint="JSON-String",
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example_output="{}",
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minimum_data_requirements="Ausreichend gekoppelte Zeitreihen",
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quality_filter_policy=None,
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confidence_logic="Wie correlations.*",
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missing_value_policy=MVP("insufficient_data", "{}"),
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known_limitations="Bei wenigen Daten leer oder wenig robust",
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layer_1_decision=f"correlations.{dl_fn}",
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layer_2a_decision="_safe_json",
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layer_2b_reuse_possible=True,
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architecture_alignment="Phase 0c",
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issue_53_alignment="Layer 1",
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evidence={},
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)
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tag_standard_evidence(m)
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register_placeholder(m)
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register_korrelationen()
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