feat(apps): cree les six tables data et l'hypertable readings
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Meryemel-gham
@@ -1,2 +1,6 @@
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# Piege : tout modele absent de ce module reste invisible de `alembic revision
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# --autogenerate`, qui genererait alors un drop de sa table.
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from app.models.energy import Alert, Dataset, Prediction, Reading, Recommendation, Site
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__all__ = ["Alert", "Dataset", "Prediction", "Reading", "Recommendation", "Site"]
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@@ -0,0 +1,209 @@
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"""Tables du modèle de données EnerVision (CSV, API Mock et résultats ML)."""
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from datetime import datetime
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from decimal import Decimal
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from typing import Any
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from sqlalchemy import (
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BigInteger,
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Boolean,
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CheckConstraint,
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DateTime,
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Double,
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ForeignKey,
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ForeignKeyConstraint,
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Index,
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Integer,
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Numeric,
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String,
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Text,
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UniqueConstraint,
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func,
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text,
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)
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from sqlalchemy.dialects.postgresql import ARRAY, JSONB
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from sqlalchemy.orm import Mapped, mapped_column
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from app.db.base import Base
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class Dataset(Base):
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__tablename__ = "datasets"
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__table_args__ = (
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CheckConstraint("dataset_id > 0", name="ck_datasets_positive_id"),
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UniqueConstraint("archive_sha256", name="uq_datasets_archive_sha256"),
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)
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dataset_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True)
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dataset_name: Mapped[str] = mapped_column(Text)
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archive_sha256: Mapped[str] = mapped_column(String(64))
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storage_uri: Mapped[str] = mapped_column(Text)
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source_timezone: Mapped[str | None] = mapped_column(Text)
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# "metadata" est réservé par SQLAlchemy ; le nom SQL reste inchangé.
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dataset_metadata: Mapped[dict[str, Any]] = mapped_column("metadata", JSONB(none_as_null=True))
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class Site(Base):
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__tablename__ = "sites"
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site_id: Mapped[str] = mapped_column(Text, primary_key=True)
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site_name: Mapped[str] = mapped_column(Text)
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site_type: Mapped[str] = mapped_column(Text)
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location: Mapped[str | None] = mapped_column(Text)
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capacity_kw: Mapped[float | None] = mapped_column(Double)
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status: Mapped[str | None] = mapped_column(Text)
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class Reading(Base):
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__tablename__ = "readings"
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__table_args__ = (
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CheckConstraint(
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"source IN ('csv', 'api_current', 'api_history')", name="ck_readings_source"
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),
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CheckConstraint(
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"(source = 'csv' AND dataset_id IS NOT NULL) OR "
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"(source IN ('api_current', 'api_history') AND dataset_id IS NULL)",
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name="ck_readings_dataset_source",
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),
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CheckConstraint(
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"data_quality IS NULL OR data_quality IN ('good', 'partial', 'degraded', 'critical')",
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name="ck_readings_quality",
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),
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CheckConstraint(
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"(imputed_values IS NULL AND imputation_method IS NULL) OR "
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"(imputed_values IS NOT NULL AND imputation_method IS NOT NULL)",
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name="ck_readings_imputation",
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),
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Index("ix_readings_site_timestamp", "site_id", "timestamp"),
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Index("ix_readings_dataset_id", "dataset_id"),
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)
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reading_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True)
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site_id: Mapped[str] = mapped_column(
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Text, ForeignKey("sites.site_id", name="fk_readings_site", ondelete="RESTRICT")
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)
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timestamp: Mapped[datetime] = mapped_column(DateTime(timezone=True), primary_key=True)
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source: Mapped[str] = mapped_column(Text)
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dataset_id: Mapped[int | None] = mapped_column(
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BigInteger,
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ForeignKey("datasets.dataset_id", name="fk_readings_dataset", ondelete="RESTRICT"),
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)
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consumption_kw: Mapped[float | None] = mapped_column(Double)
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consumption_kwh: Mapped[float | None] = mapped_column(Double)
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consumption_euros: Mapped[Decimal | None] = mapped_column(Numeric(14, 2))
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voltage_v: Mapped[float | None] = mapped_column(Double)
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current_a: Mapped[float | None] = mapped_column(Double)
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power_factor: Mapped[float | None] = mapped_column(Double)
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temperature_celsius: Mapped[float | None] = mapped_column(Double)
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humidity_percent: Mapped[float | None] = mapped_column(Double)
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solar_irradiance_wm2: Mapped[float | None] = mapped_column(Double)
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is_working_hours: Mapped[bool | None] = mapped_column(Boolean)
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data_quality: Mapped[str | None] = mapped_column(Text)
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null_reasons: Mapped[list[str] | None] = mapped_column(ARRAY(Text))
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imputed_values: Mapped[dict[str, Any] | None] = mapped_column(JSONB(none_as_null=True))
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imputation_method: Mapped[str | None] = mapped_column(Text)
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ingested_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now()
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)
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raw_data: Mapped[dict[str, Any]] = mapped_column(JSONB(none_as_null=True))
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Index(
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"uq_readings_source",
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Reading.site_id,
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Reading.timestamp,
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Reading.source,
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func.coalesce(Reading.dataset_id, text("0")),
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unique=True,
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)
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class Prediction(Base):
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__tablename__ = "predictions"
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__table_args__ = (
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UniqueConstraint("prediction_id", "site_id", name="uq_predictions_id_site"),
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Index("ix_predictions_site_target", "site_id", "target_at"),
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CheckConstraint(
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"target_metric IN ('consumption_kwh', 'consumption_kw')",
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name="ck_predictions_metric",
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),
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CheckConstraint(
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"period_minutes IS NULL OR period_minutes > 0", name="ck_predictions_period"
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),
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CheckConstraint(
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"target_metric <> 'consumption_kwh' OR period_minutes IS NOT NULL",
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name="ck_predictions_energy_period",
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),
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CheckConstraint(
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"(status = 'available' AND predicted_value IS NOT NULL AND failure_reason IS NULL) OR "
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"(status IN ('insufficient_data', 'error') AND predicted_value IS NULL "
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"AND failure_reason IS NOT NULL)",
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name="ck_predictions_status",
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),
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)
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prediction_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True)
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site_id: Mapped[str] = mapped_column(
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Text, ForeignKey("sites.site_id", name="fk_predictions_site", ondelete="RESTRICT")
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)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
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target_at: Mapped[datetime] = mapped_column(DateTime(timezone=True))
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target_metric: Mapped[str] = mapped_column(Text)
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period_minutes: Mapped[int | None] = mapped_column(Integer)
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predicted_value: Mapped[float | None] = mapped_column(Double)
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model_reference: Mapped[str] = mapped_column(Text)
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status: Mapped[str] = mapped_column(Text)
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failure_reason: Mapped[str | None] = mapped_column(Text)
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class Alert(Base):
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__tablename__ = "alerts"
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__table_args__ = (
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UniqueConstraint("source", "site_id", "alert_id", name="uq_alerts_source_site_id"),
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Index("ix_alerts_site_timestamp", "site_id", "timestamp"),
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ForeignKeyConstraint(
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["prediction_id", "site_id"],
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["predictions.prediction_id", "predictions.site_id"],
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name="fk_alerts_prediction_site",
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ondelete="RESTRICT",
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),
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CheckConstraint("source IN ('api_mock', 'enervision')", name="ck_alerts_source"),
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CheckConstraint(
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"type IN ('spike', 'threshold', 'anomaly', 'outage', 'sensor')", name="ck_alerts_type"
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),
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CheckConstraint(
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"severity IN ('low', 'medium', 'high', 'critical')", name="ck_alerts_severity"
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),
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)
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id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True)
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alert_id: Mapped[str] = mapped_column(Text)
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site_id: Mapped[str] = mapped_column(
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Text, ForeignKey("sites.site_id", name="fk_alerts_site", ondelete="RESTRICT")
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)
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source: Mapped[str] = mapped_column(Text)
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timestamp: Mapped[datetime] = mapped_column(DateTime(timezone=True))
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type: Mapped[str] = mapped_column(Text)
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severity: Mapped[str] = mapped_column(Text)
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message: Mapped[str] = mapped_column(Text)
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value: Mapped[float | None] = mapped_column(Double)
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threshold: Mapped[float | None] = mapped_column(Double)
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metric: Mapped[str | None] = mapped_column(Text)
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prediction_id: Mapped[int | None] = mapped_column(BigInteger)
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raw_data: Mapped[dict[str, Any]] = mapped_column(JSONB(none_as_null=True))
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class Recommendation(Base):
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__tablename__ = "recommendations"
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__table_args__ = (
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UniqueConstraint("alert_id", "rule_reference", name="uq_recommendations_alert_rule"),
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)
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recommendation_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True)
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alert_id: Mapped[int] = mapped_column(
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BigInteger, ForeignKey("alerts.id", name="fk_recommendations_alert", ondelete="RESTRICT")
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)
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action: Mapped[str] = mapped_column(Text)
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explanation: Mapped[str] = mapped_column(Text)
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rule_reference: Mapped[str] = mapped_column(Text)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
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