def ap_quniform_sampler(obs, prior_weight, low, high, q, size=(), rng=None):
    prior_mu = 0.5 * (high + low)
    prior_sigma = 1.0 * (high - low)
    weights, mus, sigmas = scope.adaptive_parzen_normal(obs,
            prior_weight, prior_mu, prior_sigma)
    return scope.GMM1(weights, mus, sigmas, low=low, high=high, q=q,
            size=size, rng=rng)

def ap_qloguniform_sampler(obs, prior_weight, low, high, q, size=(), rng=None):
    prior_mu = 0.5 * (high + low)
    prior_sigma = 1.0 * (high - low)
    weights, mus, sigmas = scope.adaptive_parzen_normal(
        scope.log(
            # -- map observations that were quantized to be below exp(low)
            #    (particularly 0) back up to exp(low) where they will
            #    interact in a reasonable way with the AdaptiveParzen
            #    thing.
            scope.maximum(obs, scope.maximum(EPS, scope.exp(low)))  # -- protect against exp(low) underflow
        ),
        prior_weight,
        prior_mu,
        prior_sigma,
    )
    return scope.LGMM1(weights, mus, sigmas, low, high, q=q, size=size, rng=rng)

def ap_qlognormal_sampler(obs, prior_weight, mu, sigma, q, size=(), rng=None):
    log_obs = scope.log(scope.maximum(obs, EPS))
    weights, mus, sigmas = scope.adaptive_parzen_normal(
            log_obs, prior_weight, mu, sigma)
    rval = scope.LGMM1(weights, mus, sigmas, q=q, size=size, rng=rng)
    return rval

def ap_loglognormal_sampler(obs, prior_weight, mu, sigma, size=(), rng=None):
    weights, mus, sigmas = scope.adaptive_parzen_normal(
            scope.log(obs), prior_weight, mu, sigma)
    rval = scope.LGMM1(weights, mus, sigmas, size=size, rng=rng)
    return rval

def ap_qnormal_sampler(obs, prior_weight, mu, sigma, q, size=(), rng=None):
    weights, mus, sigmas = scope.adaptive_parzen_normal(
            obs, prior_weight, mu, sigma)
    return scope.GMM1(weights, mus, sigmas, q=q, size=size, rng=rng)