MileVault/milevault_export/backend/app/services/fit_parser.py

"""
FIT and GPX file parser.
Parses FIT files directly using the Garmin SDK but applies manual
scale conversion for fields where the SDK doesn't auto-convert.
"""
import math
import struct
from datetime import datetime, timezone
from typing import Optional
import gpxpy
import polyline as polyline_lib
from garmin_fit_sdk import Decoder, Stream

FIT_EPOCH_S = 631065600
SEMICIRCLES_TO_DEG = 180.0 / (2 ** 31)


def _semicircles_to_deg(val):
    if val is None:
        return None
    try:
        result = float(val) * SEMICIRCLES_TO_DEG
        if -90 <= result <= 90 or -180 <= result <= 180:
            return result
    except (TypeError, ValueError):
        pass
    return None


def _safe_float(val) -> Optional[float]:
    try:
        return float(val) if val is not None else None
    except (TypeError, ValueError):
        return None


def _sanitize_speed(val, dist_m=None, dur_s=None) -> Optional[float]:
    """Reject the FIT invalid sentinel (0xFFFF/1000 = 65.535 m/s) and fall back to dist/dur."""
    fv = _safe_float(val)
    if fv is None or fv >= 65.0:
        if dist_m and dur_s and float(dur_s) > 0:
            return float(dist_m) / float(dur_s)
        return None
    return fv


def _bounding_box(coords):
    if not coords:
        return None
    lats = [c[0] for c in coords]
    lons = [c[1] for c in coords]
    return {"min_lat": min(lats), "max_lat": max(lats),
            "min_lon": min(lons), "max_lon": max(lons)}


def _to_dt(val) -> Optional[datetime]:
    if val is None:
        return None
    if isinstance(val, datetime):
        return val.replace(tzinfo=timezone.utc) if val.tzinfo is None else val
    if isinstance(val, (int, float)):
        try:
            return datetime.fromtimestamp(int(val) + FIT_EPOCH_S, tz=timezone.utc)
        except (OSError, OverflowError, ValueError):
            return None
    return None


def _is_valid_lat(v):
    return v is not None and -90 <= v <= 90


def _is_valid_lon(v):
    return v is not None and -180 <= v <= 180


def parse_fit_file(filepath: str) -> dict:
    session_data = {}
    records = []
    laps = []

    def listener(mesg_num: int, msg: dict):
        if mesg_num == 18:   # session
            session_data.update(msg)
        elif mesg_num == 20:  # record
            records.append(msg)
        elif mesg_num == 19:  # lap
            laps.append(msg)

    stream = Stream.from_file(filepath)
    decoder = Decoder(stream)
    decoder.read(
        apply_scale_and_offset=True,
        convert_datetimes_to_dates=True,
        convert_types_to_strings=True,
        enable_crc_check=False,
        expand_sub_fields=True,
        expand_components=True,
        merge_heart_rates=True,
        mesg_listener=listener,
    )

    # The SDK may return field names in camelCase or snake_case depending on version.
    # Try both. Also handle raw timestamp integers for start_time.
    def get(d, *keys):
        for k in keys:
            v = d.get(k)
            if v is not None:
                return v
        return None

    sport_raw = str(get(session_data, "sport", "Sport") or "generic").lower()
    sport_map = {
        "running": "running", "cycling": "cycling",
        "hiking": "hiking", "walking": "walking",
        "generic": "other", "trail_running": "running",
        "e_biking": "cycling", "open_water_swimming": "other",
    }
    sport_type = sport_map.get(sport_raw, sport_raw)

    # start_time — SDK may return datetime or raw int
    start_time_raw = get(session_data, "startTime", "start_time")
    start_time = _to_dt(start_time_raw)

    # Position fields — the SDK may or may not convert semicircles.
    # Check if values look like semicircles (>= 90 for lat) and convert if so.
    def get_lat(d):
        v = get(d, "positionLat", "position_lat")
        if v is None:
            return None
        fv = _safe_float(v)
        if fv is None:
            return None
        # If absolute value > 90, it's semicircles
        if abs(fv) > 90:
            fv = fv * SEMICIRCLES_TO_DEG
        return fv if _is_valid_lat(fv) else None

    def get_lon(d):
        v = get(d, "positionLong", "position_long")
        if v is None:
            return None
        fv = _safe_float(v)
        if fv is None:
            return None
        if abs(fv) > 180:
            fv = fv * SEMICIRCLES_TO_DEG
        return fv if _is_valid_lon(fv) else None

    # Build GPS track
    coords = []
    for r in records:
        lat = get_lat(r)
        lon = get_lon(r)
        if lat is not None and lon is not None:
            coords.append((lat, lon))

    encoded_polyline = polyline_lib.encode(coords) if coords else None
    bounding_box = _bounding_box(coords)

    # Normalize data points
    normalized_points = []
    for r in records:
        ts = _to_dt(get(r, "timestamp"))
        lat = get_lat(r)
        lon = get_lon(r)

        altitude = get(r, "altitude", "enhancedAltitude", "enhanced_altitude")
        hr = get(r, "heartRate", "heart_rate")
        cadence = get(r, "cadence")
        speed = get(r, "speed", "enhancedSpeed", "enhanced_speed")
        power = get(r, "power")
        temp = get(r, "temperature")
        distance = get(r, "distance")

        normalized_points.append({
            "timestamp": ts.isoformat() if ts else None,
            "latitude": _safe_float(lat),
            "longitude": _safe_float(lon),
            "altitude_m": _safe_float(altitude),
            "heart_rate": _safe_float(hr),
            "cadence": _safe_float(cadence),
            "speed_ms": _safe_float(speed),
            "power": _safe_float(power),
            "temperature_c": _safe_float(temp),
            "distance_m": _safe_float(distance),
        })

    # Normalize laps
    normalized_laps = []
    for i, lap in enumerate(laps):
        ls = _to_dt(get(lap, "startTime", "start_time"))
        lap_dist = _safe_float(get(lap, "totalDistance", "total_distance"))
        lap_dur = _safe_float(get(lap, "totalElapsedTime", "total_elapsed_time"))
        normalized_laps.append({
            "lap_number": i + 1,
            "start_time": ls.isoformat() if ls else None,
            "duration_s": lap_dur,
            "distance_m": lap_dist,
            "avg_heart_rate": _safe_float(get(lap, "avgHeartRate", "avg_heart_rate")),
            "avg_cadence": _safe_float(get(lap, "avgCadence", "avg_cadence")),
            "avg_speed_ms": _sanitize_speed(
                get(lap, "avgSpeed", "avg_speed", "enhancedAvgSpeed", "enhanced_avg_speed"),
                dist_m=lap_dist, dur_s=lap_dur,
            ),
            "avg_power": _safe_float(get(lap, "avgPower", "avg_power")),
        })

    name = sport_type.title()
    if start_time:
        name += " " + start_time.strftime("%Y-%m-%d")

    return {
        "name": name,
        "sport_type": sport_type,
        "start_time": start_time.isoformat() if start_time else None,
        "distance_m": _safe_float(get(session_data, "totalDistance", "total_distance")),
        "duration_s": _safe_float(get(session_data, "totalElapsedTime", "total_elapsed_time")),
        "elevation_gain_m": _safe_float(get(session_data, "totalAscent", "total_ascent")),
        "elevation_loss_m": _safe_float(get(session_data, "totalDescent", "total_descent")),
        "avg_heart_rate": _safe_float(get(session_data, "avgHeartRate", "avg_heart_rate")),
        "max_heart_rate": _safe_float(get(session_data, "maxHeartRate", "max_heart_rate")),
        "avg_cadence": _safe_float(get(session_data, "avgCadence", "avg_cadence")),
        "avg_power": _safe_float(get(session_data, "avgPower", "avg_power")),
        "normalized_power": _safe_float(get(session_data, "normalizedPower", "normalized_power")),
        "avg_speed_ms": _sanitize_speed(
            get(session_data, "avgSpeed", "avg_speed", "enhancedAvgSpeed", "enhanced_avg_speed"),
            dist_m=_safe_float(get(session_data, "totalDistance", "total_distance")),
            dur_s=_safe_float(get(session_data, "totalElapsedTime", "total_elapsed_time")),
        ),
        "max_speed_ms": _safe_float(get(session_data, "maxSpeed", "max_speed",
                                        "enhancedMaxSpeed", "enhanced_max_speed")),
        "avg_temperature_c": _safe_float(get(session_data, "avgTemperature", "avg_temperature")),
        "calories": _safe_float(get(session_data, "totalCalories", "total_calories")),
        "training_stress_score": _safe_float(get(session_data, "trainingStressScore",
                                                  "training_stress_score")),
        "vo2max_estimate": _safe_float(get(session_data, "totalTrainingEffect",
                                           "total_training_effect")),
        "polyline": encoded_polyline,
        "bounding_box": bounding_box,
        "source_type": "fit",
        "data_points": normalized_points,
        "laps": normalized_laps,
    }


def parse_gpx_file(filepath: str) -> dict:
    with open(filepath) as f:
        gpx = gpxpy.parse(f)

    data_points = []
    track = gpx.tracks[0] if gpx.tracks else None
    if not track:
        raise ValueError("No tracks found in GPX file")

    for segment in track.segments:
        for pt in segment.points:
            ts = pt.time
            if ts and ts.tzinfo is None:
                ts = ts.replace(tzinfo=timezone.utc)
            extensions = {}
            if pt.extensions:
                for ext in pt.extensions:
                    for child in ext:
                        tag = child.tag.split("}")[-1] if "}" in child.tag else child.tag
                        try:
                            extensions[tag] = float(child.text)
                        except (ValueError, TypeError):
                            pass
            data_points.append({
                "timestamp": ts.isoformat() if ts else None,
                "latitude": pt.latitude, "longitude": pt.longitude,
                "altitude_m": pt.elevation,
                "heart_rate": extensions.get("hr"),
                "cadence": extensions.get("cad"),
                "speed_ms": extensions.get("speed"),
                "power": extensions.get("power"),
                "temperature_c": extensions.get("temp") or extensions.get("atemp"),
                "distance_m": None,
            })

    coords = [(p["latitude"], p["longitude"]) for p in data_points if p["latitude"] and p["longitude"]]
    encoded_polyline = polyline_lib.encode(coords) if coords else None
    bounding_box = _bounding_box(coords)

    total_dist = 0.0
    prev = None
    for p in data_points:
        if p["latitude"] and p["longitude"]:
            if prev:
                R = 6371000
                phi1, phi2 = math.radians(prev[0]), math.radians(p["latitude"])
                dphi = math.radians(p["latitude"] - prev[0])
                dlam = math.radians(p["longitude"] - prev[1])
                a = math.sin(dphi/2)**2 + math.cos(phi1)*math.cos(phi2)*math.sin(dlam/2)**2
                total_dist += 2 * R * math.asin(math.sqrt(a))
            prev = (p["latitude"], p["longitude"])
        p["distance_m"] = total_dist

    uphill, downhill = 0.0, 0.0
    alts = [p["altitude_m"] for p in data_points if p["altitude_m"]]
    for i in range(1, len(alts)):
        diff = alts[i] - alts[i-1]
        if diff > 0: uphill += diff
        else: downhill += abs(diff)

    hrs = [p["heart_rate"] for p in data_points if p["heart_rate"]]
    start_time_str = data_points[0]["timestamp"] if data_points else None
    start_dt = datetime.fromisoformat(start_time_str) if start_time_str else None
    end_dt = datetime.fromisoformat(data_points[-1]["timestamp"]) if data_points else None
    duration = (end_dt - start_dt).total_seconds() if (start_dt and end_dt) else None
    sport = track.type.lower() if track.type else "running"

    return {
        "name": track.name or gpx.name or f"Activity {start_dt.date() if start_dt else ''}",
        "sport_type": sport, "start_time": start_time_str,
        "distance_m": total_dist, "duration_s": duration,
        "elevation_gain_m": uphill, "elevation_loss_m": downhill,
        "avg_heart_rate": (sum(hrs) / len(hrs)) if hrs else None,
        "max_heart_rate": max(hrs) if hrs else None,
        "avg_cadence": None, "avg_power": None, "normalized_power": None,
        "avg_speed_ms": (total_dist / duration) if (total_dist and duration) else None,
        "max_speed_ms": None, "avg_temperature_c": None, "calories": None,
        "training_stress_score": None, "vo2max_estimate": None,
        "polyline": encoded_polyline, "bounding_box": bounding_box,
        "source_type": "gpx", "data_points": data_points, "laps": [],
    }


def calculate_hr_zones(data_points: list, user_max_hr: float) -> dict:
    if not user_max_hr or user_max_hr < 100:
        return {}
    zone_bounds = [0.0, 0.60, 0.70, 0.80, 0.90, 1.01]
    zone_keys = ["z1", "z2", "z3", "z4", "z5"]
    zones = {k: 0 for k in zone_keys}
    total = 0
    for p in data_points:
        hr = p.get("heart_rate")
        if not hr or hr < 20:
            continue
        pct = hr / user_max_hr
        total += 1
        for i, key in enumerate(zone_keys):
            if zone_bounds[i] <= pct < zone_bounds[i+1]:
                zones[key] += 1
                break
        else:
            zones["z5"] += 1
    if total:
        return {k: round(v / total * 100, 1) for k, v in zones.items()}
    return {}