6f2f6d2d72
- NAV viewer v5: grid 2x2 (map + charts), trail slider, play, layer toggles - Datebar: date picker + datalist, fetches /api/data-dates from :8766 - Mission label shows #NN-folder (X sessions) in green or grey - Tools: parse_usv_nav, extract_mcap_signals, extract_usv_pwm, merge_nav_usbl, usbl_to_json, check_sync, parse_kogger_usbl - Vendor: Kogger-Protocol docs
320 lines
12 KiB
Python
320 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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merge_nav_usbl.py — Merge USBL decoded data with USV navigation log
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Inputs:
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--usbl : combined_usbl.csv (output of parse_kogger_usbl.py)
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--nav-dir: directory containing *_navigation_log.csv files
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--output : output CSV (default: combined_nav_usbl.csv)
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Nav log format: timestamp,data,value (long format)
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data=Lat → latitude_deg
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data=Lon → longitude_deg
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data=Heading → heading_deg
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Interpolation: for each USBL timestamp, find nearest nav point within 1s window.
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If multiple sessions, use session matching by timestamp overlap.
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AUV position calculation:
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azimuth_deg from USBL is RELATIVE to USV heading (yaw) based on USBL hardware mounting.
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AUV bearing from North = (USV heading + azimuth_deg) mod 360
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Horizontal dist = dist_m * cos(elevation_deg * pi/180)
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Note: if azimuth is already absolute (referenced to North), do NOT add heading.
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Check: usbl_yaw in payload should match nav Heading if relative azimuth.
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We use RELATIVE convention (add USV heading) — documented here.
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Geodetic forward (haversine):
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Using flat-earth approximation valid for distances < 500m:
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dlat = horiz_dist * cos(bearing) / R_earth
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dlon = horiz_dist * sin(bearing) / (R_earth * cos(lat))
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R_earth = 6371000 m
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"""
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import csv
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import io
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import sys
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import math
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import os
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R_EARTH = 6371000.0 # meters
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def parse_nav_log(nav_file):
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"""
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Parse navigation_log.csv into:
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- nav_points: sorted list of (timestamp_str, lat, lon) from Lat+Lon entries
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- heading_series: sorted list of (timestamp_str, heading_deg) from Heading entries
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Lat/Lon and Heading have different timestamps so must be interpolated separately.
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Returns (nav_points, heading_series).
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"""
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lat_by_ts = {}
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lon_by_ts = {}
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heading_series_raw = []
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with open(nav_file) as f:
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reader = csv.DictReader(f)
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for row in reader:
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data = row.get('data', '')
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ts = row.get('timestamp', '')
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try:
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val_raw = row.get('value', '') or ''
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if not val_raw:
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continue
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val = float(val_raw)
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except (ValueError, TypeError):
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continue
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if data == 'Lat':
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lat_by_ts[ts] = val
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elif data == 'Lon':
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lon_by_ts[ts] = val
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elif data == 'Heading':
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heading_series_raw.append((ts, val))
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# Build nav_points: timestamps where both Lat and Lon appear (same ts)
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nav_points = []
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for ts in sorted(set(lat_by_ts.keys()) & set(lon_by_ts.keys())):
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nav_points.append((ts, lat_by_ts[ts], lon_by_ts[ts]))
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# heading_series sorted by timestamp
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heading_series = sorted(heading_series_raw, key=lambda x: x[0])
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return nav_points, heading_series
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def ts_to_seconds(ts_str):
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"""Convert '2026-03-24 09:29:05.230392' to float seconds since epoch (approx)."""
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# Simple: parse date+time, compute offset
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try:
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date_part, time_part = ts_str.strip().split(' ', 1)
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y, mo, d = date_part.split('-')
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parts = time_part.split(':')
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h, m = int(parts[0]), int(parts[1])
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s_str = parts[2]
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s = float(s_str)
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# Days since fixed epoch (don't need absolute, just relative diffs)
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total = (int(y)*365 + int(mo)*30 + int(d)) * 86400 + h*3600 + m*60 + s
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return total
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except Exception:
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return 0.0
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def find_nearest(ts_sec, series_sec, series, max_gap=1.0):
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"""Find nearest entry in sorted series within max_gap seconds."""
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best_idx = -1
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best_dt = float('inf')
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for i, (s_sec, entry) in enumerate(zip(series_sec, series)):
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dt = abs(s_sec - ts_sec)
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if dt < best_dt:
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best_dt = dt
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best_idx = i
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elif s_sec > ts_sec + max_gap:
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break
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if best_idx >= 0 and best_dt <= max_gap:
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return series[best_idx], best_dt
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return None, None
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def geodetic_forward(lat_deg, lon_deg, bearing_deg, dist_m):
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"""
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Compute destination point given start lat/lon, bearing (deg from North), distance (m).
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Flat-earth approximation valid for dist < 500m.
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"""
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bearing_rad = math.radians(bearing_deg)
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lat_rad = math.radians(lat_deg)
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dlat = dist_m * math.cos(bearing_rad) / R_EARTH
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dlon = dist_m * math.sin(bearing_rad) / (R_EARTH * math.cos(lat_rad))
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auv_lat = lat_deg + math.degrees(dlat)
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auv_lon = lon_deg + math.degrees(dlon)
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return auv_lat, auv_lon
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def haversine_dist(lat1, lon1, lat2, lon2):
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"""Distance in meters between two lat/lon points."""
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phi1, phi2 = math.radians(lat1), math.radians(lat2)
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dphi = math.radians(lat2 - lat1)
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dlam = math.radians(lon2 - lon1)
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a = math.sin(dphi/2)**2 + math.cos(phi1)*math.cos(phi2)*math.sin(dlam/2)**2
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return 2 * R_EARTH * math.asin(math.sqrt(a))
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def find_nav_file_for_session(usbl_file, nav_dir):
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"""Match nav file by common timestamp prefix."""
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base = os.path.basename(usbl_file)
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# e.g. 2026-03-24_09-28-44_USV003_usbl.csv -> 2026-03-24_09-28-44_USV003
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prefix = base.replace('_usbl.csv', '')
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nav_candidate = os.path.join(nav_dir, prefix + '_navigation_log.csv')
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if os.path.exists(nav_candidate):
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return nav_candidate
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return None
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--usbl', required=True, help='combined_usbl.csv')
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parser.add_argument('--nav-dir', required=True, help='Directory with *_navigation_log.csv')
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parser.add_argument('--output', default='combined_nav_usbl.csv')
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parser.add_argument('--max-gap', type=float, default=1.0, help='Max timestamp gap in seconds')
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args = parser.parse_args()
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# Load USBL records
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usbl_records = []
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with open(args.usbl) as f:
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reader = csv.DictReader(f)
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for row in reader:
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usbl_records.append(row)
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print("USBL records loaded: %d" % len(usbl_records))
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# Group by source_file
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from collections import defaultdict
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by_source = defaultdict(list)
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for rec in usbl_records:
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by_source[rec.get('source_file', '')].append(rec)
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# Load nav files
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nav_data = {} # source_file -> (nav_points, nav_points_sec, heading_series, heading_sec)
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nav_dir = args.nav_dir
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for source_file in by_source.keys():
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# Try to match nav file
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prefix = source_file.replace('_usbl.csv', '')
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nav_file = os.path.join(nav_dir, prefix + '_navigation_log.csv')
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if not os.path.exists(nav_file):
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# Try to find by scanning directory
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matched = None
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for fn in os.listdir(nav_dir):
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if prefix in fn and 'navigation_log' in fn:
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matched = os.path.join(nav_dir, fn)
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break
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if matched is None:
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print("WARNING: no nav file found for %s" % source_file)
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nav_data[source_file] = ([], [], [], [])
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continue
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nav_file = matched
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nav_points, heading_series = parse_nav_log(nav_file)
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nav_points_sec = [ts_to_seconds(pt[0]) for pt in nav_points]
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heading_sec = [ts_to_seconds(h[0]) for h in heading_series]
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nav_data[source_file] = (nav_points, nav_points_sec, heading_series, heading_sec)
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print("Nav loaded for %s: %d pos points, %d heading points" % (
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source_file, len(nav_points), len(heading_series)))
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# Process and write output
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output_rows = []
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stats_match = 0
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stats_nomatch = 0
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for source_file, records in by_source.items():
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nav_points, nav_points_sec, heading_series, heading_sec = nav_data.get(
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source_file, ([], [], [], []))
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for rec in records:
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ts_str = rec.get('Timestamp', '')
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ts_sec = ts_to_seconds(ts_str)
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dist_str = rec.get('Dist', '')
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azimuth_str = rec.get('Azimuth', '')
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elev_str = rec.get('Elev', '')
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snr_str = rec.get('SNR', '')
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try:
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dist = float(dist_str) if dist_str else float('nan')
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azimuth = float(azimuth_str) if azimuth_str else float('nan')
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elev = float(elev_str) if elev_str else float('nan')
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snr = float(snr_str) if snr_str else float('nan')
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except ValueError:
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dist, azimuth, elev, snr = float('nan'), float('nan'), float('nan'), float('nan')
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nav_pt, dt = find_nearest(ts_sec, nav_points_sec, nav_points, args.max_gap)
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hdg_pt, _ = find_nearest(ts_sec, heading_sec, heading_series, args.max_gap)
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if nav_pt is None:
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stats_nomatch += 1
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lat_usv, lon_usv, heading_usv = float('nan'), float('nan'), float('nan')
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auv_lat, auv_lon = float('nan'), float('nan')
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else:
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stats_match += 1
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_, lat_usv, lon_usv = nav_pt
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heading_usv = hdg_pt[1] if hdg_pt is not None else float('nan')
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# Calculate AUV absolute position
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# Azimuth from USBL is relative to USV heading (yaw convention)
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# AUV bearing from North = (USV Heading + azimuth_deg) mod 360
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if not (math.isnan(dist) or math.isnan(azimuth) or
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math.isnan(lat_usv) or math.isnan(heading_usv)):
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horiz_dist = dist * math.cos(math.radians(elev)) if not math.isnan(elev) else dist
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abs_bearing = (heading_usv + azimuth) % 360
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auv_lat, auv_lon = geodetic_forward(lat_usv, lon_usv, abs_bearing, horiz_dist)
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else:
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auv_lat, auv_lon = float('nan'), float('nan')
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output_rows.append({
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'Timestamp': ts_str,
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'lat': '%.7f' % lat_usv if not math.isnan(lat_usv) else '',
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'lon': '%.7f' % lon_usv if not math.isnan(lon_usv) else '',
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'Heading': '%.2f' % heading_usv if not math.isnan(heading_usv) else '',
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'Dist': '%.4f' % dist if not math.isnan(dist) else '',
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'Azimuth': '%.4f' % azimuth if not math.isnan(azimuth) else '',
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'Elev': '%.4f' % elev if not math.isnan(elev) else '',
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'SNR': '%.4f' % snr if not math.isnan(snr) else '',
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'auv_lat': '%.7f' % auv_lat if not math.isnan(auv_lat) else '',
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'auv_lon': '%.7f' % auv_lon if not math.isnan(auv_lon) else '',
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'nav_dt_s': '%.3f' % dt if dt is not None else '',
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})
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print("\n=== Merge stats ===")
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print("Matched: %d No nav match: %d" % (stats_match, stats_nomatch))
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with open(args.output, 'w', newline='') as f:
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writer = csv.writer(f)
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writer.writerow(['Timestamp', 'lat', 'lon', 'Heading', 'Dist', 'Azimuth', 'Elev', 'SNR',
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'auv_lat', 'auv_lon', 'nav_dt_s'])
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for row in output_rows:
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writer.writerow([
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row['Timestamp'], row['lat'], row['lon'], row['Heading'],
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row['Dist'], row['Azimuth'], row['Elev'], row['SNR'],
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row['auv_lat'], row['auv_lon'], row['nav_dt_s']
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])
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print("Output: %s (%d rows)" % (args.output, len(output_rows)))
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# Sample output
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if output_rows:
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print("\n=== Sample (first 5 rows) ===")
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print("Timestamp,lat,lon,Heading,Dist,Azimuth,Elev,SNR,auv_lat,auv_lon")
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for row in output_rows[:5]:
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print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % (
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row['Timestamp'], row['lat'], row['lon'], row['Heading'],
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row['Dist'], row['Azimuth'], row['Elev'], row['SNR'],
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row['auv_lat'], row['auv_lon']))
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# AUV position stats
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auv_lats = [float(r['auv_lat']) for r in output_rows if r['auv_lat']]
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auv_lons = [float(r['auv_lon']) for r in output_rows if r['auv_lon']]
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usv_lats = [float(r['lat']) for r in output_rows if r['lat']]
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usv_lons = [float(r['lon']) for r in output_rows if r['lon']]
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if auv_lats and usv_lats:
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print("\n=== Position stats ===")
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print("AUV lat range: %.6f - %.6f" % (min(auv_lats), max(auv_lats)))
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print("AUV lon range: %.6f - %.6f" % (min(auv_lons), max(auv_lons)))
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print("USV lat range: %.6f - %.6f" % (min(usv_lats), max(usv_lats)))
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# Average USV-AUV distance
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dists = []
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for r in output_rows:
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if r['lat'] and r['auv_lat']:
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d = haversine_dist(float(r['lat']), float(r['lon']),
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float(r['auv_lat']), float(r['auv_lon']))
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dists.append(d)
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if dists:
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print("Avg USV-AUV dist: %.2f m (min=%.2f max=%.2f)" % (
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sum(dists)/len(dists), min(dists), max(dists)))
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if __name__ == '__main__':
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main()
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