# Produce some simulated survival data from a weird hazard function
import numpy
from samplers import HazardSampler
# Set a random seed and sample size
numpy.random.seed(1)
m = 1000
# Use this totally crazy hazard function
hazard = lambda t: numpy.exp(numpy.sin(t) - 2.0)
# Sample failure times from the hazard function
sampler = HazardSampler(hazard)
failure_times = numpy.array([sampler.draw() for _ in range(m)])
# Apply some non-informative right censoring, just to demonstrate how it's done
censor_times = numpy.random.uniform(0.0, 25.0, size=m)
y = numpy.minimum(failure_times, censor_times)
c = 1.0 * (censor_times > failure_times)
# Make some plots of the simulated data
from matplotlib import pyplot
from statsmodels.distributions import ECDF
# Plot a histogram of failure times from this hazard function
pyplot.hist(failure_times, bins=50)
pyplot.title('Uncensored Failure Times')
pyplot.savefig('uncensored_hist.png', transparent=True)
pyplot.show()
# Plot a histogram of censored failure times from this hazard function
