resume ptemcee sampler

ptsampler.py

# Define the sampler
ptsampler = sampler.Sampler()

available_time = # some number of hours (seconds)
pos0 = # some initial random positions

# Timing params
elapsed, start_t = 0, t.time()

# Fixed number of steps
steps = 50

while True:

    # Run sampler for 'steps' iterations
    for (pos, lnprob, lnlike) in ptsampler.sample(
            pos0, iterations=steps, adapt=True):
        pass

    # Update position
    pos0 = ptsampler._p0
    # Update also the betas?

    # Catch and remove nans and infs (this shouldn't happen)
    if np.any(np.isinf(pos0)):
        print(runs, "infs")
        pos0[np.isinf(pos0)] = 0.
    if np.any(np.isnan(pos0)):
        print(runs, "nans")
        pos0[np.isnan(pos0)] = 0.

    elapsed += t.time() - start_t
    if elapsed_time > available_time:
        break