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some connection with the lisp server

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2025-05-07 20:50:44 +02:00
parent ec4822be39
commit 237e52f9ab

112
rowers/fitness.py Normal file
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import polars as pl
import datetime
import arrow
import numpy as np
from rowers.dataprep import rscore_approx
from rowers.models import Rower, Workout
def weightfromrecord(row,metricchoice):
vv = row[metricchoice][0]
if vv > 0:
return vv
if metricchoice == 'rscore': # pragma: no cover
return rscore_approx(row)
return 0 # pragma: no cover
def getfatigues(df, fatigues, fitnesses, dates, impulses, startdate, kfatigue, kfitness):
fatigue = 0
fitness = 0
impulses = []
for f in fatigues:
impulses.append(0)
lambda_a = 2/(kfatigue+1)
lambda_c = 2/(kfitness+1)
enddate = datetime.date.today()
nrdays = (enddate - startdate).days
for i in range(nrdays+1):
date = startdate + datetime.timedelta(days=i)
datekey = date.strftime('%Y-%m-%d')
weight = 0
try:
df2 = df.filter(pl.col('date') == date)
if type(df2) == pl.Series:
weight += weightfromrecord(df2, 'rscore')
else:
for row in df2.iter_slices(n_rows=1):
weight += weightfromrecord(row, 'rscore')
except KeyError:
pass
impulses.append(weight)
fatigue = (1 - lambda_a) * fatigue + lambda_a * weight
fitness = (1 - lambda_c) * fitness + lambda_c * weight
fatigues.append(fatigue)
fitnesses.append(fitness)
dates.append(arrow.get(date).datetime)
return fatigues, fitnesses, dates, impulses
def calculate_fitness(rower):
workouts = Workout.objects.filter(user=rower, date__gte=datetime.date.today() - datetime.timedelta(days=90),
duplicate=False).order_by('date')
kfitness = rower.kfit
kfatigue = rower.kfatigue
records = []
for w in workouts:
dd = {
'date': w.date,
'trimp': w.trimp,
'rscore': w.rscore,
'hrtss':w.hrtss,
'duration': w.duration,
'id': w.id,
'rpe': w.rpe,
}
records.append(dd)
df = pl.from_records(records)
if df.is_empty():
return 0, 0
dates = [arrow.get(w.date).datetime for w in workouts]
impulses = [np.nan for w in workouts]
fitnesses = [np.nan for w in workouts]
fatigues = [np.nan for w in workouts]
fatigues, fitnesses, dates, impulses = getfatigues(
df,
fatigues,
fitnesses,
dates,
impulses,
datetime.date.today()-datetime.timedelta(days=90),
kfatigue, kfitness
)
df = pl.DataFrame({
'date': dates,
'fatigue': fatigues,
'fitness': fitnesses,
'impulse': impulses,
})
endfitness = fitnesses[-2]
endfatigue = fatigues[-2]
return endfitness, endfatigue