90 lines
2.3 KiB
Python
90 lines
2.3 KiB
Python
'''
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[[https://github.com/jonasoreland/runnerup][Runnerup]] exercise data (TCX format)
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'''
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REQUIRES = [
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'python-tcxparser',
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]
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import NamedTuple, Iterable
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from .core import Res, get_files
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from .core.common import isoparse, Json
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import tcxparser
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from my.config import runnerup as config
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# TODO later, use a proper namedtuple?
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Workout = Json
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def _parse(f: Path) -> Workout:
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tcx = tcxparser.TCXParser(str(f))
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sport = f.stem.split('_')[-1] # todo not sure how reliable...
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hr_avg = tcx.hr_avg
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distance_m = tcx.distance
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duration_s = tcx.duration
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# kmh to match endomondo.. should probably be CI
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speed_avg_kmh = (distance_m / 1000) / (duration_s / 3600)
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# eh. not sure if there is a better way
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# for now use this to be compatible with Endomondo
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# https://beepb00p.xyz/heartbeats_vs_kcals.html
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# filtered for Endomondo running:
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reg_coeff = 0.0993
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intercept = -11.0739
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total_beats = hr_avg * (duration_s / 60)
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kcal_estimate = total_beats * reg_coeff + intercept
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return {
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'id' : f.name, # not sure?
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'start_time' : isoparse(tcx.started_at),
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'duration' : timedelta(seconds=tcx.duration),
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'sport' : sport,
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'heart_rate_avg': tcx.hr_avg,
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'speed_avg' : speed_avg_kmh,
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'kcal' : kcal_estimate,
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}
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# from more_itertools import zip_equal
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# for ts, latlon, hr in zip_equal(
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# tcx.time_values(),
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# tcx.position_values(),
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# tcx.hr_values(),
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# # todo cadence?
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# ):
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# t = isoparse(ts)
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def workouts() -> Iterable[Res[Workout]]:
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for f in get_files(config.export_path):
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try:
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yield _parse(f)
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except Exception as e:
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yield e
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from .core.pandas import DataFrameT, check_dataframe, error_to_row
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@check_dataframe
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def dataframe() -> DataFrameT:
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def it():
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for w in workouts():
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if isinstance(w, Exception):
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yield error_to_row(w)
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else:
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yield w
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import pandas as pd # type: ignore
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df = pd.DataFrame(it())
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if 'error' not in df:
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df['error'] = None
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return df
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from .core import stat, Stats
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def stats() -> Stats:
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return stat(dataframe)
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