Files
serversdown a073b9b06e fix(deployment-timeline): respect user timezone for display and edits
Deployment timestamps were stored correctly as UTC but rendered raw —
a 1:30 PM EDT swap displayed as "5:30" because the frontend sliced the
naive UTC ISO string straight to the screen.

Display side: deployment_timeline.py now converts every emitted
timestamp (starts_at, ends_at, event_overlay.peak_pvs_at and last_event)
through `utc_to_local()` using the user's configured timezone from
UserPreferences before serializing.  Frontend slice keeps working — it
just slices a local-time string now.

Write side (so the new edit / add-historical-assignment modals stay
consistent):
  - PATCH /api/projects/{pid}/assignments/{aid}
  - POST  /api/projects/{pid}/locations/{loc}/assign
both now interpret a *naive* assigned_at / assigned_until ISO string as
the user's local time and convert to UTC for storage via
`local_to_utc()`.  Explicit tz-aware strings ("...Z" or "...+00:00")
skip the conversion so programmatic callers that already speak UTC
keep working.

Verified live: BE13121's stored 2026-01-28 18:06:29 UTC now serializes
as 2026-01-28 13:06:29 in the timeline endpoint; PATCHing
"2026-01-28T13:06:29" round-trips back to the same UTC value.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-18 21:45:52 +00:00

319 lines
12 KiB
Python

"""
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,
)
from backend.utils.timezone import utc_to_local
log = logging.getLogger("backend.services.deployment_timeline")
def _iso_local(dt) -> Optional[str]:
"""Serialize a datetime / ISO-string in the user's configured timezone.
The timeline frontend slices these strings to character 19 to produce
"YYYY-MM-DD HH:MM:SS" — no JS-side timezone conversion happens. We
therefore emit *already-local* timestamps here so the displayed time
matches what the operator actually saw on the wall clock.
Accepts either a ``datetime`` (DB column) or an ISO ``str`` (SFM
response). Returns ``None`` for ``None`` input. Naive ISO strings
from SFM are interpreted as UTC.
"""
if dt is None:
return None
if isinstance(dt, str):
try:
dt = datetime.fromisoformat(dt.replace("Z", "").replace(" ", "T"))
except ValueError:
return dt # give up gracefully — emit whatever SFM sent
local = utc_to_local(dt)
if local is None:
return None
return local.replace(tzinfo=None).isoformat()
# 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": _iso_local(peak_at),
"last_event": _iso_local(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_local(a.assigned_at),
"ends_at": _iso_local(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_local(gap_start),
"ends_at": _iso_local(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_local(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,
}