def get_summary_analytics(self):
self.trimp, self.hrss, self.wap, self.tss, self.ri, self.variability_index, self.efficiency_factor = None, None, None, None, None, None, None
activity_length = self.df_samples['time'].max()
# Calculate power metrics
if 'weighttraining' in self.type.lower():
self.tss, self.ri = self.wss_score()
elif self.max_watts is not None and self.ftp is not None:
self.wap = weighted_average_power(
self.df_samples['watts'].to_numpy())
self.ri = relative_intensity(self.wap, self.ftp)
self.tss = stress_score(self.wap, self.ftp, activity_length)
self.variability_index = self.wap / self.df_samples['watts'].mean()
if self.max_heartrate is not None:
# Calculate heartrate metrics
athlete_lthr = .89 * self.athlete_max_hr
self.df_samples['hrr'] = self.df_samples['heartrate'].apply(
lambda x: (x - self.hr_lowest) /
(self.athlete_max_hr - self.hr_lowest))
self.trimp = (
(1 / 60) * self.df_samples['hrr'] *
(0.64 * np.exp(1.92 * self.df_samples['hrr']))).sum()
athlete_hrr_lthr = (athlete_lthr - self.hr_lowest) / (
self.athlete_max_hr - self.hr_lowest)
self.hrss = (self.trimp /
(60 * athlete_hrr_lthr *
(0.64 * np.exp(1.92 * athlete_hrr_lthr)))) * 100
self.df_samples = self.df_samples.drop(columns='hrr')
if self.max_heartrate is not None and self.wap is not None:
self.efficiency_factor = self.wap / self.df_samples[
'heartrate'].mean()
def test_normalized_power_xpower(self):
stream = np.ones(30)
moving = np.ones(30, dtype=bool)
assert weighted_average_power(stream, moving, algorithm="xPower") == 1
def test_wap(self):
stream = np.ones(30)
moving = np.ones(30, dtype=bool)
assert weighted_average_power(stream, moving) == 1
def compute_weighted_average_power(self):
return core.weighted_average_power(self.power, type='WAP')
def compute_weighted_average_power(self):
return core.weighted_average_power(self.power, algorithm='WAP')
