PDC

Utilities to manipulate power duration curves, fit them and do what-if analysis

The power_curve function will be fitted to its parameters, with reasonable bounds

power_curve

 power_curve (x, frc, ftp, tte, tau, tau2, a)

	Details
x
frc	Functional Reserve Capacity
ftp	Functional Threshold Power
tte	Time to Exhaustion
tau	Short end calibration
tau2	Long end calibration
a	Decay factor past TTE

source

PDC

 PDC (x, y)

A Power Duraction Curve

Load a mean maximal curve, with time in seconds and corresponding watts

df = pd.read_csv("../data/mmpcurve.csv")
pdc = PDC(df['Secs'], df['Watts'])

result = pdc.fit()

result.best_values

{'frc': 10190.691792353007,
 'ftp': 247.97104270072407,
 'tte': 1800.0000561443576,
 'tau': 12.619522854826833,
 'tau2': 25.0,
 'a': 33.22731702525135}

fitted_curve = power_curve(pdc.x, result.best_values['frc'],
                          result.best_values['ftp'],
                          result.best_values['tte'],
                          result.best_values['tau'],
                          result.best_values['tau2'],
                          result.best_values['a'])

plt.figure(figsize=(10,6))
plt.plot(pdc.x, pdc.y, label='MMP')
plt.plot(pdc.x, fitted_curve, label='PDC')
plt.legend()
plt.grid(True)
plt.xscale('log')
plt.show()