Martin v. Löwis <martin <at> v.loewis.de> writes:
> If you want others to help you in finding the bug, you
> need to provide more detail, e.g. a specific piece of
> code that reproducibly wastes memory. If you want to
> study how Python objects are allocated and released,
> you need to create a debug build of Python (and all
> extension modules), and start, e.g., with looking at the
> value of sys.gettotalrefcount() over time.
I tried monitoring the refcount at every iteration, but it
does not change; at the same time, the memory use by the
python process increases. This is why I suspected that
python was not returning memory.
Below is the method that gets called iteratively; the *_like methods
are statistical likelihoods implemented in f2py. I dont see
anything that is obviously responsible:
def model(self):
# Specification of joint log-posterior
#alpha3 = concatenate(([0], self.alpha3))
# Linear model for surfacing wait time
#self.lamda = exp(self.alpha0 + self.alpha1 * self.wind + self.alpha2 *
self.air + alpha3[self.series-1])
gamma3 = concatenate(([0], self.gamma3))
# Linear model for at-surface probability
self.theta = invlogit(self.gamma0 + self.gamma1 * self.wind + self.gamma
2 * self.air + gamma3[self.series-1])
x, n, theta = transpose([[z[1], sum(z), t] for z, t in zip(self.downup,
self.theta) if type(z)!=type(0.0)])
# Binomial likelihood of available animals
self.binomial_like(x, n, theta, name='theta')
# Probability of availability (per survey)
self.pa = 1.0 - (1 - self.theta)**10
beta3 = concatenate(([0], self.beta3))
# Linearmodel for probability of detection
self.pd = invlogit(self.beta0 + self.beta1 * self.wind + self.beta2 * (s
elf.cloud>0) + beta3[self.series-1])
# Binomial likelihood of detection
self.binomial_like(self.obs, self.present, self.pd * self.pa, name='pd')
zeta1 = concatenate(([0], self.zeta1))
# Probability of presence
self.pp = invlogit(self.zeta0 + zeta1[self.series-1] + self.zeta2 * self
.intake + self.zeta3 * (self.discharge - self.intake))
# Binomial likelihood of presence
self.binomial_like(self.present, self.absent + self.present, self.pp, na
me='pp')
# Correct flight counts for detection
self.N_flight = self.count / (self.pd * self.pa)
# Aggregate counts by series
N_series = [self.N_flight[self.series==i+1] for i in range(6)]
# Log-normal likelihood for N
#sum(self.lognormal_like(X, log(N), T) for X, N, T in zip(N_series, self
.N, self.T))
for N, mu in zip(N_series, self.N):
self.poisson_like(N, mu)
Thanks in advance for anything that you might suggest.
cf
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