correlator/intelligence: let 10-8s actually close incidents
Replay of 09-22 10:00-12:00 ET (server-26#170): 0 of 19 incidents resolved on a clear, 19 on the idle timer, although 25 transmissions said 10-8/clear. Three independent breaks: 1. Short clears never reached extraction. "45-9, I'm clear." is <=5 words, so extract_scenes skipped it before GPT and cleared_units stayed empty. A rule parser now names the unit when it precedes the status word (never guesses: "10-8, 10-8." / "CMT clear." clear nobody) and returns a minimal scene that links by unit overlap but cannot open an incident. 2. Clearance compared unit strings exactly, so "11-Adam" clearing never removed "11 Adam". Now by _normalize_unit key. 3. units_active collected "Desk", "Central", "Division", "unknown", plate phonetics — none of which ever clear, so all-clear could never pass. Only units carrying a number (and not a ten-code) are tracked now; the rest stay in `units` for matching. c2-core: 463 pass. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
co-authored by
Claude Opus 5.5
parent
6e82ee8579
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8eac32caf5
@@ -224,6 +224,28 @@ def _normalize_unit(unit: str) -> str:
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return key or unit.strip().lower()
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def _is_trackable_unit(unit: str) -> bool:
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"""
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Whether a unit is concrete enough to hold an incident open until it clears.
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Extraction lists everything that sounds like a unit — "Desk", "Central",
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"Division", "sergeant", "unknown", and plate phonetics ("John Henry
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Zebra"). None of those ever transmit a 10-8, so while they sat in
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units_active the all-clear gate below could never pass: in the first
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replay (server-26#170, 09-22 10:00-12:00) 0 of 19 incidents resolved on
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a clear and every one had such a name in units_active. A real radio unit
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ID carries a number ("45-9", "11-Adam 2", "Whitestone 1", "E-14"), so
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only those gate resolution. The others are still kept in `units` and
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still match for correlation.
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"""
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if _TEN_CODE_RE.match((unit or "").strip()):
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return False # "10-8" read back as a unit ID is the status, not a unit
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return any(ch.isdigit() for ch in unit or "")
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_TEN_CODE_RE = re.compile(r"^10[\s-]?\d{1,2}$")
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def _unit_keys(units: Optional[list[str]]) -> set[str]:
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"""Comparison keys for a unit list, empties dropped."""
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return {k for k in (_normalize_unit(u) for u in (units or [])) if k}
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@@ -1910,11 +1932,15 @@ def _apply_unit_clearance(inc: dict, cleared: list[str]) -> tuple[list[str], lis
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"""
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units_active = list(inc.get("units_active") or [])
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units_cleared = list(inc.get("units_cleared") or [])
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# Compared by normalised key: the unit that cleared as "11-Adam" is the
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# one that went active as "11 Adam", and exact equality left it active.
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cleared_keys = _unit_keys(cleared)
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units_active = [u for u in units_active if _normalize_unit(u) not in cleared_keys]
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known_cleared = _unit_keys(units_cleared)
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for u in cleared:
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if u in units_active:
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units_active.remove(u)
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if u not in units_cleared:
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if _normalize_unit(u) not in known_cleared:
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units_cleared.append(u)
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known_cleared.add(_normalize_unit(u))
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auto_resolved = bool(units_cleared) and not units_active
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return units_active, units_cleared, auto_resolved
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@@ -2009,9 +2035,11 @@ async def _update_incident(
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# units_active = units currently on scene; units_cleared = units back in service
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units_active = list(inc.get("units_active") or [])
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units_cleared = list(inc.get("units_cleared") or [])
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tracked = _unit_keys(units_active) | _unit_keys(units_cleared)
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for u in call_units:
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if u not in units_cleared and u not in units_active:
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if _is_trackable_unit(u) and _normalize_unit(u) not in tracked:
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units_active.append(u)
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tracked.add(_normalize_unit(u))
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inc_with_active_update = {**inc, "units_active": units_active, "units_cleared": units_cleared}
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units_active, units_cleared, _ = _apply_unit_clearance(inc_with_active_update, cleared_units or [])
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@@ -2143,7 +2171,7 @@ async def _create_incident(
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"system_ids": [system_id] if system_id else [],
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"tags": tags + ["auto-generated"],
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"units": call_units,
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"units_active": list(call_units),
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"units_active": [u for u in call_units if _is_trackable_unit(u)],
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"units_cleared": [],
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"vehicles": call_vehicles,
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"srcaddrs": [call_srcaddr] if call_srcaddr else [],
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@@ -247,18 +247,26 @@ async def extract_scenes(
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f"Intelligence: call {call_id} — transcript too short for extraction "
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f"({len(transcript.split())} words), skipping"
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)
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cleared_unit = _short_clearance_unit(transcript)
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try:
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# Severity is still recorded: a five-word acknowledgement is genuinely
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# routine traffic, and downstream code treats a missing severity as
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# "not yet processed" rather than "nothing happened".
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await fstore.doc_set("calls", call_id, {
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updates = {
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"skip_reason": "transcript_too_short",
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"severity": "routine",
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"chatter_classifier_verdict": chatter_is_chatter,
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"chatter_classifier_reason": chatter_reason,
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})
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}
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if cleared_unit:
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updates["units"] = [cleared_unit]
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updates["cleared_units"] = [cleared_unit]
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await fstore.doc_set("calls", call_id, updates)
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except Exception:
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pass
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if cleared_unit:
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logger.info(f"Intelligence: call {call_id} — short clearance from {cleared_unit!r}")
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return [_clearance_scene(transcript, cleared_unit)]
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return []
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try:
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@@ -469,6 +477,52 @@ async def extract_scenes(
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return processed
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# "45-9, I'm clear." / "Vehicle 1, clear." / "Car 12 10-8" — a unit reporting
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# itself back in service is the one signal that ends an incident, and it is
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# almost always five words or fewer, which is exactly the population the
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# too-short skip above keeps away from GPT. In the first replay
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# (server-26#170, 09-22 10:00-12:00 ET) 25 transmissions said 10-8/clear and
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# 2 reached cleared_units. Rule-based on purpose: no model call, and only a
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# unit named BEFORE the status word counts, so "10-8, 10-8." or "CMT clear."
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# (no number) clears nobody rather than guessing.
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_CLEAR_WORD_RE = re.compile(
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r"\b(clear|10-?8|10-?98|back in service|in service|available)\b", re.IGNORECASE
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)
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_TEN_CODE_TOKEN_RE = re.compile(r"^10-?\d{1,2}$")
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_UNIT_PREFIX_WORDS = {"unit", "car", "vehicle", "engine", "ladder", "medic", "rescue", "post", "truck", "squad"}
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def _short_clearance_unit(transcript: str) -> Optional[str]:
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m = _CLEAR_WORD_RE.search(transcript or "")
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if not m:
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return None
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before = [t.strip(".,;:!?") for t in transcript[: m.start()].split()]
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before = [t for t in before if t]
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for i, tok in enumerate(before[:4]):
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if not any(ch.isdigit() for ch in tok) or _TEN_CODE_TOKEN_RE.match(tok):
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continue
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prev = before[i - 1] if i else ""
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if prev.lower() in _UNIT_PREFIX_WORDS:
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return f"{prev} {tok}"
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nxt = before[i + 1] if i + 1 < len(before) else ""
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if nxt.isalpha() and nxt.lower() not in {"i'm", "im", "is", "are", "to", "we're", "copy"} \
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and nxt[0].isupper():
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return f"{tok} {nxt}" # "11 Adam, clear"
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return tok
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return None
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def _clearance_scene(transcript: str, unit: str) -> dict:
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"""A minimal scene for a rule-parsed clearance: the unit, and nothing that
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could make the incident-creation gate open a new incident for it."""
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return {
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"tags": [], "incident_type": None, "location": None, "location_coords": None,
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"resolved": False, "severity": "routine", "vehicles": [], "units": [unit],
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"cleared_units": [unit], "reassignment": False, "transcript": transcript,
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"transcript_corrected": None, "segment_indices": [], "embedding": None,
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}
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def _geo_dist_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
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"""Haversine distance in km between two lat/lon points."""
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R = 6371.0
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@@ -0,0 +1,43 @@
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"""
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Dispatch→10-8 lifecycle, as measured by the first replay (server-26#170):
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0 of 19 incidents resolved on a clear although 25 transmissions said one.
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Three independent breaks, each pinned here.
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"""
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from app.internal import incident_correlator as ic
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from app.internal.intelligence import _clearance_scene, _short_clearance_unit
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def test_short_clearance_names_the_unit_that_cleared():
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assert _short_clearance_unit("45-9, I'm clear.") == "45-9"
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assert _short_clearance_unit("Vehicle 1, clear.") == "Vehicle 1"
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assert _short_clearance_unit("11 Adam, clear") == "11 Adam"
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assert _short_clearance_unit("Car 12 10-8") == "Car 12"
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def test_short_clearance_never_guesses():
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for t in ("10-8, 10-8.", "CMT clear.", "10-8, I'm back now. Clear.",
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"10-8, thank you.", "Show us 10-8, post 4.", "7, Charlie Central.", "10-4."):
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assert _short_clearance_unit(t) is None, t
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def test_clearance_scene_cannot_open_an_incident():
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scene = _clearance_scene("45-9, I'm clear.", "45-9")
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ctx = {"call_vehicles": scene["vehicles"], "coords": scene["location_coords"], "tags": scene["tags"]}
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assert not ic.has_event_substance(ctx)
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assert scene["severity"] == "routine" and scene["incident_type"] is None
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def test_clearance_matches_a_differently_spoken_unit():
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inc = {"units_active": ["11 Adam", "45-9"], "units_cleared": []}
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active, cleared, resolved = ic._apply_unit_clearance(inc, ["11-Adam"])
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assert active == ["45-9"]
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active, cleared, resolved = ic._apply_unit_clearance(
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{"units_active": active, "units_cleared": cleared}, ["45 9"])
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assert active == [] and resolved
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def test_only_numbered_units_hold_an_incident_open():
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for junk in ("Desk", "Central", "Division", "sergeant", "unknown", "John", "Zebra", "10-8", "10 4"):
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assert not ic._is_trackable_unit(junk), junk
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for real in ("45-9", "11-Adam", "Whitestone 1", "E-14", "Highway 3-4", "7"):
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assert ic._is_trackable_unit(real), real
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