lfp Weibull Fit not working

[leester1690]

I tried the following Weibull Fit with example from the User Guide (surpyval-readthedocs-io-en-latest.pdf). I get a completely different output. Model parameters are different. I expect the parameters shown in the User Guide are correct, given that lfp=True in the fit constructor.

Why the discrepancy? Note: I'm using v0.10.1, latest stable release.

Code:
import surpyval as surv
f = [.1, .1, .15, .6, .8, .8, 1.2, 2.5, 3., 4., 4., 6., 10., 10.,
12.5, 20., 20., 43., 43., 48., 48., 54., 74., 84., 94., 168., 263., 593.]
s = [1370.] * 4128
x, c, n = surv.fs_to_xcn(f, s)
model = surv.Weibull.fit(x, c, n, lfp=True)
print(model)
model.plot()

User Guide output:
Parametric SurPyval Model

Distribution : Weibull
Fitted by : MLE
Max Proportion (p) : 0.006744450944727198
Parameters :
alpha: 28.367193779799038
beta: 0.4959762140288241

My Output
Precision was lost, try:

• Using alternate fitting method
• visually checking model fit
• change data to be closer to 1.
  Parametric SurPyval Model
  =========================
  Distribution : Weibull
  Fitted by : MLE
  Max Proportion (p) : 0.982053236083039
  Parameters :
  alpha: 94757062081178.73
  beta: 0.19946444457267584
  94757062081178.73