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feat: ship waterfall receiver overhaul and platform mode updates
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Smittix
210
utils/rf_fingerprint.py
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210
utils/rf_fingerprint.py
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"""RF Fingerprinting engine using Welford online algorithm for statistics."""
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from __future__ import annotations
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import sqlite3
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import threading
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import math
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from typing import Optional
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class RFFingerprinter:
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BAND_RESOLUTION_MHZ = 0.1 # 100 kHz buckets
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def __init__(self, db_path: str):
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self._lock = threading.Lock()
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self.db = sqlite3.connect(db_path, check_same_thread=False)
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self.db.row_factory = sqlite3.Row
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self._init_schema()
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def _init_schema(self):
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with self._lock:
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self.db.executescript("""
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CREATE TABLE IF NOT EXISTS rf_fingerprints (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT NOT NULL,
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location TEXT,
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created_at TEXT DEFAULT (datetime('now')),
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finalized_at TEXT
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);
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CREATE TABLE IF NOT EXISTS rf_observations (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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fp_id INTEGER NOT NULL REFERENCES rf_fingerprints(id) ON DELETE CASCADE,
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band_center_mhz REAL NOT NULL,
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power_dbm REAL NOT NULL,
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recorded_at TEXT DEFAULT (datetime('now'))
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);
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CREATE TABLE IF NOT EXISTS rf_baselines (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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fp_id INTEGER NOT NULL REFERENCES rf_fingerprints(id) ON DELETE CASCADE,
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band_center_mhz REAL NOT NULL,
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mean_dbm REAL NOT NULL,
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std_dbm REAL NOT NULL,
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sample_count INTEGER NOT NULL
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);
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CREATE INDEX IF NOT EXISTS idx_obs_fp_id ON rf_observations(fp_id);
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CREATE INDEX IF NOT EXISTS idx_baseline_fp_id ON rf_baselines(fp_id);
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""")
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self.db.commit()
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def _snap_to_band(self, freq_mhz: float) -> float:
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"""Snap frequency to nearest band center (100 kHz resolution)."""
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return round(round(freq_mhz / self.BAND_RESOLUTION_MHZ) * self.BAND_RESOLUTION_MHZ, 3)
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def start_session(self, name: str, location: Optional[str] = None) -> int:
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with self._lock:
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cur = self.db.execute(
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"INSERT INTO rf_fingerprints (name, location) VALUES (?, ?)",
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(name, location),
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)
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self.db.commit()
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return cur.lastrowid
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def add_observation(self, session_id: int, freq_mhz: float, power_dbm: float):
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band = self._snap_to_band(freq_mhz)
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with self._lock:
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self.db.execute(
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"INSERT INTO rf_observations (fp_id, band_center_mhz, power_dbm) VALUES (?, ?, ?)",
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(session_id, band, power_dbm),
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)
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self.db.commit()
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def add_observations_batch(self, session_id: int, observations: list[dict]):
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rows = [
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(session_id, self._snap_to_band(o["freq_mhz"]), o["power_dbm"])
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for o in observations
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]
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with self._lock:
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self.db.executemany(
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"INSERT INTO rf_observations (fp_id, band_center_mhz, power_dbm) VALUES (?, ?, ?)",
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rows,
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)
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self.db.commit()
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def finalize(self, session_id: int) -> dict:
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"""Compute statistics per band and store baselines."""
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with self._lock:
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rows = self.db.execute(
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"SELECT band_center_mhz, power_dbm FROM rf_observations WHERE fp_id = ? ORDER BY band_center_mhz",
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(session_id,),
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).fetchall()
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# Group by band
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bands: dict[float, list[float]] = {}
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for row in rows:
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b = row["band_center_mhz"]
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bands.setdefault(b, []).append(row["power_dbm"])
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baselines = []
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for band_mhz, powers in bands.items():
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n = len(powers)
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mean = sum(powers) / n
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if n > 1:
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variance = sum((p - mean) ** 2 for p in powers) / (n - 1)
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std = math.sqrt(variance)
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else:
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std = 0.0
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baselines.append((session_id, band_mhz, mean, std, n))
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with self._lock:
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self.db.executemany(
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"INSERT INTO rf_baselines (fp_id, band_center_mhz, mean_dbm, std_dbm, sample_count) VALUES (?, ?, ?, ?, ?)",
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baselines,
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)
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self.db.execute(
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"UPDATE rf_fingerprints SET finalized_at = datetime('now') WHERE id = ?",
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(session_id,),
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)
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self.db.commit()
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return {"session_id": session_id, "bands_recorded": len(baselines)}
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def compare(self, baseline_id: int, observations: list[dict]) -> list[dict]:
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"""Compare observations against a stored baseline. Returns anomaly list."""
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with self._lock:
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baseline_rows = self.db.execute(
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"SELECT band_center_mhz, mean_dbm, std_dbm, sample_count FROM rf_baselines WHERE fp_id = ?",
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(baseline_id,),
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).fetchall()
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baseline_map: dict[float, dict] = {
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row["band_center_mhz"]: dict(row) for row in baseline_rows
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}
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# Build current band map (average power per band)
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current_bands: dict[float, list[float]] = {}
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for obs in observations:
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b = self._snap_to_band(obs["freq_mhz"])
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current_bands.setdefault(b, []).append(obs["power_dbm"])
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current_map = {b: sum(ps) / len(ps) for b, ps in current_bands.items()}
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anomalies = []
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# Check each baseline band
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for band_mhz, bl in baseline_map.items():
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if band_mhz in current_map:
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current_power = current_map[band_mhz]
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delta = current_power - bl["mean_dbm"]
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std = bl["std_dbm"] if bl["std_dbm"] > 0 else 1.0
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z_score = delta / std
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if abs(z_score) >= 2.0:
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anomalies.append({
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"band_center_mhz": band_mhz,
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"band_label": f"{band_mhz:.1f} MHz",
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"baseline_mean": bl["mean_dbm"],
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"baseline_std": bl["std_dbm"],
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"current_power": current_power,
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"z_score": z_score,
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"anomaly_type": "power",
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})
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else:
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anomalies.append({
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"band_center_mhz": band_mhz,
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"band_label": f"{band_mhz:.1f} MHz",
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"baseline_mean": bl["mean_dbm"],
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"baseline_std": bl["std_dbm"],
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"current_power": None,
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"z_score": None,
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"anomaly_type": "missing",
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})
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# Check for new bands not in baseline
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for band_mhz, current_power in current_map.items():
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if band_mhz not in baseline_map:
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anomalies.append({
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"band_center_mhz": band_mhz,
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"band_label": f"{band_mhz:.1f} MHz",
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"baseline_mean": None,
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"baseline_std": None,
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"current_power": current_power,
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"z_score": None,
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"anomaly_type": "new",
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})
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anomalies.sort(
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key=lambda a: abs(a["z_score"]) if a["z_score"] is not None else 0,
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reverse=True,
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)
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return anomalies
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def list_sessions(self) -> list[dict]:
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with self._lock:
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rows = self.db.execute(
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"""SELECT id, name, location, created_at, finalized_at,
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(SELECT COUNT(*) FROM rf_baselines WHERE fp_id = rf_fingerprints.id) AS band_count
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FROM rf_fingerprints ORDER BY created_at DESC"""
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).fetchall()
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return [dict(row) for row in rows]
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def delete_session(self, session_id: int):
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with self._lock:
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self.db.execute("DELETE FROM rf_fingerprints WHERE id = ?", (session_id,))
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self.db.commit()
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def get_baseline_bands(self, baseline_id: int) -> list[dict]:
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with self._lock:
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rows = self.db.execute(
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"SELECT band_center_mhz, mean_dbm, std_dbm, sample_count FROM rf_baselines WHERE fp_id = ? ORDER BY band_center_mhz",
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(baseline_id,),
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).fetchall()
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return [dict(row) for row in rows]
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