def generate_frame_time(self):
is_nonzero_frame_time = False
while not is_nonzero_frame_time:
#planck gives us discrete exponential values
R = planck.rvs(self.exponential_mean, size=1)
if R[0] > 0:
frame_time = R[0]
is_nonzero_frame_time = True
return frame_time
def wait(self):
mean = 0.0025
R = planck.rvs(mean, size=1)
retry_time = R[0]
yield self.env.timeout(retry_time)
mean, var, skew, kurt = planck.stats(lambda_, moments='mvsk')
# Display the probability mass function (``pmf``):
x = np.arange(planck.ppf(0.01, lambda_),
planck.ppf(0.99, lambda_))
ax.plot(x, planck.pmf(x, lambda_), 'bo', ms=8, label='planck pmf')
ax.vlines(x, 0, planck.pmf(x, lambda_), colors='b', lw=5, alpha=0.5)
# Alternatively, the distribution object can be called (as a function)
# to fix the shape and location. This returns a "frozen" RV object holding
# the given parameters fixed.
# Freeze the distribution and display the frozen ``pmf``:
rv = planck(lambda_)
ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1,
label='frozen pmf')
ax.legend(loc='best', frameon=False)
plt.show()
# Check accuracy of ``cdf`` and ``ppf``:
prob = planck.cdf(x, lambda_)
np.allclose(x, planck.ppf(prob, lambda_))
# True
# Generate random numbers:
r = planck.rvs(lambda_, size=1000)