"""Tables du modèle de données EnerVision (CSV, API Mock et résultats ML).""" from datetime import datetime from decimal import Decimal from typing import Any from sqlalchemy import ( BigInteger, Boolean, CheckConstraint, DateTime, Double, ForeignKey, ForeignKeyConstraint, Index, Integer, Numeric, String, Text, UniqueConstraint, func, text, ) from sqlalchemy.dialects.postgresql import ARRAY, JSONB from sqlalchemy.orm import Mapped, mapped_column from app.db.base import Base class Dataset(Base): __tablename__ = "datasets" __table_args__ = ( CheckConstraint("dataset_id > 0", name="ck_datasets_positive_id"), UniqueConstraint("archive_sha256", name="uq_datasets_archive_sha256"), ) dataset_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) dataset_name: Mapped[str] = mapped_column(Text) archive_sha256: Mapped[str] = mapped_column(String(64)) storage_uri: Mapped[str] = mapped_column(Text) source_timezone: Mapped[str | None] = mapped_column(Text) # "metadata" est réservé par SQLAlchemy ; le nom SQL reste inchangé. dataset_metadata: Mapped[dict[str, Any]] = mapped_column("metadata", JSONB(none_as_null=True)) class Site(Base): __tablename__ = "sites" site_id: Mapped[str] = mapped_column(Text, primary_key=True) site_name: Mapped[str] = mapped_column(Text) site_type: Mapped[str] = mapped_column(Text) location: Mapped[str | None] = mapped_column(Text) capacity_kw: Mapped[float | None] = mapped_column(Double) status: Mapped[str | None] = mapped_column(Text) class Reading(Base): __tablename__ = "readings" __table_args__ = ( CheckConstraint( "source IN ('csv', 'api_current', 'api_history')", name="ck_readings_source" ), CheckConstraint( "(source = 'csv' AND dataset_id IS NOT NULL) OR " "(source IN ('api_current', 'api_history') AND dataset_id IS NULL)", name="ck_readings_dataset_source", ), CheckConstraint( "data_quality IS NULL OR data_quality IN ('good', 'partial', 'degraded', 'critical')", name="ck_readings_quality", ), CheckConstraint( "(imputed_values IS NULL AND imputation_method IS NULL) OR " "(imputed_values IS NOT NULL AND imputation_method IS NOT NULL)", name="ck_readings_imputation", ), Index("ix_readings_site_timestamp", "site_id", "timestamp"), Index("ix_readings_dataset_id", "dataset_id"), ) reading_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) site_id: Mapped[str] = mapped_column( Text, ForeignKey("sites.site_id", name="fk_readings_site", ondelete="RESTRICT") ) timestamp: Mapped[datetime] = mapped_column(DateTime(timezone=True), primary_key=True) source: Mapped[str] = mapped_column(Text) dataset_id: Mapped[int | None] = mapped_column( BigInteger, ForeignKey("datasets.dataset_id", name="fk_readings_dataset", ondelete="RESTRICT"), ) consumption_kw: Mapped[float | None] = mapped_column(Double) consumption_kwh: Mapped[float | None] = mapped_column(Double) consumption_euros: Mapped[Decimal | None] = mapped_column(Numeric(14, 2)) voltage_v: Mapped[float | None] = mapped_column(Double) current_a: Mapped[float | None] = mapped_column(Double) power_factor: Mapped[float | None] = mapped_column(Double) temperature_celsius: Mapped[float | None] = mapped_column(Double) humidity_percent: Mapped[float | None] = mapped_column(Double) solar_irradiance_wm2: Mapped[float | None] = mapped_column(Double) is_working_hours: Mapped[bool | None] = mapped_column(Boolean) data_quality: Mapped[str | None] = mapped_column(Text) null_reasons: Mapped[list[str] | None] = mapped_column(ARRAY(Text)) imputed_values: Mapped[dict[str, Any] | None] = mapped_column(JSONB(none_as_null=True)) imputation_method: Mapped[str | None] = mapped_column(Text) ingested_at: Mapped[datetime] = mapped_column( DateTime(timezone=True), server_default=func.now() ) raw_data: Mapped[dict[str, Any]] = mapped_column(JSONB(none_as_null=True)) Index( "uq_readings_source", Reading.site_id, Reading.timestamp, Reading.source, func.coalesce(Reading.dataset_id, text("0")), unique=True, ) class Prediction(Base): __tablename__ = "predictions" __table_args__ = ( UniqueConstraint("prediction_id", "site_id", name="uq_predictions_id_site"), Index("ix_predictions_site_target", "site_id", "target_at"), CheckConstraint( "target_metric IN ('consumption_kwh', 'consumption_kw')", name="ck_predictions_metric", ), CheckConstraint( "period_minutes IS NULL OR period_minutes > 0", name="ck_predictions_period" ), CheckConstraint( "target_metric <> 'consumption_kwh' OR period_minutes IS NOT NULL", name="ck_predictions_energy_period", ), CheckConstraint( "(status = 'available' AND predicted_value IS NOT NULL AND failure_reason IS NULL) OR " "(status IN ('insufficient_data', 'error') AND predicted_value IS NULL " "AND failure_reason IS NOT NULL)", name="ck_predictions_status", ), ) prediction_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) site_id: Mapped[str] = mapped_column( Text, ForeignKey("sites.site_id", name="fk_predictions_site", ondelete="RESTRICT") ) created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now()) target_at: Mapped[datetime] = mapped_column(DateTime(timezone=True)) target_metric: Mapped[str] = mapped_column(Text) period_minutes: Mapped[int | None] = mapped_column(Integer) predicted_value: Mapped[float | None] = mapped_column(Double) model_reference: Mapped[str] = mapped_column(Text) status: Mapped[str] = mapped_column(Text) failure_reason: Mapped[str | None] = mapped_column(Text) class Alert(Base): __tablename__ = "alerts" __table_args__ = ( UniqueConstraint("source", "site_id", "alert_id", name="uq_alerts_source_site_id"), Index("ix_alerts_site_timestamp", "site_id", "timestamp"), ForeignKeyConstraint( ["prediction_id", "site_id"], ["predictions.prediction_id", "predictions.site_id"], name="fk_alerts_prediction_site", ondelete="RESTRICT", ), CheckConstraint("source IN ('api_mock', 'enervision')", name="ck_alerts_source"), CheckConstraint( "type IN ('spike', 'threshold', 'anomaly', 'outage', 'sensor')", name="ck_alerts_type" ), CheckConstraint( "severity IN ('low', 'medium', 'high', 'critical')", name="ck_alerts_severity" ), ) id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) alert_id: Mapped[str] = mapped_column(Text) site_id: Mapped[str] = mapped_column( Text, ForeignKey("sites.site_id", name="fk_alerts_site", ondelete="RESTRICT") ) source: Mapped[str] = mapped_column(Text) timestamp: Mapped[datetime] = mapped_column(DateTime(timezone=True)) type: Mapped[str] = mapped_column(Text) severity: Mapped[str] = mapped_column(Text) message: Mapped[str] = mapped_column(Text) value: Mapped[float | None] = mapped_column(Double) threshold: Mapped[float | None] = mapped_column(Double) metric: Mapped[str | None] = mapped_column(Text) prediction_id: Mapped[int | None] = mapped_column(BigInteger) raw_data: Mapped[dict[str, Any]] = mapped_column(JSONB(none_as_null=True)) class Recommendation(Base): __tablename__ = "recommendations" __table_args__ = ( UniqueConstraint("alert_id", "rule_reference", name="uq_recommendations_alert_rule"), ) recommendation_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) alert_id: Mapped[int] = mapped_column( BigInteger, ForeignKey("alerts.id", name="fk_recommendations_alert", ondelete="RESTRICT") ) action: Mapped[str] = mapped_column(Text) explanation: Mapped[str] = mapped_column(Text) rule_reference: Mapped[str] = mapped_column(Text) created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())