"""
Deployment timeline service — replaces the legacy `deployment_records`-driven
timeline on the seismograph unit detail page.

Architecture:
  - `unit_assignments` is the authoritative source for "where was this unit"
    (one row per location/time-window).  Auto-written by the project location
    swap/assign/unassign/update workflows.
  - `unit_history` is the audit log for non-location state changes
    (calibration toggles, retirement, allocation, etc.).
  - SFM events are overlaid per assignment window to show "what was the unit
    actually doing during this deployment" (count + peak PVS + last-event).

Gaps between assignments are emitted as synthetic "gap" entries so operators
can see when the unit was idle vs out-of-service.

`deployment_records` is being deprecated; this module does not read it.
"""

from __future__ import annotations

import asyncio
import logging
from datetime import datetime, timedelta
from typing import Optional

import httpx
from sqlalchemy.orm import Session

from backend.models import (
    UnitAssignment,
    UnitHistory,
    MonitoringLocation,
    Project,
    RosterUnit,
)
from backend.services.sfm_events import (
    SFM_BASE_URL,
    _fetch_events_for_serial,
    _iso_utc,
)

log = logging.getLogger("backend.services.deployment_timeline")

# Don't emit synthetic gap entries shorter than this (seconds).  Avoids visual
# clutter from a sub-second handoff during a swap workflow.
_MIN_GAP_SECONDS = 24 * 3600  # 1 day

# When detecting "mergeable" groups of consecutive same-location assignments,
# treat assignments separated by no more than this many seconds as adjacent.
# Generous enough to catch overnight handoffs and weekend gaps where the
# operator forgot to log, but tight enough that genuinely separate
# deployments months apart don't get suggested for merging.
_MERGE_GAP_TOLERANCE_SECONDS = 7 * 24 * 3600   # 7 days

# Per-call timeout when querying SFM for the event overlay.
_SFM_TIMEOUT = 10.0
_SFM_FETCH_CEILING = 5000


# ── Public API ────────────────────────────────────────────────────────────────


async def deployment_timeline_for_unit(
    db: Session,
    unit_id: str,
    *,
    include_event_overlay: bool = True,
) -> dict:
    """Build a chronological timeline for a unit.

    Returns:
      {
        "unit_id":       str,
        "device_type":   str,
        "entries": [
          {
            "kind":        "assignment" | "gap" | "state_change",
            "starts_at":   ISO timestamp,
            "ends_at":     ISO timestamp | None,
            "duration_days": float | None,
            # — assignment-only fields —
            "assignment_id":  str,
            "location_id":    str,
            "location_name":  str,
            "project_id":     str,
            "project_name":   str,
            "is_active":      bool,
            "event_overlay":  {event_count, peak_pvs, peak_pvs_at, last_event}
                              or None if include_event_overlay=False,
            "notes":          str | None,
            # — gap-only fields —
            "context":        "between assignments" | None,
            # — state_change-only fields —
            "change_type":    str,
            "field_name":     str | None,
            "old_value":      str | None,
            "new_value":      str | None,
            "source":         str,
            "history_notes":  str | None,
          },
          ...                            # newest first
        ],
      }
    """
    unit = db.query(RosterUnit).filter_by(id=unit_id).first()
    if not unit:
        return {"unit_id": unit_id, "device_type": None, "entries": []}

    # 1. Load assignments + their location/project lookups in bulk.
    assignments = (
        db.query(UnitAssignment)
        .filter(UnitAssignment.unit_id == unit_id)
        .order_by(UnitAssignment.assigned_at.asc())
        .all()
    )

    loc_ids = {a.location_id for a in assignments}
    proj_ids = {a.project_id for a in assignments}
    loc_map = {
        l.id: l for l in db.query(MonitoringLocation).filter(
            MonitoringLocation.id.in_(loc_ids)
        ).all()
    } if loc_ids else {}
    proj_map = {
        p.id: p for p in db.query(Project).filter(
            Project.id.in_(proj_ids)
        ).all()
    } if proj_ids else {}

    # 2. Load relevant unit_history rows.  We surface state changes that
    #    operators care about on a deployment timeline: calibration status,
    #    retirement, deployed flag, allocation, calibration date, and the
    #    assignment_* events we just added (those are redundant with the
    #    assignment rows themselves, so we skip them to avoid double-rendering).
    interesting_change_types = (
        "calibration_status_change",
        "retired_change",
        "deployed_change",
        "allocation_change",
        "last_calibrated_change",
        "next_calibration_due_change",
    )
    history = (
        db.query(UnitHistory)
        .filter(UnitHistory.unit_id == unit_id)
        .filter(UnitHistory.change_type.in_(interesting_change_types))
        .order_by(UnitHistory.changed_at.asc())
        .all()
    )

    now = datetime.utcnow()

    # 3. Optionally fetch SFM event overlay for each assignment window.
    #    Concurrent fan-out via httpx + asyncio.gather.
    overlays: dict[str, dict] = {}
    if include_event_overlay and assignments and unit.device_type == "seismograph":
        async with httpx.AsyncClient(timeout=_SFM_TIMEOUT) as client:
            results = await asyncio.gather(
                *(
                    _fetch_events_for_serial(
                        client,
                        serial=unit_id,
                        from_dt=a.assigned_at,
                        to_dt=a.assigned_until or now,
                        false_trigger=None,
                        limit=_SFM_FETCH_CEILING,
                    )
                    for a in assignments
                ),
                return_exceptions=False,
            )
        for a, events in zip(assignments, results):
            peak = None
            peak_at = None
            last_ev = None
            for ev in events:
                pvs = ev.get("peak_vector_sum")
                if pvs is not None and (peak is None or pvs > peak):
                    peak = pvs
                    peak_at = ev.get("timestamp")
                ts = ev.get("timestamp")
                if ts and (last_ev is None or ts > last_ev):
                    last_ev = ts
            overlays[a.id] = {
                "event_count": len(events),
                "peak_pvs":    peak,
                "peak_pvs_at": peak_at,
                "last_event":  last_ev,
            }

    # 4. Build entries.  Start by emitting assignment rows + gap rows between
    #    consecutive assignments, then add state-change rows from unit_history.
    entries: list[dict] = []

    for idx, a in enumerate(assignments):
        loc = loc_map.get(a.location_id)
        proj = proj_map.get(a.project_id)
        is_active = a.assigned_until is None
        ends_at = a.assigned_until or now
        duration_days = (ends_at - a.assigned_at).total_seconds() / 86400 if a.assigned_at else None

        entry = {
            "kind":          "assignment",
            "starts_at":     _iso_utc(a.assigned_at),
            "ends_at":       _iso_utc(a.assigned_until),
            "duration_days": round(duration_days, 1) if duration_days is not None else None,
            "assignment_id": a.id,
            "location_id":   a.location_id,
            "location_name": loc.name if loc else None,
            "project_id":    a.project_id,
            "project_name":  proj.name if proj else None,
            "is_active":     is_active,
            "notes":         a.notes,
            "event_overlay": overlays.get(a.id),
        }
        entries.append(entry)

        # Gap detection: from the end of this assignment to the start of the
        # next one.  Only emit gaps that are at least _MIN_GAP_SECONDS long
        # so trivial sub-second handoffs during swaps don't clutter the view.
        if idx + 1 < len(assignments):
            next_a = assignments[idx + 1]
            gap_start = a.assigned_until or now
            gap_end = next_a.assigned_at
            gap_seconds = (gap_end - gap_start).total_seconds() if gap_end and gap_start else 0
            if gap_seconds >= _MIN_GAP_SECONDS:
                entries.append({
                    "kind":          "gap",
                    "starts_at":     _iso_utc(gap_start),
                    "ends_at":       _iso_utc(gap_end),
                    "duration_days": round(gap_seconds / 86400, 1),
                    "context":       "between assignments",
                })

    # 5. State changes — interleaved by timestamp.  Skip no-op rows where
    #    old_value == new_value (an artifact of the legacy record_history()
    #    being called on every save regardless of whether the field changed).
    for h in history:
        if h.old_value == h.new_value:
            continue
        entries.append({
            "kind":          "state_change",
            "starts_at":     _iso_utc(h.changed_at),
            "ends_at":       None,
            "duration_days": None,
            "change_type":   h.change_type,
            "field_name":    h.field_name,
            "old_value":     h.old_value,
            "new_value":     h.new_value,
            "source":        h.source,
            "history_notes": h.notes,
        })

    # 6. Detect mergeable groups — runs of consecutive assignments to the
    #    same location with small gaps between them.  Each group becomes a
    #    list of assignment_ids; the UI offers a "Merge into one" action
    #    on any group >= 2.
    merge_groups: list[list[str]] = []
    if len(assignments) >= 2:
        # Sort ascending for the linear scan.
        sorted_assignments = sorted(assignments, key=lambda a: a.assigned_at)
        cur_group: list[UnitAssignment] = [sorted_assignments[0]]
        for a in sorted_assignments[1:]:
            prev = cur_group[-1]
            same_location = a.location_id == prev.location_id
            prev_end = prev.assigned_until or now
            gap_seconds = (a.assigned_at - prev_end).total_seconds() if a.assigned_at else 0
            # Within tolerance and same location → extend the current group.
            # Negative gaps (overlap) also count as adjacent.
            if same_location and gap_seconds <= _MERGE_GAP_TOLERANCE_SECONDS:
                cur_group.append(a)
            else:
                if len(cur_group) >= 2:
                    merge_groups.append([x.id for x in cur_group])
                cur_group = [a]
        if len(cur_group) >= 2:
            merge_groups.append([x.id for x in cur_group])

    # 7. Sort newest first.  Active assignments (no end) sort by start time,
    #    same as everything else.
    entries.sort(key=lambda e: e.get("starts_at") or "", reverse=True)

    return {
        "unit_id":      unit.id,
        "device_type":  unit.device_type,
        "entries":      entries,
        # List of assignment_id lists; each inner list is a mergeable group.
        # Empty if nothing is mergeable.  UI shows a "Merge" button on any
        # row whose assignment_id appears in a group.
        "merge_groups": merge_groups,
    }