Davide Scaini
14a4a0b994
Layout: map + charts stacked left, stats panel (2-col) on the right. Cadence moved to last stat. Charts sit directly below the map. Speed coloring: most FIT files don't record per-second speed, leaving timeseries speed_kmh all-null and the hover link dead. Fix: derive speed from consecutive GPS coordinates (haversine + 5-pt moving average) when the device didn't record it. Add --backfill-speed render flag to retrofit existing timeseries files.
99 lines
3.6 KiB
Python
99 lines
3.6 KiB
Python
"""Downsample a list of DataPoints to at most 1 sample/second and build
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the BAS timeseries object (parallel arrays)."""
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from datetime import datetime
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from math import atan2, cos, radians, sin, sqrt
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from typing import Optional
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from bincio.extract.models import DataPoint
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def _gps_speed_kmh(
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lat_vals: list[Optional[float]],
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lon_vals: list[Optional[float]],
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ts_vals: list[int],
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) -> list[Optional[float]]:
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"""Compute speed (km/h) from consecutive GPS coordinates via haversine.
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Applies a 5-point centred moving-average to reduce GPS noise.
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"""
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n = len(ts_vals)
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raw: list[Optional[float]] = [None] * n
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for i in range(1, n):
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la0, lo0 = lat_vals[i - 1], lon_vals[i - 1]
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la1, lo1 = lat_vals[i], lon_vals[i]
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dt = ts_vals[i] - ts_vals[i - 1]
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if la0 is None or lo0 is None or la1 is None or lo1 is None or dt <= 0:
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continue
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dlat = radians(la1 - la0)
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dlon = radians(lo1 - lo0)
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a = sin(dlat / 2) ** 2 + cos(radians(la0)) * cos(radians(la1)) * sin(dlon / 2) ** 2
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d_km = 2 * 6371.0 * atan2(sqrt(a), sqrt(1 - a))
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raw[i] = d_km / dt * 3600.0
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# 5-point centred moving average (skip None anchors)
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half = 2
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smoothed: list[Optional[float]] = [None] * n
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for i in range(n):
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vals = [raw[j] for j in range(max(0, i - half), min(n, i + half + 1)) if raw[j] is not None]
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if vals:
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smoothed[i] = round(sum(vals) / len(vals), 2)
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return smoothed
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def build_timeseries(
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points: list[DataPoint],
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started_at: datetime,
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privacy: str = "public",
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) -> dict:
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"""Return the BAS `timeseries` object.
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privacy='no_gps' → lat/lon set to null. All other privacy levels
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(including 'unlisted') retain GPS in the timeseries.
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Downsamples so at most one point per second is emitted.
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"""
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if not points:
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return {"t": []}
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include_gps = privacy not in ("no_gps", "private") # "private" = legacy alias for "unlisted"
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# Downsample: keep at most one point per second
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sampled: list[DataPoint] = []
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last_t: Optional[int] = None
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for p in points:
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t = int((p.timestamp - started_at).total_seconds())
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if t < 0:
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continue
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if last_t is not None and t <= last_t:
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continue # skip sub-second duplicates and non-monotonic points
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sampled.append(p)
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last_t = t
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ts_vals = [int((p.timestamp - started_at).total_seconds()) for p in sampled]
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lat_vals = [round(p.lat, 7) if p.lat is not None else None for p in sampled] if include_gps else None
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lon_vals = [round(p.lon, 7) if p.lon is not None else None for p in sampled] if include_gps else None
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ele_vals = [round(p.elevation_m, 1) if p.elevation_m is not None else None for p in sampled]
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spd_vals = [round(p.speed_kmh, 2) if p.speed_kmh is not None else None for p in sampled]
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# Derive speed from GPS when the device didn't record per-second speed.
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if include_gps and lat_vals and lon_vals and all(v is None for v in spd_vals):
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spd_vals = _gps_speed_kmh(lat_vals, lon_vals, ts_vals)
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hr_vals = [p.hr_bpm for p in sampled]
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cad_vals = [p.cadence_rpm for p in sampled]
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pwr_vals = [p.power_w for p in sampled]
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tmp_vals = [round(p.temperature_c, 1) if p.temperature_c is not None else None for p in sampled]
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result: dict = {
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"t": ts_vals,
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"lat": lat_vals,
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"lon": lon_vals,
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"elevation_m": ele_vals,
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"speed_kmh": spd_vals,
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"hr_bpm": hr_vals,
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"cadence_rpm": cad_vals,
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"power_w": pwr_vals,
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"temperature_c": tmp_vals,
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}
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return result
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