bambuddy/backend/app/models/print_log.py

from datetime import datetime

from sqlalchemy import Boolean, DateTime, Float, ForeignKey, Integer, String, func
from sqlalchemy.orm import Mapped, mapped_column

from backend.app.core.database import Base


class PrintLogEntry(Base):
    """Independent print log entry. Written when print events occur.

    This is a separate table from archives/queue — clearing the log
    never touches archives or queue items.

    archive_id is a nullable FK so log entries survive archive deletion (ON
    DELETE SET NULL). Aggregating runs per archive — for the per-archive
    "Print Log" view and for statistics that should not double-count
    overwritten archives (#1378) — is done via WHERE archive_id = X.
    """

    __tablename__ = "print_log_entries"

    id: Mapped[int] = mapped_column(primary_key=True)
    archive_id: Mapped[int | None] = mapped_column(
        ForeignKey("print_archives.id", ondelete="SET NULL"), nullable=True, index=True
    )
    # Which queue item produced this run, when one did. Printer-initiated
    # prints have none. Batch cost/energy roll-up joins on this (#342): the
    # archive alone can't attribute a run to an order because several orders
    # — and plain reprints — share one archive.
    queue_item_id: Mapped[int | None] = mapped_column(
        ForeignKey("print_queue.id", ondelete="SET NULL"), nullable=True, index=True
    )
    print_name: Mapped[str | None] = mapped_column(String(255))
    # Which plate of a multi-plate 3MF this run printed (1-based). The archive
    # holds one plate, its latest run's, and every run of a file shares that
    # archive, so a run's own plate is kept here. NULL when not known.
    plate_id: Mapped[int | None] = mapped_column(Integer, nullable=True)
    printer_name: Mapped[str | None] = mapped_column(String(255))
    printer_id: Mapped[int | None] = mapped_column(Integer)
    status: Mapped[str] = mapped_column(String(20))  # completed, failed, stopped, cancelled, skipped
    started_at: Mapped[datetime | None] = mapped_column(DateTime)
    completed_at: Mapped[datetime | None] = mapped_column(DateTime)
    duration_seconds: Mapped[int | None] = mapped_column(Integer)
    filament_type: Mapped[str | None] = mapped_column(String(50))
    filament_color: Mapped[str | None] = mapped_column(String(50))
    filament_used_grams: Mapped[float | None] = mapped_column(Float)
    cost: Mapped[float | None] = mapped_column(Float)
    energy_kwh: Mapped[float | None] = mapped_column(Float)
    energy_cost: Mapped[float | None] = mapped_column(Float)
    # Printer wear for this run: duration at the printer's hourly wear cost (#694).
    wear_cost: Mapped[float | None] = mapped_column(Float)
    # True when the run's times were not measured: a print closed at reconnect
    # (#3310), whose end is its start plus the slicer's estimate when it
    # completed, or the reconnect (duration 0). Counted in
    # the totals, left out of the time accuracy. NULL for a measured run.
    duration_unmeasured: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
    failure_reason: Mapped[str | None] = mapped_column(String(100))
    # User quality verdict ('good' / 'reject'), mirrored from the archive on
    # PATCH exactly like status/failure_reason (#1444 mirror) so verdict-aware
    # statistics can stay on this table (#1898).
    user_verdict: Mapped[str | None] = mapped_column(String(10), nullable=True)
    thumbnail_path: Mapped[str | None] = mapped_column(String(500))
    created_by_id: Mapped[int | None] = mapped_column(ForeignKey("users.id", ondelete="SET NULL"), nullable=True)
    created_by_username: Mapped[str | None] = mapped_column(String(100))
    created_at: Mapped[datetime] = mapped_column(DateTime, server_default=func.now())