From 0571ed829233ad438442e500266e58dd06928886 Mon Sep 17 00:00:00 2001
From: Dan Waxman <dan.waxman1@gmail.com>
Date: Sun, 1 Mar 2026 22:38:18 -0500
Subject: [PATCH 1/5] Add PFJax; restrict Python version

Needs to be this way until cd-dynamax allows for higher jax versions
---
 pyproject.toml |   5 +-
 uv.lock        | 559 +++++--------------------------------------------
 2 files changed, 52 insertions(+), 512 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 486fb73e..2327778e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ name = "dynestyx"
 version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
-requires-python = ">=3.12"
+requires-python = ">=3.12,<3.14"
 dependencies = [
     "cd-dynamax",
     "effectful",
@@ -24,6 +24,7 @@ dependencies = [
     "cuthbert>=0.0.2",
     "jax>=0.6.2",
     "jaxlib>=0.6.2",
+    "pfjax",
 ]
 
 [tool.hatch.build.targets.wheel]
@@ -33,6 +34,7 @@ packages = ["dynestyx"]
 effectful = { git = "https://github.com/BasisResearch/effectful.git" }
 cuthbert = { git = "https://github.com/DanWaxman/cuthbert.git", rev = "dw-fix-install" }
 cd-dynamax = { git = "https://github.com/DanWaxman/cd_dynamax.git", rev = "dw-dpf" }
+pfjax = { git = "https://github.com/DanWaxman/pfjax.git", rev = "dw-sg-dpf" }
 
 [dependency-groups]
 dev = [
@@ -45,6 +47,7 @@ dev = [
     "mkdocs-jupyter>=0.25.1",
     "seaborn>=0.13.2",
     "imageio>=2.37.2",
+    "jax-tqdm>=0.4.0",
 ]
 
 [tool.ruff.lint]
diff --git a/uv.lock b/uv.lock
index a2d47e3d..43708394 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,13 +1,10 @@
 version = 1
 revision = 2
-requires-python = ">=3.12"
+requires-python = ">=3.12, <3.14"
 resolution-markers = [
-    "python_full_version >= '3.14' and sys_platform == 'win32'",
-    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version == '3.13.*' and sys_platform == 'win32'",
-    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
-    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
     "python_full_version < '3.13' and sys_platform == 'win32'",
     "python_full_version < '3.13' and sys_platform == 'emscripten'",
     "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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From c2006a1e9a27357fae16876898e00153062efd7a Mon Sep 17 00:00:00 2001
From: Dan Waxman <dan.waxman1@gmail.com>
Date: Sun, 1 Mar 2026 22:50:16 -0500
Subject: [PATCH 2/5] Add PFJax Particle Filter Support

This adds a particle filter and marginal particle filter backend for PFJax. It includes the filter configs, glue code, and unittesting.

Lots of this code is AI-generated; I've looked over & used to first approximation, but it requires a closer inspection (and likely some pairing down) before seriously merging.
---
 dynestyx/inference/filter_configs.py          |  24 +-
 dynestyx/inference/filters.py                 |  21 +-
 .../inference/integrations/pfjax/__init__.py  |   3 +
 .../inference/integrations/pfjax/discrete.py  | 397 ++++++++++++++++++
 tests/test_pfjax_integration.py               | 118 ++++++
 5 files changed, 559 insertions(+), 4 deletions(-)
 create mode 100644 dynestyx/inference/integrations/pfjax/__init__.py
 create mode 100644 dynestyx/inference/integrations/pfjax/discrete.py
 create mode 100644 tests/test_pfjax_integration.py

diff --git a/dynestyx/inference/filter_configs.py b/dynestyx/inference/filter_configs.py
index 6d793efb..d963db0e 100644
--- a/dynestyx/inference/filter_configs.py
+++ b/dynestyx/inference/filter_configs.py
@@ -9,7 +9,7 @@
 
 ResamplingBaseMethod = Literal["systematic", "multinomial", "stratified"]
 ResamplingDifferentiableMethod = Literal["stop_gradient", "straight_through", "soft"]
-FilterSource = Literal["cuthbert", "cd_dynamax", "dynestyx"]
+FilterSource = Literal["cuthbert", "cd_dynamax", "dynestyx", "pfjax"]
 FilterEmissionOrder = Literal["zeroth", "first", "second"]
 FilterStateOrder = Literal["zeroth", "first", "second"]
 
@@ -226,7 +226,7 @@ class PFConfig(BaseFilterConfig):
         ess_threshold_ratio (float): Resampling fires when the effective
             sample size drops below `ess_threshold_ratio * n_particles`.
             `1.0` → always resample; `0.0` → never. Defaults to `0.7`.
-        filter_source (FilterSource): Backend. Defaults to `"cuthbert"`, which is currently the only available implementation.
+        filter_source (FilterSource): Backend. Defaults to `"cuthbert"`.
 
     ??? note "Algorithm Reference"
         At each step, particles are propagated through the transition and
@@ -264,6 +264,25 @@ class PFConfig(BaseFilterConfig):
     filter_source: FilterSource = "cuthbert"
 
 
+@dataclasses.dataclass
+class MarginalPFConfig(PFConfig):
+    r"""Marginal Particle Filter (MPF / Rao-Blackwellized PF) for discrete-time models.
+
+    This configuration targets the PFJax marginal particle filter (`particle_filter_rb`),
+    which computes particle weights via mixture-based corrections and can reduce variance
+    relative to standard bootstrap PF in some settings.
+
+    Attributes:
+        stop_proposal_gradient (bool): Whether to stop gradients through proposal
+            sampling and proposal log-density terms (PFJax `stop_proposal_gradient`).
+            Defaults to `True`.
+        filter_source (FilterSource): Backend. Defaults to `"pfjax"`.
+    """
+
+    stop_proposal_gradient: bool = True
+    filter_source: FilterSource = "pfjax"
+
+
 @dataclasses.dataclass
 class EKFConfig(BaseFilterConfig):
     """Extended Kalman Filter (EKF) for discrete-time models.
@@ -602,6 +621,7 @@ class ContinuousTimeUKFConfig(UKFConfig, ContinuousTimeConfig):
 DiscreteTimeConfigs: tuple[type, ...] = (
     EnKFConfig,
     PFConfig,
+    MarginalPFConfig,
     EKFConfig,
     KFConfig,
     UKFConfig,
diff --git a/dynestyx/inference/filters.py b/dynestyx/inference/filters.py
index 3ad58172..e4f52ad8 100644
--- a/dynestyx/inference/filters.py
+++ b/dynestyx/inference/filters.py
@@ -21,6 +21,7 @@
     HMMConfig,
     HMMConfigs,
     KFConfig,
+    MarginalPFConfig,
     PFConfig,
     PFResamplingConfig,
     UKFConfig,
@@ -33,6 +34,9 @@
 from dynestyx.inference.integrations.cuthbert.discrete import (
     run_discrete_filter as run_cuthbert_discrete,
 )
+from dynestyx.inference.integrations.pfjax.discrete import (
+    run_discrete_filter as run_pfjax_discrete,
+)
 from dynestyx.models import DynamicalModel
 from dynestyx.types import FunctionOfTime
 
@@ -250,10 +254,10 @@ def _filter_discrete_time(
     ctrl_values=None,
     **kwargs,
 ) -> None:
-    """Discrete-time marginal likelihood via cuthbert or cd-dynamax.
+    """Discrete-time marginal likelihood via cuthbert, pfjax, or cd-dynamax.
 
     Filter type inferred from config class: KFConfig, EKFConfig, UKFConfig (cd-dynamax)
-    or EKFConfig (cuthbert), PFConfig (cuthbert).
+    or EKFConfig (cuthbert), PFConfig (cuthbert or pfjax).
 
     Args:
         name: Name of the factor.
@@ -288,6 +292,18 @@ def _filter_discrete_time(
             ctrl_values=ctrl_values,
             **kwargs,
         )
+    elif filter_config.filter_source == "pfjax":
+        run_pfjax_discrete(
+            name,
+            dynamics,
+            filter_config,
+            key=key,
+            obs_times=obs_times,
+            obs_values=obs_values,
+            ctrl_times=ctrl_times,
+            ctrl_values=ctrl_values,
+            **kwargs,
+        )
     else:
         raise ValueError(f"Unknown filter source: {filter_config.filter_source}")
 
@@ -342,6 +358,7 @@ def _filter_continuous_time(
     "HMMConfig",
     "HMMConfigs",
     "KFConfig",
+    "MarginalPFConfig",
     "PFConfig",
     "PFResamplingConfig",
     "UKFConfig",
diff --git a/dynestyx/inference/integrations/pfjax/__init__.py b/dynestyx/inference/integrations/pfjax/__init__.py
new file mode 100644
index 00000000..08f0f343
--- /dev/null
+++ b/dynestyx/inference/integrations/pfjax/__init__.py
@@ -0,0 +1,3 @@
+from dynestyx.inference.integrations.pfjax.discrete import run_discrete_filter
+
+__all__ = ["run_discrete_filter"]
diff --git a/dynestyx/inference/integrations/pfjax/discrete.py b/dynestyx/inference/integrations/pfjax/discrete.py
new file mode 100644
index 00000000..800b8907
--- /dev/null
+++ b/dynestyx/inference/integrations/pfjax/discrete.py
@@ -0,0 +1,397 @@
+import warnings
+from typing import Any
+
+import jax
+import jax.numpy as jnp
+import numpyro
+from jax import lax, random
+from jax.scipy.special import logsumexp
+
+from dynestyx.inference.filter_configs import (
+    BaseFilterConfig,
+    MarginalPFConfig,
+    PFConfig,
+    PFResamplingConfig,
+    _config_to_record_kwargs,
+)
+from dynestyx.models import DynamicalModel
+from dynestyx.utils import (
+    _should_record_field,
+    _validate_control_dim,
+    _validate_controls,
+)
+from pfjax.particle_filter import particle_filter as pfjax_particle_filter
+from pfjax.particle_filter import particle_filter_rb as pfjax_particle_filter_rb
+
+
+def _config_to_filter_kwargs(config: BaseFilterConfig) -> dict:
+    """Build filter kwargs dict from config dataclass."""
+    kwargs = dict(config.extra_filter_kwargs)
+    if isinstance(config, PFConfig):
+        kwargs["n_filter_particles"] = config.n_particles
+        kwargs["ess_threshold"] = config.ess_threshold_ratio
+    if isinstance(config, MarginalPFConfig):
+        kwargs["stop_proposal_gradient"] = config.stop_proposal_gradient
+    return kwargs
+
+
+def _tree_take(tree: Any, idx: jax.Array) -> Any:
+    """Index a pytree along axis 0."""
+    return jax.tree.map(lambda x: x[idx], tree)
+
+
+# TODO: Move these to a separate file, and reuse the multinomial resampling from PFJax.
+def _resample_multinomial(
+    key: jax.Array, x_particles_prev: Any, logw: jax.Array
+) -> dict[str, Any]:
+    n_particles = int(logw.shape[0])
+    logw_norm = logw - logsumexp(logw)
+    prob = jnp.exp(logw_norm)
+    ancestors = random.choice(
+        key, a=jnp.arange(n_particles), shape=(n_particles,), p=prob
+    )
+    return {
+        "x_particles": _tree_take(x_particles_prev, ancestors),
+        "ancestors": ancestors,
+    }
+
+
+def _resample_systematic(
+    key: jax.Array, x_particles_prev: Any, logw: jax.Array
+) -> dict[str, Any]:
+    n_particles = int(logw.shape[0])
+    logw_norm = logw - logsumexp(logw)
+    prob = jnp.exp(logw_norm)
+    cdf = jnp.cumsum(prob)
+    u = (random.uniform(key, ()) + jnp.arange(n_particles)) / n_particles
+    ancestors = jnp.searchsorted(cdf, u, side="right")
+    ancestors = jnp.minimum(ancestors, n_particles - 1)
+    return {
+        "x_particles": _tree_take(x_particles_prev, ancestors),
+        "ancestors": ancestors,
+    }
+
+
+def _resample_stratified(
+    key: jax.Array, x_particles_prev: Any, logw: jax.Array
+) -> dict[str, Any]:
+    n_particles = int(logw.shape[0])
+    logw_norm = logw - logsumexp(logw)
+    prob = jnp.exp(logw_norm)
+    cdf = jnp.cumsum(prob)
+    u = (jnp.arange(n_particles) + random.uniform(key, (n_particles,))) / n_particles
+    ancestors = jnp.searchsorted(cdf, u, side="right")
+    ancestors = jnp.minimum(ancestors, n_particles - 1)
+    return {
+        "x_particles": _tree_take(x_particles_prev, ancestors),
+        "ancestors": ancestors,
+    }
+
+
+def _make_resampler(
+    resampling_config: PFResamplingConfig,
+    ess_threshold: float,
+):
+    base_method = resampling_config.base_method
+    if base_method == "multinomial":
+        base_resampler = _resample_multinomial
+    elif base_method == "systematic":
+        base_resampler = _resample_systematic
+    elif base_method == "stratified":
+        base_resampler = _resample_stratified
+    else:
+        raise ValueError(f"Unsupported PF resampling base method: {base_method}")
+
+    def resampler(
+        key: jax.Array, x_particles_prev: Any, logw: jax.Array
+    ) -> dict[str, Any]:
+        n_particles = int(logw.shape[0])
+        logw_norm = logw - logsumexp(logw)
+        prob = jnp.exp(logw_norm)
+        ess = 1.0 / jnp.sum(jnp.square(prob))
+        threshold = ess_threshold * n_particles
+
+        def _do_resample(_: None) -> jax.Array:
+            return base_resampler(
+                key=key, x_particles_prev=x_particles_prev, logw=logw
+            )["ancestors"]
+
+        def _skip_resample(_: None) -> jax.Array:
+            return jnp.arange(n_particles, dtype=jnp.int32)
+
+        ancestors = lax.cond(
+            ess < threshold, _do_resample, _skip_resample, operand=None
+        )
+        return {
+            "x_particles": _tree_take(x_particles_prev, ancestors),
+            "ancestors": ancestors,
+        }
+
+    return resampler
+
+
+def _resolve_stop_gradient(filter_config: PFConfig) -> bool:
+    method = filter_config.resampling_method.differential_method
+    if method == "stop_gradient":
+        return True
+    if method == "straight_through":
+        return False
+    raise ValueError(
+        "PFJax integration does not support "
+        "resampling_method.differential_method='soft'."
+    )
+
+
+class _PFJaxDynestyxModel:
+    """PFJax model adapter for dynestyx discrete-time dynamical models."""
+
+    def __init__(self, dynamics: DynamicalModel):
+        self.dynamics = dynamics
+
+    def pf_init(self, key, y_init, theta):
+        del theta
+        x_init = self.dynamics.initial_condition.sample(key)
+        edist = self.dynamics.observation_model(x_init, y_init["u"], y_init["time"])
+        logw = jnp.asarray(edist.log_prob(y_init["y"])).sum()
+        return x_init, logw
+
+    def pf_step(self, key, x_prev, y_curr, theta):
+        del theta
+        pdist = self.dynamics.state_evolution(
+            x_prev, y_curr["u_prev"], y_curr["time_prev"], y_curr["time"]
+        )
+        x_curr = pdist.sample(key)
+        edist = self.dynamics.observation_model(x_curr, y_curr["u"], y_curr["time"])
+        logw = jnp.asarray(edist.log_prob(y_curr["y"])).sum()
+        return x_curr, logw
+
+
+class _PFJaxMarginalDynestyxModel:
+    """PFJax marginal PF model adapter for dynestyx discrete-time models."""
+
+    def __init__(self, dynamics: DynamicalModel):
+        self.dynamics = dynamics
+
+    def _pack_state(
+        self, x: jax.Array, y_step: dict[str, jax.Array]
+    ) -> dict[str, jax.Array]:
+        return {
+            "x": x,
+            "u": y_step["u"],
+            "u_prev": y_step["u_prev"],
+            "time": y_step["time"],
+            "time_prev": y_step["time_prev"],
+        }
+
+    def pf_init(self, key, y_init, theta):
+        del theta
+        x_raw = self.dynamics.initial_condition.sample(key)
+        x_init = self._pack_state(x_raw, y_init)
+        logw = self.meas_lpdf(y_curr=y_init, x_curr=x_init, theta=jnp.zeros((0,)))
+        return x_init, logw
+
+    def step_sample(self, key, x_prev, y_curr, theta):
+        del theta
+        pdist = self.dynamics.state_evolution(
+            x_prev["x"], y_curr["u_prev"], y_curr["time_prev"], y_curr["time"]
+        )
+        x_raw = pdist.sample(key)
+        return self._pack_state(x_raw, y_curr)
+
+    def step_lpdf(self, x_curr, x_prev, y_curr, theta):
+        del theta, y_curr
+        return self.state_lpdf(x_curr=x_curr, x_prev=x_prev, theta=jnp.zeros((0,)))
+
+    def state_lpdf(self, x_curr, x_prev, theta):
+        del theta
+        pdist = self.dynamics.state_evolution(
+            x_prev["x"], x_curr["u_prev"], x_curr["time_prev"], x_curr["time"]
+        )
+        return jnp.asarray(pdist.log_prob(x_curr["x"])).sum()
+
+    def meas_lpdf(self, y_curr, x_curr, theta):
+        del theta
+        edist = self.dynamics.observation_model(
+            x_curr["x"], y_curr["u"], y_curr["time"]
+        )
+        return jnp.asarray(edist.log_prob(y_curr["y"])).sum()
+
+
+def _normalize_marginal_pf_out(pf_out: dict[str, Any]) -> dict[str, Any]:
+    out = dict(pf_out)
+    out["x_particles"] = pf_out["x_particles"]["x"]
+    out["logw"] = pf_out["logw_bar"]
+    return out
+
+
+def run_discrete_filter(
+    name: str,
+    dynamics: DynamicalModel,
+    filter_config: BaseFilterConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> None:
+    """Run discrete-time particle filter via PFJax."""
+    del kwargs
+
+    if not isinstance(filter_config, (PFConfig, MarginalPFConfig)):
+        raise ValueError(
+            f"Unsupported pfjax config: {type(filter_config).__name__}. "
+            "Expected PFConfig or MarginalPFConfig."
+        )
+    if key is None:
+        raise ValueError(
+            "Particle filter requires a PRNG key: set 'crn_seed' in the filter config, "
+            "or run inside a NumPyro seeded context (e.g., with numpyro.handlers.seed)."
+        )
+
+    filter_kwargs = _config_to_filter_kwargs(filter_config)
+    record_kwargs = _config_to_record_kwargs(filter_config)
+
+    ys = obs_values
+    t1 = int(ys.shape[0])
+    if t1 == 0:
+        return
+
+    times = obs_times
+    _validate_controls(times, ctrl_times, ctrl_values)
+    _validate_control_dim(dynamics, ctrl_values)
+
+    if ctrl_values is None:
+        control_dim = dynamics.control_dim
+        ctrl_values = jnp.zeros((t1, control_dim), dtype=ys.dtype)
+
+    dt0 = times[1] - times[0] if t1 > 1 else jnp.asarray(1.0, dtype=times.dtype)
+    time_prev = jnp.concatenate([times[:1] - dt0, times[:-1]], axis=0)
+    u_prev = jnp.concatenate([ctrl_values[:1], ctrl_values[:-1]], axis=0)
+
+    y_meas = {
+        "y": ys,
+        "u": ctrl_values,
+        "u_prev": u_prev,
+        "time": times,
+        "time_prev": time_prev,
+    }
+
+    n_particles = int(filter_kwargs.pop("n_filter_particles", 1_000))
+    ess_threshold = float(filter_kwargs.pop("ess_threshold", 0.7))
+
+    stop_gradient = _resolve_stop_gradient(filter_config)
+    if stop_gradient and ess_threshold < 1.0:
+        if filter_config.warn:
+            warnings.warn(
+                "PFJax stop-gradient mode requires resampling at every step for "
+                "its correction term to be valid. Overriding ess_threshold_ratio "
+                f"from {ess_threshold} to 1.0.",
+                stacklevel=2,
+            )
+        ess_threshold = 1.0
+    resampler = _make_resampler(filter_config.resampling_method, ess_threshold)
+
+    theta = filter_kwargs.pop("theta", jnp.zeros((0,), dtype=ys.dtype))
+    for reserved in (
+        "model",
+        "key",
+        "y_meas",
+        "n_particles",
+        "resampler",
+        "history",
+        "stop_gradient",
+    ):
+        filter_kwargs.pop(reserved, None)
+
+    if isinstance(filter_config, MarginalPFConfig):
+        stop_proposal_gradient = bool(filter_kwargs.pop("stop_proposal_gradient", True))
+        model = _PFJaxMarginalDynestyxModel(dynamics)
+        pf_out = pfjax_particle_filter_rb(
+            model=model,
+            key=key,
+            y_meas=y_meas,
+            theta=theta,
+            n_particles=n_particles,
+            resampler=resampler,  # type: ignore[arg-type]
+            history=True,
+            stop_gradient=stop_gradient,
+            stop_proposal_gradient=stop_proposal_gradient,
+            **filter_kwargs,
+        )
+        pf_out = _normalize_marginal_pf_out(pf_out)
+    else:
+        model = _PFJaxDynestyxModel(dynamics)  # type: ignore
+        pf_out = pfjax_particle_filter(
+            model=model,
+            key=key,
+            y_meas=y_meas,
+            theta=theta,
+            n_particles=n_particles,
+            resampler=resampler,  # type: ignore[arg-type]
+            history=True,
+            stop_gradient=stop_gradient,
+            **filter_kwargs,
+        )
+
+    marginal_loglik = pf_out["loglik"]
+    numpyro.factor(f"{name}_marginal_log_likelihood", marginal_loglik)
+    numpyro.deterministic(f"{name}_marginal_loglik", marginal_loglik)
+    _add_sites_pf(name, pf_out, record_kwargs)
+
+
+def _add_sites_pf(name: str, pf_out: dict[str, Any], record_kwargs: dict):
+    particles = pf_out["x_particles"]
+    log_weights = pf_out["logw"]
+    if particles.ndim == 2:
+        particles = particles[..., None]
+
+    max_elems = record_kwargs["record_max_elems"]
+    t1, n_particles, state_dim = particles.shape
+
+    add_particles = _should_record_field(
+        record_kwargs["record_filtered_particles"], particles.shape, max_elems
+    )
+    add_log_weights = _should_record_field(
+        record_kwargs["record_filtered_log_weights"], log_weights.shape, max_elems
+    )
+    add_mean = _should_record_field(
+        record_kwargs["record_filtered_states_mean"], (t1, state_dim), max_elems
+    )
+    add_filtered_states_cov = _should_record_field(
+        record_kwargs["record_filtered_states_cov"],
+        (t1, state_dim, state_dim),
+        max_elems,
+    )
+    add_filtered_states_cov_diag = _should_record_field(
+        record_kwargs["record_filtered_states_cov_diag"], (t1, state_dim), max_elems
+    )
+
+    need_filtered_moments = (
+        add_mean or add_filtered_states_cov or add_filtered_states_cov_diag
+    )
+    if need_filtered_moments:
+        log_weights_norm = log_weights - logsumexp(log_weights, axis=1, keepdims=True)
+        w = jnp.exp(log_weights_norm)[..., None]
+        filtered_means = jnp.sum(particles * w, axis=1)
+
+    if add_filtered_states_cov or add_filtered_states_cov_diag:
+        second_mom = jnp.einsum(
+            "...tnj,...tnk,...tn->...tjk", particles, particles, w.squeeze(-1)
+        )
+        filtered_covariances = second_mom - jnp.einsum(
+            "...tj,...tk->...tjk", filtered_means, filtered_means
+        )
+
+    if add_particles:
+        numpyro.deterministic(f"{name}_filtered_particles", particles)
+    if add_log_weights:
+        numpyro.deterministic(f"{name}_filtered_log_weights", log_weights)
+    if add_mean:
+        numpyro.deterministic(f"{name}_filtered_states_mean", filtered_means)
+    if add_filtered_states_cov:
+        numpyro.deterministic(f"{name}_filtered_states_cov", filtered_covariances)
+    if add_filtered_states_cov_diag:
+        diag_cov = jnp.diagonal(filtered_covariances, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
diff --git a/tests/test_pfjax_integration.py b/tests/test_pfjax_integration.py
new file mode 100644
index 00000000..68dd6c41
--- /dev/null
+++ b/tests/test_pfjax_integration.py
@@ -0,0 +1,118 @@
+import jax.numpy as jnp
+import jax.random as jr
+import pytest
+from numpyro.handlers import seed, trace
+from numpyro.infer import Predictive
+
+from dynestyx.inference.filter_configs import (
+    MarginalPFConfig,
+    PFConfig,
+    PFResamplingConfig,
+)
+from dynestyx.inference.filters import Filter
+from dynestyx.simulators import DiscreteTimeSimulator
+from tests.models import discrete_time_l63_model
+
+
+def test_discrete_pfjax_particle_filter_records_outputs() -> None:
+    obs_times = jnp.arange(start=0.0, stop=1.0, step=0.1)
+    true_params = {"rho": jnp.array(28.0)}
+
+    predictive = Predictive(
+        discrete_time_l63_model,
+        params=true_params,
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+    with DiscreteTimeSimulator():
+        synthetic = predictive(jr.PRNGKey(0), obs_times=obs_times)
+
+    obs_values = synthetic["observations"].squeeze(0)
+
+    def data_conditioned_model():
+        with Filter(filter_config=PFConfig(n_particles=64, filter_source="pfjax")):
+            return discrete_time_l63_model(
+                obs_times=obs_times,
+                obs_values=obs_values,
+            )
+
+    with trace() as tr, seed(rng_seed=jr.PRNGKey(1)):
+        data_conditioned_model()
+
+    assert "f_marginal_loglik" in tr
+    assert jnp.isfinite(tr["f_marginal_loglik"]["value"])
+
+    particles = tr["f_filtered_particles"]["value"]
+    log_weights = tr["f_filtered_log_weights"]["value"]
+    assert particles.shape == (obs_times.shape[0], 64, 3)
+    assert log_weights.shape == (obs_times.shape[0], 64)
+
+
+def test_discrete_pfjax_marginal_particle_filter_records_outputs() -> None:
+    obs_times = jnp.arange(start=0.0, stop=1.0, step=0.1)
+    true_params = {"rho": jnp.array(28.0)}
+
+    predictive = Predictive(
+        discrete_time_l63_model,
+        params=true_params,
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+    with DiscreteTimeSimulator():
+        synthetic = predictive(jr.PRNGKey(2), obs_times=obs_times)
+
+    obs_values = synthetic["observations"].squeeze(0)
+
+    def data_conditioned_model():
+        with Filter(filter_config=MarginalPFConfig(n_particles=64)):
+            return discrete_time_l63_model(
+                obs_times=obs_times,
+                obs_values=obs_values,
+            )
+
+    with trace() as tr, seed(rng_seed=jr.PRNGKey(3)):
+        data_conditioned_model()
+
+    assert "f_marginal_loglik" in tr
+    assert jnp.isfinite(tr["f_marginal_loglik"]["value"])
+
+    particles = tr["f_filtered_particles"]["value"]
+    log_weights = tr["f_filtered_log_weights"]["value"]
+    assert particles.shape == (obs_times.shape[0], 64, 3)
+    assert log_weights.shape == (obs_times.shape[0], 64)
+
+
+def test_discrete_pfjax_stop_gradient_forces_always_resample() -> None:
+    obs_times = jnp.arange(start=0.0, stop=1.0, step=0.1)
+    true_params = {"rho": jnp.array(28.0)}
+
+    predictive = Predictive(
+        discrete_time_l63_model,
+        params=true_params,
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+    with DiscreteTimeSimulator():
+        synthetic = predictive(jr.PRNGKey(4), obs_times=obs_times)
+
+    obs_values = synthetic["observations"].squeeze(0)
+
+    def data_conditioned_model():
+        with Filter(
+            filter_config=PFConfig(
+                n_particles=32,
+                filter_source="pfjax",
+                ess_threshold_ratio=0.0,
+                resampling_method=PFResamplingConfig(
+                    differential_method="stop_gradient"
+                ),
+            )
+        ):
+            return discrete_time_l63_model(
+                obs_times=obs_times,
+                obs_values=obs_values,
+            )
+
+    with pytest.warns(UserWarning, match="requires resampling at every step"):
+        with trace(), seed(rng_seed=jr.PRNGKey(5)):
+            data_conditioned_model()

From ded0565f601267c05f0c09547cd1507c75f09ec6 Mon Sep 17 00:00:00 2001
From: Dan Waxman <dan.waxman1@gmail.com>
Date: Sun, 1 Mar 2026 23:12:01 -0500
Subject: [PATCH 3/5] Preliminary pHMC notebook

---
 docs/deep_dives/particle_hmc.ipynb | 588 +++++++++++++++++++++++++++++
 1 file changed, 588 insertions(+)
 create mode 100644 docs/deep_dives/particle_hmc.ipynb

diff --git a/docs/deep_dives/particle_hmc.ipynb b/docs/deep_dives/particle_hmc.ipynb
new file mode 100644
index 00000000..57d6a975
--- /dev/null
+++ b/docs/deep_dives/particle_hmc.ipynb
@@ -0,0 +1,588 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "71dacd15",
+   "metadata": {},
+   "source": [
+    "# pHMC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "14ba6172",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "from dataclasses import dataclass\n",
+    "\n",
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import jax.random as jr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import numpyro\n",
+    "import numpyro.distributions as dist\n",
+    "from numpyro.diagnostics import effective_sample_size\n",
+    "from numpyro.infer import HMC, MCMC, Predictive\n",
+    "\n",
+    "import dynestyx as dsx\n",
+    "from dynestyx import (\n",
+    "    DiscreteTimeSimulator,\n",
+    "    DynamicalModel,\n",
+    "    Filter,\n",
+    "    LinearGaussianObservation,\n",
+    "    LinearGaussianStateEvolution,\n",
+    ")\n",
+    "from dynestyx.inference.filters import (\n",
+    "    KFConfig,\n",
+    "    MarginalPFConfig,\n",
+    "    PFConfig,\n",
+    "    PFResamplingConfig,\n",
+    ")\n",
+    "\n",
+    "import jax.numpy as jnp\n",
+    "\n",
+    "import jax.numpy as jnp\n",
+    "import jax.random as jr\n",
+    "import numpyro\n",
+    "import numpyro.distributions as dist\n",
+    "from numpyro.infer import Predictive\n",
+    "\n",
+    "import dynestyx as dsx\n",
+    "from dynestyx import DiscreteTimeSimulator, DynamicalModel, LinearGaussianStateEvolution\n",
+    "\n",
+    "\n",
+    "jax.config.update(\"jax_enable_x64\", True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9621fa06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model(sigma_h=None, alpha=None, rho=None, obs_times=None, obs_values=None):\n",
+    "    inv_sigma_h_obs = None if sigma_h is None else 1 / sigma_h**2\n",
+    "    inv_sigma_h = numpyro.sample(\n",
+    "        \"inv_sigma_h\", dist.Gamma(1e-1, 1e-1), obs=inv_sigma_h_obs\n",
+    "    )\n",
+    "    sigma_h = jnp.sqrt(1.0 / inv_sigma_h)\n",
+    "\n",
+    "    rho = numpyro.sample(\"rho\", dist.Uniform(-0.999, 0.999), obs=rho)\n",
+    "    alpha = numpyro.sample(\"alpha\", dist.Normal(0.0, 10.0), obs=alpha)\n",
+    "\n",
+    "    dynamics_model = DynamicalModel(\n",
+    "        initial_condition=dist.MultivariateNormal(\n",
+    "            jnp.zeros(1),\n",
+    "            jnp.array([[sigma_h**2 / (1.0 - rho**2)]]),  # variance, not sd\n",
+    "        ),\n",
+    "        state_evolution=LinearGaussianStateEvolution(\n",
+    "            A=jnp.array([[rho]]), cov=jnp.array([[sigma_h**2]])\n",
+    "        ),\n",
+    "        observation_model=lambda x, u, t: dist.Poisson(\n",
+    "            jnp.exp(x[0] + alpha)\n",
+    "        ),  # exp(h + alpha)\n",
+    "        state_dim=1,\n",
+    "        control_dim=0,\n",
+    "        observation_dim=1,\n",
+    "    )\n",
+    "\n",
+    "    dsx.sample(\n",
+    "        \"f\",\n",
+    "        dynamics_model,\n",
+    "        obs_times=obs_times,\n",
+    "        obs_values=obs_values,\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "97f16cdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Array(-0.16292269, dtype=float64), Array(1.31830802, dtype=float64), 2)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true_rho = dist.Uniform(-0.999, 0.999).sample(jr.PRNGKey(0))\n",
+    "true_inv_sigma_h = dist.Gamma(1, 1).sample(jr.PRNGKey(1))\n",
+    "true_alpha = 2\n",
+    "\n",
+    "true_rho, true_inv_sigma_h, true_alpha\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "df2a7e9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs_times = jnp.arange(0.0, 100.0, 1.0) \n",
+    "\n",
+    "def make_data():\n",
+    "    predictive = Predictive(\n",
+    "        model,\n",
+    "        params={\n",
+    "            \"rho\": jnp.array(true_rho),\n",
+    "            \"inv_sigma_h\": jnp.array(true_inv_sigma_h),\n",
+    "            \"alpha\": jnp.array(true_alpha),\n",
+    "        },\n",
+    "        num_samples=1,\n",
+    "        exclude_deterministic=False,\n",
+    "    )\n",
+    "    with DiscreteTimeSimulator():\n",
+    "        pred = predictive(rng_key=jr.PRNGKey(0), obs_times=obs_times)\n",
+    "\n",
+    "    obs_values = pred[\"observations\"][0]\n",
+    "    return obs_times, obs_values, pred\n",
+    "\n",
+    "\n",
+    "obs_times, obs_values, pred = make_data()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "056b0f00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(jnp.exp(pred[\"states\"][0] + 0.5))\n",
+    "plt.scatter(obs_times, obs_values)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "614d4378",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dynestyx.inference.filter_configs import (\n",
+    "    MarginalPFConfig,\n",
+    "    PFResamplingConfig,\n",
+    "    EKFConfig,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def data_conditioned_model(obs_times, obs_values):\n",
+    "    filter_config = MarginalPFConfig(n_particles=100, resampling_method=PFResamplingConfig(base_method=\"systematic\", differential_method=\"stop_gradient\"))\n",
+    "    # filter_config = EKFConfig()\n",
+    "    # filter_config = PFConfig(n_particles=1_000, resampling_method=PFResamplingConfig(base_method=\"systematic\", differential_method=\"stop_gradient\"))\n",
+    "    with Filter(filter_config=filter_config):\n",
+    "        model(obs_times=obs_times, obs_values=obs_values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e7c8bc03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/danwaxman/Documents/dynestyx/dynestyx/inference/filters.py:296: UserWarning: PFJax stop-gradient mode requires resampling at every step for its correction term to be valid. Overriding ess_threshold_ratio from 0.7 to 1.0.\n",
+      "  run_pfjax_discrete(\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictive = Predictive(\n",
+    "    data_conditioned_model,\n",
+    "    params={\n",
+    "        \"rho\": jnp.array(true_rho),\n",
+    "        \"inv_sigma_h\": jnp.array(true_inv_sigma_h),\n",
+    "        \"alpha\": jnp.array(true_alpha),\n",
+    "    },\n",
+    "    num_samples=1,\n",
+    "    exclude_deterministic=False,\n",
+    ")\n",
+    "\n",
+    "filter_outputs = predictive(\n",
+    "    rng_key=jr.PRNGKey(0), obs_times=obs_times, obs_values=obs_values\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c6b65f37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(filter_outputs[\"f_filtered_states_mean\"].squeeze(), label=\"filtered\")\n",
+    "plt.plot(pred[\"states\"][0], label=\"truth\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "91376d21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpyro.infer.util import initialize_model\n",
+    "from numpyro.infer.initialization import init_to_sample\n",
+    "\n",
+    "rng_key = jr.PRNGKey(0)\n",
+    "rng_key, init_key = jax.random.split(rng_key)\n",
+    "init_params, potential_fn_gen, postprocess_fn, *_ = initialize_model(\n",
+    "    init_key,\n",
+    "    data_conditioned_model,\n",
+    "    model_args=(obs_times, obs_values),\n",
+    "    dynamic_args=True,\n",
+    "    init_strategy=init_to_sample,\n",
+    ")\n",
+    "\n",
+    "logdensity_fn = lambda position: -potential_fn_gen(obs_times, obs_values)(position)\n",
+    "initial_position = init_params.z\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7c0dc875",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import blackjax\n",
+    "from tqdm import tqdm\n",
+    "from jax import tree_util\n",
+    "\n",
+    "hmc = blackjax.hmc(logdensity_fn, 1e-2, jnp.eye(len(initial_position)), 10)\n",
+    "state = hmc.init(initial_position)\n",
+    "\n",
+    "\n",
+    "def inference_loop(rng_key, kernel, initial_state, num_samples):\n",
+    "\n",
+    "    @jax.jit\n",
+    "    def one_step(state, rng_key):\n",
+    "        state, info = kernel(rng_key, state)\n",
+    "        return state, info\n",
+    "\n",
+    "    keys = jax.random.split(rng_key, num_samples)\n",
+    "\n",
+    "    state = initial_state\n",
+    "\n",
+    "    states = []\n",
+    "    acceptance_rate = []\n",
+    "    is_divergent = []\n",
+    "    num_integration_steps = []\n",
+    "\n",
+    "    for key in tqdm(keys):\n",
+    "        state, info = one_step(state, key)\n",
+    "\n",
+    "        states.append(state)\n",
+    "        acceptance_rate.append(info.acceptance_rate)\n",
+    "        is_divergent.append(info.is_divergent)\n",
+    "        num_integration_steps.append(info.num_integration_steps)\n",
+    "\n",
+    "    # stack to match scan outputs\n",
+    "    states = tree_util.tree_map(lambda *xs: jnp.stack(xs), *states)\n",
+    "\n",
+    "    acceptance_rate = jnp.stack(acceptance_rate)\n",
+    "    is_divergent = jnp.stack(is_divergent)\n",
+    "    num_integration_steps = jnp.stack(num_integration_steps)\n",
+    "\n",
+    "    return states, (\n",
+    "        acceptance_rate,\n",
+    "        is_divergent,\n",
+    "        num_integration_steps,\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "23aff572",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [02:55<00:00,  5.69it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_sample = 1_000\n",
+    "rng_key, sample_key = jax.random.split(rng_key)\n",
+    "states, infos = inference_loop(sample_key, hmc.step, state, num_sample)\n",
+    "_ = states.position[\"rho\"].block_until_ready()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0de0cd94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [02:55<00:00,  5.70it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "rng_key, sample_key = jax.random.split(rng_key)\n",
+    "states2, infos2 = inference_loop(sample_key, hmc.step, state, num_sample)\n",
+    "_ = states2.position[\"rho\"].block_until_ready()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f680d58c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/danwaxman/Documents/dynestyx/dynestyx/inference/filters.py:296: UserWarning: PFJax stop-gradient mode requires resampling at every step for its correction term to be valid. Overriding ess_threshold_ratio from 0.7 to 1.0.\n",
+      "  run_pfjax_discrete(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alpha</th>\n",
+       "      <td>1.576</td>\n",
+       "      <td>0.654</td>\n",
+       "      <td>-0.232</td>\n",
+       "      <td>2.022</td>\n",
+       "      <td>0.263</td>\n",
+       "      <td>0.337</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>inv_sigma_h</th>\n",
+       "      <td>1.113</td>\n",
+       "      <td>0.520</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>1.729</td>\n",
+       "      <td>0.230</td>\n",
+       "      <td>0.115</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rho</th>\n",
+       "      <td>-0.155</td>\n",
+       "      <td>0.228</td>\n",
+       "      <td>-0.442</td>\n",
+       "      <td>0.445</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
+       "alpha        1.576  0.654  -0.232    2.022      0.263    0.337      12.0   \n",
+       "inv_sigma_h  1.113  0.520   0.020    1.729      0.230    0.115       6.0   \n",
+       "rho         -0.155  0.228  -0.442    0.445      0.063    0.069      18.0   \n",
+       "\n",
+       "             ess_tail  r_hat  \n",
+       "alpha            11.0   1.11  \n",
+       "inv_sigma_h      15.0   1.26  \n",
+       "rho              11.0   1.06  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import arviz as az\n",
+    "\n",
+    "# positions_stacked = {\n",
+    "#     k: jnp.stack([states.position[k][100:], states2.position[k][100:]])\n",
+    "#     for k in [\"alpha\", \"inv_sigma_h\", \"rho\"]\n",
+    "# }\n",
+    "\n",
+    "def _postprocess_single(i):\n",
+    "    full = postprocess_fn(obs_times, obs_values)(\n",
+    "        {\n",
+    "            \"alpha\": states.position[\"alpha\"][i],\n",
+    "            \"inv_sigma_h\": states.position[\"inv_sigma_h\"][i],\n",
+    "            \"rho\": states.position[\"rho\"][i],\n",
+    "        }\n",
+    "    )\n",
+    "    return {k: full[k] for k in [\"alpha\", \"inv_sigma_h\", \"rho\"]}\n",
+    "\n",
+    "\n",
+    "postprocessed = jax.jit(jax.vmap(_postprocess_single))(jnp.arange(num_sample))\n",
+    "posterior_samples_1 = {k: v[None, ...] for k, v in postprocessed.items()}\n",
+    "\n",
+    "def _postprocess_single(i):\n",
+    "    full = postprocess_fn(obs_times, obs_values)(\n",
+    "        {\n",
+    "            \"alpha\": states2.position[\"alpha\"][i],\n",
+    "            \"inv_sigma_h\": states2.position[\"inv_sigma_h\"][i],\n",
+    "            \"rho\": states2.position[\"rho\"][i],\n",
+    "        }\n",
+    "    )\n",
+    "    return {k: full[k] for k in [\"alpha\", \"inv_sigma_h\", \"rho\"]}\n",
+    "\n",
+    "postprocessed_2 = jax.jit(jax.vmap(_postprocess_single))(jnp.arange(num_sample))\n",
+    "posterior_samples_2 = {k: v[None, ...] for k, v in postprocessed_2.items()}\n",
+    "\n",
+    "num_warmup = 250\n",
+    "posterior_samples_stacked = {k: jnp.stack([posterior_samples_1[k][..., num_warmup:], posterior_samples_2[k][..., num_warmup:]], axis=0).squeeze(1) for k in [\"alpha\", \"inv_sigma_h\", \"rho\"]}\n",
+    "\n",
+    "az.summary(posterior_samples_stacked, var_names=[\"alpha\", \"inv_sigma_h\", \"rho\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "323b4be3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9PV1uv/12adasmT2+U6dOFmz36dNHpk6dauPAx4wZI4MHDybIBgAAAR2rr78zNMM3LqSzcGkvxNJMTkkQDgABoC3YOq+3zu+tU1ZocK0B+F133WVdxFevXm3Tk2nGdJ3aIi0tzYJsD73qunTpUsuGrq3i5cuXtzHlhecVBwAAuFyeAFx73ulsFZE4E0JJL05oRn/9TVe4t2JpIAgHEJb0PwTNJu5ZD3ezZ88ucp8G3Zqg7Yfo+12+fHmAzwwIPaeVZwCBQdkPzy7ongBce+fBV9myZe3WMzywtLqmE4QDCEt6Zfbzzz8P9WkACADKMxCdKPvhxzMGXL8b+Of5bPSzKq0gnOzoAAAAABBF6JUU2s+GIBwAAAAAgCAhCAcQlr777jtp3bq1LboOwLkoz0B0ouyjNK1bt85arZ2Y5Z0x4QDCdo7tbdu2edcBOBflGYhOlH1nqTtqWVBf7/Mp3SRaEYQDCMtKvSDnrHe90dMrJCY+8aLHR3NFDoR7uS9ueb5UlHugZGWytFAmEUw5OTmO/cDpjg4AAAAACGvt2rWTIUOGyLBhw+TKK6+Uzp072/bt27dLq1atLKv5LbfcIvv27fN53MyZM+Waa66R+Ph4adCggfzlL3+RUCMIBwAAAACEvXnz5lkw/dFHH8msWbNs269//Wv57W9/a8MYy5QpIz/96U+9xy9evFgef/xxeeKJJ2TXrl3y85//XB599FH54IMPQvgu6I4OAAAAAHCA6667TqZOnWrrR48etdtJkybJHXfcYeujRo2Sbt26ydmzZyUxMVFeeOEFeeSRR+QXv/iF7R8+fLhs2rTJtt95550hex+0hAMAAAAAwl7Lli0v2NasWTPvevXq1e02MzPTbvfu3Su33nqrz/F6X7eHEonZAIStmLJJoT4FAAFCeQaik47dBQKlfPnyF2yLi4vzruuUZU6YWYcgHEBY0uzJtX65INSnASAAKM9A9AZMX3/9dahPA1GsUaNGNn68X79+3m16v3HjxiE9L4JwAAAAAEDEGTFihNx///3SokUL6dixoyxZskTeeustWb16dUjPiyAcAAAAABBxevbsKS+//LIlYtMs6fXq1ZM5c+bYdGehRBAOICwV5J6TzEXP2HryfeMlJi4h1KcEoIQoz0B0+u6776RLly62/t5770nZsmVDfUq4iM+ndAvrz2fdunU+9zWQdrvdPttSU1Mv2DZo0CBbwglBOIDw5HbLucO7vOsAHIzyDEQlTY61fv167zqA7xGEAwAQZeqOWhbqUwAAIGoxTzgAAAAAAEFCEA4AAAAAQJAQhAMAAAAAEK5B+FdffSUPP/ywVK1a1TIcNm3aVLZt2+bdr9noxo4dK9WrV7f9Oh/bp59+6vMcJ06ckN69e0tSUpJUrlxZ+vfvL6dPnw7MOwIAAAAAFIlEeaH9bIqVmO2///2v3HrrrXLnnXfaNANXXXWVBdhXXHGF95ipU6fKK6+8IvPmzbN52J5++mnp3Lmz7NmzRxITE+0YDcCPHj0qq1atktzcXHn00Udl4MCBsmDBgsC/QwCO5WJaMiBiUJ6B6FSuXLlQnwIKiY+Pl5iYGDly5IjFcnrf5XLxGcn3jck5OTny9ddf22ekn01YBOHPP/+81KpVyyY499BAu/CJv/TSSzJmzBjp0aOHbXvttdckJSVF3n77bXnggQdk7969smLFCtm6dau0atXKjnn11Vela9euNol6jRo1AvfuADhWTHyi1B7+91CfBoAAoDwD0al8+fJy5syZUJ8GCtHgUuM3bRDVQBz+LxzVrl3bPquwCMLfffdda9W+7777bM6/q6++Wn7xi1/IgAEDbP/Bgwfl2LFj1gXdo1KlStKmTRvJyMiwIFxvtQu6JwBXery+yc2bN8u99957weueO3fOFo/s7OySvl8AAAAAiFrawqtBZl5enuTn54f6dMJKbGyslClTptR7BxQrCP/ss89k5syZMnz4cHnqqaesNfuXv/ylfZH9+vWzAFxpy3dhet+zT2+Tk5N9T6JMGalSpYr3mPNNnjxZxo8fX9z3BgAAAAA4jwaZcXFxtiD4Yoo7SP3GG2+U5557Tlq0aGHjuLUVfNasWaV3hiIyevRoycrK8i6HDx8u1dcDEHruvBzJXDTOFl0H4FyUZyA6nT17Vrp162aLrgMoQUu4Zjxv3Lixz7ZGjRrJ3//+/bjNatWq2e3x48ftWA+9n5qa6j0mMzPT5zm0K4RmTPc8/nwJCQm2AIge7oIC+e6zbd51UoYAzkV5BqKTdnVevny5dx1ACVrCNTP6vn37fLb9+9//ljp16ti6DvLXQHrNmjU+47d1rHfbtm3tvt6ePHlStm/f7j1m7dq11squY8cBAAAAAIhUxWoJT09Pl1tuucW6o99///2yZcsW+cMf/mCLZ2zBsGHD5Nlnn5XrrrvOO0WZZjzv2bOnt+X87rvv9nZj1ynKhgwZYknbyIwOBF/dUcv42AEAAIBwDMJbt24tixcvtjHaEyZMsCBbpyTTeb89nnzySZuKQMeLa4v3bbfdZlOSeeYIV/Pnz7fAu0OHDpYVPS0tzeYWBwAAAAAgkhUrCFf33HOPLUXR1nAN0HUpimZCX7BgQXFfGgAAAAAARyu9GcgBAAAAAIAPgnAAAAAAAIKEIBxAWIqJT5Q6I5faouvhbubMmdKsWTNJSkqyRWeCeO+997z7dX7UwYMHS9WqVaVChQqWC0Onbyzs0KFDNpdquXLlJDk5WUaMGGFTOAJO57TyDCAwypcvL2632xZdB/A9gnAACICaNWvKlClTbPrFbdu2Sfv27aVHjx6ye/du7+wSS5YskUWLFsn69evlyJEj0qtXL+/jdf5UDcBzcnJk48aNMm/ePJk7d66MHTuW7wcAACCaE7MBAC7UvXt3n/uTJk2y1vFNmzZZgD579mxLSKnBuZozZ45N2aj7b775Zlm5cqXs2bNHVq9eLSkpKZKamioTJ06UkSNHyrhx4yQ+Pp6PHQAAIALQEg4gLLnzcuTrtyfboutOoq3aCxcutOkatVu6to7n5uZKx44dvcc0bNhQateuLRkZGXZfb5s2bWoBuEfnzp0lOzvb25ruz7lz5+yYwgsQbpxcngGUnA7Fuu+++2zRdQDfIwgHEJbcBQXy7b6PbNF1J9i5c6eN905ISJDHHntMFi9eLI0bN5Zjx45ZS3blypV9jteAW/cpvS0cgHv2e/YVZfLkyVKpUiXvUqtWrVJ5b0C0lWcAgbko/eabb9qi6wC+RxAOAAHSoEED2bFjh2zevFkGDRok/fr1sy7mpWn06NGSlZXlXQ4fPlyqrwcAAIDLw5hwAAgQbe2+9tprbb1ly5aydetWefnll+UnP/mJJVw7efKkT2u4ZkevVq2arevtli1bfJ7Pkz3dc4w/2uquCwAAAJyBlnAAKCUFBQU2ZlsD8ri4OFmzZo133759+2xKMh0zrvRWu7NnZmZ6j1m1apVNd6Zd2gEAABAZaAkHgAB1C+/SpYslWzt16pRlQl+3bp28//77Nla7f//+Mnz4cKlSpYoF1kOHDrXAWzOjq06dOlmw3adPH5k6daqNAx8zZozNLU5LNwAAQOQgCAeAANAW7L59+8rRo0ct6G7WrJkF4HfddZftnzZtmsTExEhaWpq1jmvm8xkzZngfHxsbK0uXLrWx5Bqcly9f3saUT5gwge8HAAAgghCEA0AA6DzgF5OYmCjTp0+3pSh16tSR5cuX830AAABEMIJwAGHJFZcgtdLf9K4DcC7KMxCdypUrJ6dPn/auA/geidkAhCWXyyUx8Ym26DoA56I8o7ApU6bY38SwYcO8286ePWs5MKpWrSoVKlSwoTueGSI8NJllt27dLJhLTk6WESNGSF5eHh9uGNPvWYdX6cL/5cD/EIQDAAAgKHTqxt///veWN6Ow9PR0WbJkiSxatEjWr18vR44ckV69enn35+fnWwCu0z1u3LhR5s2bJ3PnzpWxY8fyzQFwHIJwAGHJnZcr/1k2zRZdB+BclGco7Zbcu3dv+eMf/yhXXHGF90PJysqyvBovvviitG/f3qZ1nDNnjgXbmzZtsmNWrlwpe/bskb/+9a+Smppqs1FMnDjR8mxoYI7wpIlIH3nkEVt0HcD3CMIBhCV3Qb6c2bXGFl0H4FyUZyjtbq6t2R07dvT5QLZv3y65ubk+2xs2bGhTPmZkZNh9vW3atKmkpKR4j9FZJrKzs2X37t1FfsAa+OkxhRcEjw4X0F4LujB0APgfErMBAACgVC1cuFA+/vhj645+vmPHjkl8fLxUrlzZZ7sG3LrPc0zhANyz37OvKJMnT5bx48cH6F0AQGDQEg4AAIBSc/jwYXn88cdl/vz5Nl1jMI0ePdq6u3sWPRcACDWCcAAAAJQa7W6emZkpN954o5QpU8YWTb72yiuv2Lq2aOu47pMnT/o8TrOjV6tWzdb19vxs6Z77nmP8SUhIkKSkJJ8FAEKNIBwAAAClpkOHDrJz507ZsWOHd2nVqpUlafOsx8XFyZo1a7yP2bdvn01J1rZtW7uvt/ocGsx7rFq1yoLqxo0b8+0BcBTGhAMAAKDUVKxYUZo0aeKzTeeN1jnBPdv79+8vw4cPlypVqlhgPXToUAu8b775ZtvfqVMnC7b79OkjU6dOtXHgY8aMsWRv2toNAE5CEA4AAICQmjZtmsTExEhaWpplNNfM5zNmzPDuj42NlaVLl8qgQYMsONcgvl+/fjJhwoSQnjcAlARBOICw5IpLkJpD53vXATgX5RnnW7dunc99Tdimc37rUpQ6derI8uXL+TAdpFy5ct4hBLoO4HsE4QDCksvlkthylUJ9GgACgPIMRG/Zv+qqq0J9GkDYITEbAAAAAABBQhAOICy583Llm5UzbdF1AM5FeQaik47v1+R5uug6gO8RhAMIS+6CfDn9yTJbdB2Ac1GegeiUl5dnCfZ00XUA3yMIBwAAAAAgSAjCAQAAAAAIEoJwAAAAAACChCAcAAAAAIAgIQgHAAAAACAcg/Bx48aJy+XyWRo2bOjdf/bsWZuCoGrVqlKhQgVJS0uT48eP+zzHoUOHpFu3blKuXDlJTk6WESNGkC0RAAAAABAVyhT3ATfccIOsXr36f09Q5n9PkZ6eLsuWLZNFixZJpUqVZMiQIdKrVy/56KOPbH9+fr4F4NWqVZONGzfK0aNHpW/fvhIXFyfPPfdcoN4TgAjgiouXqx+b7V0H4FyUZyA6lS1bVg4ePOhdB1DCIFyDbg2iz5eVlSWzZ8+WBQsWSPv27W3bnDlzpFGjRrJp0ya5+eabZeXKlbJnzx4L4lNSUiQ1NVUmTpwoI0eOtFb2+Hh+aAP4nssVI2UqpfBxABGA8gxEp5iYGKlbt26oTwNw/pjwTz/9VGrUqCH169eX3r17W/dytX37dsnNzZWOHTt6j9Wu6rVr15aMjAy7r7dNmza1ANyjc+fOkp2dLbt37w7MOwIAAAAAIBJawtu0aSNz586VBg0aWFfy8ePHy49+9CPZtWuXHDt2zFqyK1eu7PMYDbh1n9LbwgG4Z79nX1HOnTtni4cG7QAimzs/V05u+IutV769j7hi40J9SgBKiPIMRKecnBz59a9/beuTJk2i1ytQkiC8S5cu3vVmzZpZUF6nTh154403SnWcx+TJky3gBxA93Pn5kr3lLVuvdOtDBOGAg1GegeikvWRfeOEFW2foKRCgKcq01fv666+X/fv32zhxvdp18uRJn2M0O7pnDLnenp8t3XPf3zhzj9GjR9uYc89y+PDhyzltAAAAAACcF4SfPn1aDhw4INWrV5eWLVtalvM1a9Z49+/bt8/GjLdt29bu6+3OnTslMzPTe8yqVaskKSlJGjduXOTrJCQk2DGFFwAAAAAAIro7+q9+9Svp3r27dUE/cuSIPPPMMxIbGysPPvigTUnWv39/GT58uFSpUsUC5aFDh1rgrZnRVadOnSzY7tOnj0ydOtXGgY8ZM8bmFtdAGwAA4FLVHbWs1D+sz6d0K/XXAABEl2IF4V9++aUF3N98841cddVVctttt9n0Y7qupk2bZlMRpKWlWSI1zXw+Y8YM7+M1YF+6dKkMGjTIgvPy5ctLv379ZMKECYF/ZwAAAAAAODkIX7hw4UX3JyYmyvTp020piraiL1++vDgvCwAAAABARLisMeEAgP/N4tC6dWupWLGiJCcnS8+ePS0vRmHt2rUTl8vlszz22GM+x2gejW7dukm5cuXseUaMGCF5eXl8zAAAANHYEg4AweKKi5fqP53uXQ9369evt/wWGohr0PzUU09ZHow9e/bY0BuPAQMG+AzB0WDbIz8/3wJwnS1i48aNcvToUenbt68lvXzuueeC/p6AaC3PAAJDpzDetWuXdx3A9wjCAYQllytG4q+qI06xYsUKn/tz5861luzt27fL7bff7hN0FzUl48qVKy1oX716taSkpEhqaqpMnDhRRo4cyfyqcDSnlWcAgaG5om644QY+TuA8dEcHgFKQlZVltzpbRGHz58+XK6+8Upo0aSKjR4+Wb7/91rsvIyNDmjZtagG4hya4zM7Olt27d/M9AQAARABawgGEJXd+rmRlvGHrldreL67YOHGKgoICGTZsmNx6660WbHs89NBDlpyyRo0a8s9//tNauHXc+FtvvWX7ddrGwgG48tzXff7oTBS6eGjADoQbJ5dnACWXk5PjHU6lw7Ti4xmOAiiCcABhyZ2fL1kf/c3Wk25Kc9SPdh0brmPgPvzwQ5/tAwcO9K5ri3f16tWlQ4cOcuDAAbnmmmtKnBBu/Pjxl33OQGlycnkGUHK5ubne/6M00ShBOPA9gnAACKAhQ4bI0qVLZcOGDVKzZs2LHtumTRu73b9/vwXhOlZ8y5YtPsccP37cbosaR65d2ocPH+7TEl6rVq0AvBMAAIqn7qhlPvcLcs561xs9vUJi4hMv6yP9fEo3vhJEBMaEA0AAuN1uC8AXL14sa9eulXr16v3gY3bs2GG32iKu2rZtKzt37pTMzEzvMatWrZKkpCRp3Lix3+dISEiw/YUXAAAAhC9awgEgQF3QFyxYIO+8847NFe4Zw12pUiWblkW7nOv+rl27StWqVW1MeHp6umVOb9asmR2rU5ppsN2nTx+ZOnWqPceYMWPsuTXYBgAAgPPREg4AATBz5kzLiN6uXTtr2fYsr7/+uu3XcXA69ZgG2g0bNpQnnnhC0tLSZMmSJd7niI2Nta7sequt4g8//LDNE154XnEAAAA4Gy3hABCg7ugXo+O0169f/4PPo9nTly9fzncCAAAQoWgJBwAAAAAgSGgJBxCWXGXipFrfF73rAJyL8gxEJ8o+4B9BOICw5IqJlYTq14f6NAAEAOUZiE6UfcA/uqMDAAAAABAktIQDCEvu/FzJ3vaurSe1+rG4YumSDjgV5RmITpR9wD+CcABhyZ2fLyfXzbH1ii26EYQDDkZ5BqITZR/wj+7oAAAAAAAECUE4AAAAAABBQhAOAAAAAECQEIQDAAAAABAkBOEAAAAAAAQJQTgAAAAAAEHCFGUAwpKrTJykPPicdx2Ac1GegehE2Qf8IwgHEJZcMbGSWLtZqE8DQABQnoHoRNkH/KM7OgAAAAAAQUJLOICw5M7Pk9P/3wpbr9D8bnHFUl0BTkV5BqITZR/wj1+1AML2P+4Tq2bZevkmHQnCAQejPAPRibIP+Ed3dAAAAAAAgoQgHAAAAACAICEIBwAAAAAgSAjCAQAAUKpmzpwpzZo1k6SkJFvatm0r7733nnf/2bNnZfDgwVK1alWpUKGCpKWlyfHjx32e49ChQ9KtWzcpV66cJCcny4gRIyQvL49vDoDjEIQDAACgVNWsWVOmTJki27dvl23btkn79u2lR48esnv3btufnp4uS5YskUWLFsn69evlyJEj0qtXL+/j8/PzLQDPycmRjRs3yrx582Tu3LkyduxYvjkAjkN2dAAAAJSq7t27+9yfNGmStY5v2rTJAvTZs2fLggULLDhXc+bMkUaNGtn+m2++WVauXCl79uyR1atXS0pKiqSmpsrEiRNl5MiRMm7cOImPj+cbBOAYBOEAwpKrTJxc9X/PeNcBOBflGYVpq7a2eJ85c8a6pWvreG5urnTs2NF7TMOGDaV27dqSkZFhQbjeNm3a1AJwj86dO8ugQYOsNb1FixZ+P+Rz587Z4pGdnc2XEUSUfaAUuqNrtyKXyyXDhg3zbmNMD4BAcMXESrlrWtui6wCci/IMtXPnThvvnZCQII899pgsXrxYGjduLMeOHbOW7MqVK/t8UBpw6z6lt4UDcM9+z76iTJ48WSpVquRdatWqxZcRRJR9IMBB+NatW+X3v/+9JdkojDE9AAAAOF+DBg1kx44dsnnzZmvB7tevn3UxL02jR4+WrKws73L48GG+GADODMJPnz4tvXv3lj/+8Y9yxRVXeLdr5aZjel588UUb09OyZUsb06MJNHRMj/KM6fnrX/9q43m6dOliY3qmT59uyTYAQLnz8+T0ztW26DoA56I8Q2lr97XXXmu/D7WFunnz5vLyyy9LtWrV7DfgyZMnfT4ozY6u+5Tenp8t3XPfc4w/2uruycjuWRA8lH0ggEG4TiGhGSoLj91RPzSmRxU1pkfH6HgyZAKA/sf9zfKXbCEIB5yN8gx/CgoKbLy2BuVxcXGyZs0a7759+/bZlGQ6ZlzprXZnz8zM9B6zatUqC6q1SzvCE2UfCFBitoULF8rHH39s3dHPV1pjekiqAQAA4FzaLVx7P2rDzKlTpywT+rp16+T999+3sdr9+/eX4cOHS5UqVSywHjp0qAXempRNderUyYLtPn36yNSpU+0345gxY6xhSFu7ASBig3AdR/P444/blcfExEQJFu2yNH78+KC9HgAAAAJHW7D79u0rR48etaBbcwppAH7XXXfZ/mnTpklMTIykpaVZ44v2kpwxY4b38bGxsbJ06VIbS67Befny5W1M+YQJE/iaAER2EK7dzbUSvfHGG32mmdiwYYP87ne/s8rUM6ancGv4+WN6tmzZUqwxPXr1VK+OemjXdbJbAgAAOIPmDLoYbdzR/EC6FKVOnTqyfPnyUjg7AAjjMeEdOnSw8Tia2dKztGrVypK0edZLY0wPSTUAAAAAAFHXEl6xYkVp0qSJzzbtDlS1alXvdsb0AAAAAAAQ4HnCi6Jjeu655x4b03P77bdbF/O33nrrgjE9equt4g8//LCNEWJMDwAn09wVrVu3touVycnJ0rNnT+sJVNjZs2ctiZBeuKxQoYLVk+dPuaM9h3T2iXLlytnzjBgxQvLymKINAAAgarOjn08zWxbGmB4AgeAqEydX9hjlXQ9369evtwBbA3ENmp966inL5rtnzx7rMaTS09Nl2bJlsmjRIktMNGTIEOnVq5d89NFH3hwbGoDrxcuNGzdaAiO9SKnDfJ577rkQv0MgesozgMCg7AOlFIQDQGlwxcRK+Ya3OebDXbFihc/9uXPnWku2JrTUXkFZWVmWmEin5Wnfvr0dM2fOHGnUqJFs2rTJpuFZuXKlBe2rV6+2qRtTU1Nl4sSJMnLkSBk3bpxNAQk4kdPKM4DAoOwDQeqODgAQC7qVznmrNBjPzc2Vjh07ej+ehg0b2py5GRkZdl9vmzZtagG4h07TozNC7N692+/HqlP56P7CCwAAAMIXQTiAsOQuyJcz//rQFl13koKCAhk2bJjceuut3qSVx44ds5bswtM3Kg24dZ/nmMIBuGe/Z19RY9G1a7tnYfpGhCMnl2cAJUfZB/yjOzqAsOTOy5X/vDPF1mulvymu+FhxCh0bvmvXLvnwww9L/bVGjx4tw4cP997XlnACcYQbJ5dnIBjqjloWkR80ZR/wjyAcAAJIk63pDBAbNmyQmjVrerdrsrWcnBw5efKkT2u4ZkfXfZ5jtmzZ4vN8nuzpnmPOl5CQYAsAAACcge7oABAAbrfbAvDFixfL2rVrpV69ej77W7ZsaVnO16xZ492mU5jplGQ6XaPS2507d0pmZqb3mFWrVklSUpI0btyY7wkAACAC0BIOAAHqgq6Zz9955x2bK9wzhlvHaZctW9Zu+/fvb13HNVmbBtZDhw61wFszoyud0kyD7T59+sjUqVPtOcaMGWPPTWs3AABAZCAIB4AAmDlzpt22a9fOZ7tOQ/bII4/Y+rRp0yQmJkbS0tIsq7lmPp8xY4b32NjYWOvKPmjQIAvOdX7xfv36yYQJE/iOAAAAIgRBOAAEqDv6D0lMTJTp06fbUpQ6derI8uXL+U4AAAAiFGPCAQAAAAAIElrCAYQlV2wZqdp1mHcdgHNRnoHoRNkH/OOXLYCw/Y+7QtOOoT4NAAFAeQaiE2Uf8I/u6AAAAAAABAkt4QDCkrsgX747+LGtl613o7hiYkN9SgBKiPIMRCfKPuAfQTiAsOTOy5Wv3xxv67XS3xRXPEE44FSUZyA6UfYB/+iODgAAAABAkBCEAwAAAAAQJAThAAAAAAAECUE4AAAAAABBQhAOAAAAAECQEIQDAAAAABAkTFEGICy5YstIlbse864DcC7KMxCdKPuAf/yyBRC2/3FXvPGeUJ8GgACgPAPRibIP+Ed3dAAAAAAAgoSWcABhyV2QL+e+3G3rCTVvEFdMbKhPCUAJUZ6B6ETZB/wjCAcQltx5uXL8b0/Zeq30N8UVTxAOOBXlGYhOlH3AP7qjAwAAAAAQJAThAAAAAAAECUE4AAAAAABBQhAOAAAAAECQEIQDAAAAABAkBOEAAAAAAAQJU5QBCEuu2Fip3O5R7zoA56I8A9GJsg/4RxAOICy5YuOkUpu0UJ8GgACgPAPRibIP+Ed3dAAAAAAAwjEInzlzpjRr1kySkpJsadu2rbz33nve/WfPnpXBgwdL1apVpUKFCpKWlibHjx/3eY5Dhw5Jt27dpFy5cpKcnCwjRoyQvLy8wL0jABHBXZAv547+2xZdB+BclGcgOlH2gQAE4TVr1pQpU6bI9u3bZdu2bdK+fXvp0aOH7N692/anp6fLkiVLZNGiRbJ+/Xo5cuSI9OrVy/v4/Px8C8BzcnJk48aNMm/ePJk7d66MHTu2OKcBIAq483Ll2GvDbdF1AM5FeQaiE2UfCMCY8O7du/vcnzRpkrWOb9q0yQL02bNny4IFCyw4V3PmzJFGjRrZ/ptvvllWrlwpe/bskdWrV0tKSoqkpqbKxIkTZeTIkTJu3DiJj48vzukAAAAAABAdY8K1VXvhwoVy5swZ65aureO5ubnSsWNH7zENGzaU2rVrS0ZGht3X26ZNm1oA7tG5c2fJzs72tqb7c+7cOTum8AIAAAAAQMRnR9+5c6cF3Tr+W8d9L168WBo3biw7duywluzKlSv7HK8B97Fjx2xdbwsH4J79nn1FmTx5sowfP764pwoAuER1Ry0r9c/q8yndSv01AAAAIq4lvEGDBhZwb968WQYNGiT9+vWzLualafTo0ZKVleVdDh8+XKqvBwAAAABAWATh2tp97bXXSsuWLa2Funnz5vLyyy9LtWrVLOHayZMnfY7X7Oi6T+nt+dnSPfc9x/iTkJDgzcjuWQAg3GzYsMFyZ9SoUUNcLpe8/fbbPvsfeeQR2154ufvuu32OOXHihPTu3dvqOe1Z1L9/fzl9+nSQ3wkAAADCdp7wgoICG7OtQXlcXJysWbPGu2/fvn02JZl2X1d6q93ZMzMzvcesWrXKfmxql3YAcDLNkaEXJqdPn17kMRp0Hz161Lv87W9/89mvAbjmyNC6cenSpRbYDxw4MAhnDwAAgLAbE67dwrt06WLJ1k6dOmWZ0NetWyfvv/++VKpUyVpshg8fLlWqVLHAeujQoRZ4a2Z01alTJwu2+/TpI1OnTrVx4GPGjLG5xbW1GwA8XLGxUunWB73rTqD1oy4Xo3VdUT1/9u7dKytWrJCtW7dKq1atbNurr74qXbt2lRdeeMFa2AEncmJ5BnD5KPtAAIJwbcHu27evtd5o0N2sWTMLwO+66y7bP23aNImJiZG0tDRrHdfM5zNmzPA+PjY21lp2dCy5Bufly5e3MeUTJkwozmkAiAKu2DipfFtviTR64TI5OVmuuOIKm87x2WeflapVq3pnkNAu6J4AXOmME1qvah6Oe++9N4RnDpRcpJZnABdH2QcCEITrPOAXk5iYaN0wL9YVs06dOrJ8+fLivCwARATtit6rVy+pV6+eHDhwQJ566ilrOdfgWy9Sau8gDdALK1OmjPUuKmoGCb3gqYsHUzgCAABE2BRlABAMbneB5P7n+5kQ4q6sJS7XZaewCLkHHnjAu960aVPrTXTNNddY63iHDh1K9JxM4QgniMTyDOCHUfYB//hfEEBYcufmyNE/D7ZF1yNR/fr15corr5T9+/fbfR0rXjhxpcrLy7OM6UWNI2cKRzhBNJRn/PAFw9atW0vFihWtx0/Pnj0tgW9hZ8+etTxBOkSnQoUKNrzx/Fl1NOFvt27dpFy5cvY8I0aMsHoS4YmyD/hHEA4AIfLll1/KN998I9WrV7f7mitDp3ncvn2795i1a9faLBRt2rTx+xxM4QjACdavX28B9qZNm2z2h9zcXEvYq7NKeKSnp8uSJUtk0aJFdvyRI0dsCI9Hfn6+BeA6Je7GjRtl3rx5MnfuXBk7dmyI3hUAlAzd0QEgQHQ+b0+rtjp48KDs2LHDxnTrMn78eGvZ0VZtHRP+5JNPyrXXXmtJLFWjRo1s3PiAAQNk1qxZ9iN1yJAh1o2dzOgAnExnfihMg2dtydaLjrfffrtkZWVZ7iGdeUeTVqo5c+ZYvaiBu860s3LlStmzZ4+sXr1aUlJSJDU1VSZOnCgjR46UcePGSXx8fIjeHQAUD0E4AATItm3b5M477/Te1ykblc4CMXPmTPnnP/9pLTfa2q1BtbYC6Q/IwlM0zp8/3wJvHSPumW3ilVde4TuKInVHLQv1KQClToNupRcolQbjeuFRZ4TwaNiwoU2Lq8krNQjXW82noQG4h17E1Fl3du/eLS1atOCbA+AIBOEAECDt2rUTt9td5H6d0vGH6A9SbQkCgEilQ2yGDRsmt956qzRp0sS26QwQ2pKt0zQWpgG3Z3YIvS0cgHv2e/b5wwwSAMIRY8IBAAAQNDo2fNeuXbJw4cKgJISrVKmSd6lVq1apvyYA/BCCcAAAAASFDrdZunSpfPDBB1KzZk3vds2VoQnXdLhOYZod3TM7hN6eny3dc58ZJAA4Cd3RAYQlV2ysJN3Uy7sOwLkoz9ChOkOHDpXFixfLunXrpF69ej4fSsuWLSUuLk7WrFljuTCUTmGmU5LpzBFKbydNmmRTOWpSN6WZ1pOSkqRx48Z+P2TNuVE47waCi7IP+EcQDiAsuWLj5Io7fxrq0wAQAJRnaBd0zXfxzjvv2FzhnjHc2kW8bNmydtu/f39LaKm5MTSw1qBdA29NyqY0maUG23369JGpU6fac4wZM8aem0A7PFH2Af8IwgEAAFCqdIYITwLLwnQaskceecTWp02b5p0VQhOqaebzGTNmeI+NjY21ruyaDV2D8/Lly9vsExMmTODbA+AoBOEAwpLbXSD52V/bemzSVeJykcICcCrKMy42c4RHYmKiTJ8+3Zai1KlTR5YvX84H6hCUfcA/gnAAYcmdmyNfzepv67XS3xRXfGKoTwlACVGegehE2Qf8o2kJAAAAAIAgIQgHAAAAACBICMIBAAAAAAgSgnAAAAAAAIKEIBwAAAAAgCAhCAcAAAAAIEiYogxAWHLFxEqFFt286wCci/IMRCfKPuAfQTiAsOQqEydVOw0K9WkACADKMxCdKPuAf3RHBwAAAAAgSGgJBxCW3G63FHyXbesxZZPE5XKF+pQAlBDlGYhOlH3AP1rCAYQld+45+fLV3rboOgDnojwD0YmyD/hHEA4AAAAAQJAQhAMAAAAAECQE4QAAAAAABAlBOAAAAAAAQUJ2dABAUNQdtaxUn//zKd1K9fkBAAACgZZwAAAAAACChJZwAGHJFRMr5Zt08K4DcC7KMxCdKPuAfwThAMKSq0ycXNktPdSnASAAKM9AdKLsA/7RHR0AAAAAgCChJRxAWHK73eLOPWfrrrgEcblcoT4lACVEeQaiE2UfCEBL+OTJk6V169ZSsWJFSU5Olp49e8q+fft8jjl79qwMHjxYqlatKhUqVJC0tDQ5fvy4zzGHDh2Sbt26Sbly5ex5RowYIXl5ecU5FQARTgPww9P+zxZPMA7AmSjPQHSi7AMBCMLXr19vAfamTZtk1apVkpubK506dZIzZ854j0lPT5clS5bIokWL7PgjR45Ir169vPvz8/MtAM/JyZGNGzfKvHnzZO7cuTJ27NjinAoAAAAAAJHdHX3FihU+9zV41pbs7du3y+233y5ZWVkye/ZsWbBggbRv396OmTNnjjRq1MgC95tvvllWrlwpe/bskdWrV0tKSoqkpqbKxIkTZeTIkTJu3DiJj48P7DsEAAAAACASErNp0K2qVKlitxqMa+t4x44dvcc0bNhQateuLRkZGXZfb5s2bWoBuEfnzp0lOztbdu/efTmnAwAAAABAZCZmKygokGHDhsmtt94qTZo0sW3Hjh2zluzKlSv7HKsBt+7zHFM4APfs9+zz59y5c7Z4aMAOAAAAAEDUtITr2PBdu3bJwoULpbRpQrhKlSp5l1q1apX6awJAcW3YsEG6d+8uNWrUsGzub7/99gVZYjX/RfXq1aVs2bLWa+jTTz/1OebEiRPSu3dvSUpKsgua/fv3l9OnT/NlAAAARHMQPmTIEFm6dKl88MEHUrNmTe/2atWqWcK1kydP+hyv2dF1n+eY87Ole+57jjnf6NGjreu7Zzl8+HBJThsASpUmqWzevLlMnz7d7/6pU6fKK6+8IrNmzZLNmzdL+fLlbTiOzirhoQG4Ds3R5Jdaz2pgP3DgQL45AACAaOyOrq04Q4cOlcWLF8u6deukXr16PvtbtmwpcXFxsmbNGpuaTOkUZjolWdu2be2+3k6aNEkyMzMtqZvSH5va6tO4cWO/r5uQkGALgOjhiomRcg1u9a47QZcuXWwpqv586aWXZMyYMdKjRw/b9tprr9lwHG0xf+CBB2Tv3r2WAHPr1q3SqlUrO+bVV1+Vrl27ygsvvGAt7IATObE8A7h8lH0gAEG4dkHXzOfvvPOOzRXuGcOtXcS1a6XeatfJ4cOHW7I2Daw1aNfAWzOjK53STIPtPn36WKuQPof+KNXnJtAG4OEqEy9X9RwdMR/IwYMHrb4rnLhS68w2bdpYwkoNwvVWu6B7AnClx8fExFjL+b333nvB85IzA04QaeUZwKWh7AP+Fety9MyZM607eLt27WxMo2d5/fXXvcdMmzZN7rnnHmsJ12nLtIv5W2+95d0fGxtrXSz1VoPzhx9+WPr27SsTJkwozqkAgKN4Llr6S0xZOHGlp4eQR5kyZeyiZlGJK8mZAQAAEOHd0X9IYmKijYcsakykqlOnjixfvrw4Lw0AKCJnhvY+Kjx7BMkrAQAAwhcDswCEpYKcs/LF8/fYoutO50k86S8xZeHElZovo7C8vDzLmF5U4kodxqNDfwovQLiJtPIM4NJQ9gH/CMIBIAg0kaUG0pq4snCrtY71Lpy4UmeX2L59u/eYtWvXSkFBgY0dBwAAQJR1RwcAFE3n896/f79PMrYdO3bYmO7atWvLsGHD5Nlnn5XrrrvOgvKnn37aMp737NnTjm/UqJHcfffdMmDAAJvGLDc316aE1KRtZEYHAACIDAThABAg27ZtkzvvvNN73zNWu1+/fjJ37lx58sknbS5xnfdbW7xvu+02m5JMc2l4zJ8/3wLvDh06WFZ0TXKpc4sDAAAgMhCEA0CA6MwRF0tg6XK5bCaIi80Goa3mOhUkAAAAIhNjwgEAAAAACBKCcAAAAAAAgoTu6ADCkismRsrWb+VdB+BcTi7PdUctK/XX+HxKt1J/DSAUnFz2gdJEEA4gLLnKxEvyfeNCfRoAAoDyDEQnyj7gH5ekAAAAAAAIEoJwAAAAAACChCAcQFgqyDkrh15Ms0XXATgX5RmITpR9wD/GhAMIW+7cc6E+BQABQnkGohNlH7gQLeEAAAAAAAQJQTgAAAAAAEFCEA4AAAAAQJAQhAMAAAAAECQE4QAAAAAABAnZ0QGEJ5dLEmo18a4DcDDKMxCdKPuAX7SEAwhLMXEJUu2hKbboOgDnojxjw4YN0r17d6lRo4a4XC55++23fT4Ut9stY8eOlerVq0vZsmWlY8eO8umnn/occ+LECendu7ckJSVJ5cqVpX///nL69Gk+3DBG2Qf8IwgHAABAqTpz5ow0b95cpk+f7nf/1KlT5ZVXXpFZs2bJ5s2bpXz58tK5c2c5e/as9xgNwHfv3i2rVq2SpUuXWmA/cOBAvjkAjkN3dAAAAJSqLl262OKPtoK/9NJLMmbMGOnRo4dte+211yQlJcVazB944AHZu3evrFixQrZu3SqtWrWyY1599VXp2rWrvPDCC9bCjshXd9SyUn+Nz6d0K/XXAGgJBxCWCnLOyuFXHrJF1wE4F+UZF3Pw4EE5duyYdUH3qFSpkrRp00YyMjLsvt5qF3RPAK70+JiYGGs5L8q5c+ckOzvbZ0HwUPYB/wjCAYStgu+ybQHgfJRnFEUDcKUt34Xpfc8+vU1OTvbZX6ZMGalSpYr3GH8mT55sAb1nqVWrFl9EkFH2gQsRhAMAACAijR49WrKysrzL4cOHQ31KAEAQDgAAgNCpVq2a3R4/ftxnu9737NPbzMxMn/15eXmWMd1zjD8JCQmWTb3wAgChRks4AAAAQqZevXoWSK9Zs8a7Tcdu61jvtm3b2n29PXnypGzfvt17zNq1a6WgoMDGjgOAk5AdHQAAAKVK5/Pev3+/TzK2HTt22Jju2rVry7Bhw+TZZ5+V6667zoLyp59+2jKe9+zZ045v1KiR3H333TJgwACbxiw3N1eGDBlimdNLOzN6MDJyA4guBOEAAAAoVdu2bZM777zTe3/48OF2269fP5k7d648+eSTNpe4zvutLd633XabTUmWmJjofcz8+fMt8O7QoYNlRU9LS7O5xQHAaQjCAYQnl0viq13nXQfgYJTnqNeuXTubD7zoPxGXTJgwwZaiaKv5ggULov6zdBTKPuAXQTiAsBQTlyDV+00L9WkACADKMxCdKPuAfyRmAwAAAAAgSAjCAQAAAAAIEoJwAGGpIPesfDnzp7boOgDnojwD0YmyD/jHmHAA4cktkp+d6V0H4GCUZyA6UfaBwLSEb9iwQbp3725zMmomy7fffttnv2a+HDt2rFSvXl3Kli0rHTt2lE8//dTnmBMnTkjv3r0lKSlJKleuLP3797f5IwEAAAAAiGTFDsJ1DsfmzZvL9OnT/e6fOnWqzdk4a9Ys2bx5s5QvX146d+4sZ8/+rzupBuC7d++WVatWydKlSy2w13khAQAAAACIZMUOwrt06SLPPvus3HvvvRfs01bwl156ScaMGSM9evSQZs2ayWuvvSZHjhzxtpjv3btXVqxYIX/605+kTZs2ctttt8mrr74qCxcutOMAIFKNGzfOehAVXho2bOjdrxcrBw8eLFWrVpUKFSpIWlqaHD9+PKTnDAAAgDBOzHbw4EE5duyYdUH3qFSpkgXbGRkZdl9vtQt6q1atvMfo8TExMdZyDgCR7IYbbpCjR496lw8//NC7Lz09XZYsWSKLFi2S9evX24XJXr16hfR8AQAAEMaJ2TQAVykpKT7b9b5nn94mJyf7nkSZMlKlShXvMec7d+6cLR7Z2dmBPG0ACBqt76pVq3bB9qysLJk9e7YsWLBA2rdvb9vmzJkjjRo1kk2bNsnNN9/MtwQAABABHDFF2eTJk61F3bPUqlUr1KcEoLS5ROKq1rZF1yOFJqrUxJb169e3/BiHDh2y7du3b5fc3FyfnkTaVb127drenkSAY0VoeQbwAyj7QOm3hHtad3QMo2ZH99D7qamp3mMyM//ftEP/T15enmVM99c6pEaPHi3Dhw/3aQknEAciW0xcotT42QyJJDo0Z+7cudKgQQPrij5+/Hj50Y9+JLt27bKeQPHx8TZcp6ieRP7QUwhOEInlGcAPo+xHtrqjlpX6a3w+pZtEooC2hNerV88C6TVr1vgEzDrWu23btnZfb0+ePGmtPh5r166VgoIC+4HqT0JCgk1nVngBAKfRxJb33XefJa3UWSOWL19u9eEbb7xR4uekpxAAAECEB+E6n/eOHTts8SRj03XtUqmZfocNG2bZ0999913ZuXOn9O3b17pe9uzZ047X8Y133323DBgwQLZs2SIfffSRDBkyRB544AE7DgCihbZ6X3/99bJ//367gJmTk2NBeWHak6ioXkKenkI6ntyzHD58OAhnDgAAgKAF4du2bZMWLVrYorSbuK6PHTvW7j/55JMydOhQm/e7devWFrTrlGSJiYne55g/f76NdezQoYN07drVpin7wx/+UOI3ASDyFOSelSN/+oUtuh6JtH48cOCADd9p2bKlxMXF+fQk2rdvn13g9PQk8oeeQnCCaCjPAC5E2QcCNCa8Xbt2Nh94UbQ1fMKECbYURTOhawZgACiSWyT3m0Pe9Ujwq1/9Srp37y516tSx6ceeeeYZiY2NlQcffNCSTvbv398ubGodqcNu9IKmBuBkRofjRWB5BnAJKPtA6SdmAwAU7csvv7SA+5tvvpGrrrrKegHp9GO6rqZNmyYxMTGSlpZmCdd03PiMGSSzAgAAiCQE4QAQJAsXLrzofh22M336dFsAAAAQmRwxTzgAAAAAAJGAIBwAAAAAgCAhCAcAAAAAIEgYEw4gPLlEYpOSvesAHIzyDEQnyj7gF0E4gLAUE5coNQf9OdSnASAAKM9AdKLsA/7RHR0AAAAAgCAhCAcAAAAAIEgIwgGEpYLcc3J0Xrotug7AuSjPQHSi7AP+MSYcQHhyuyXn2KfedQAORnkGohNlH/CLlnAAAAAAAIKEIBwAAAAAgCAhCAcAAAAAIEgYEw6EubqjloX6FAD8P5RHAIAT8P9VeCMIBy4DFRwAAADg3N/an0/pJsEWVUF4aX+JofgCgUgWUzYp1KcAIEAoz0B0ouxfiEYcRFUQDsA5YuITpdYvF4T6NOAg/KgJX5RnIDpR9gH/SMwGAAAAAECQEIQDAAAAABAkdEcPoEhNHACEQkHuOclc9IytJ983XmLiEvgiAIeiPAPRibIP+EcQDiA8ud1y7vAu7zoAB6M8A9GJsg/4RXd0AAAAAACChCAcAAAAAIAgIQgHAAAAACBICMIBAAAAAAgSgnAAAAAAAIKE7OgAwpaLacmAiEF5Dt0Up0xvilByWtkPxpTDAEE4gLAUE58otYf/PdSnASAAKM9AdKLsA/7RHR0AAAAAgCAhCAcAAAAAIEjojo6IxZgeZ3Pn5cjXi5+z9avufUpcZeJDfUoASojyDEQnyj7gH0G4wwQjsCSBC8KBu6BAvvtsm3fdFeoTAlBilGcgOlH2Af8IwnEBWpABAAAAoHQwJhwAAAAAgGgIwqdPny5169aVxMREadOmjWzZsiWUpwMAYYP6EQCoHwFEppAF4a+//roMHz5cnnnmGfn444+lefPm0rlzZ8nMzAzVKQFAWKB+BADqRwCRK2RB+IsvvigDBgyQRx99VBo3biyzZs2ScuXKyZ///OdQnRIAhAXqRwCgfgQQuUKSmC0nJ0e2b98uo0eP9m6LiYmRjh07SkZGxgXHnzt3zhaPrKwsu83Ozi7W6xac+/ayzhtA8BTknPUtu+6Cix5f3PrAc7zb7ZZwQv2ISFTc8ozAitb6MVC/Ifn9WHKUfThBdjHqg0DVjyEJwv/zn/9Ifn6+pKSk+GzX+//6178uOH7y5Mkyfvz4C7bXqlWrVM8TQHj4akbfHzym0ksle+5Tp05JpUqVJFxQPyLSXUp5RmBFa/2o+A0ZPij7iKQ68tRl1o+OmKJMr3jq+HGPgoICOXHihFStWlVcLmYPLgm9iqMXMQ4fPixJSUkB/LaiF5+psz5TvYKpFWiNGjXEyUpaP0bb32s0vV/ea+QK1ncbKfWjk35DOrXcOvW8nXzunHdoP+9A1Y8hCcKvvPJKiY2NlePHj/ts1/vVqlW74PiEhARbCqtcuXKpn2c00D8mJ1U8TsBn6pzPNJxaeEJVP0bb32s0vV/ea+QKxncbCfWjE39DOrXcOvW8nXzunHfoPu9A1I8hScwWHx8vLVu2lDVr1vhcmdT7bdu2DcUpAUBYoH4EAOpHAJEtZN3RtWtQv379pFWrVnLTTTfJSy+9JGfOnLFs6QAQzagfAYD6EUDkClkQ/pOf/ES+/vprGTt2rBw7dkxSU1NlxYoVFyTbQOnQrlk6R/v5XbTAZxpOovXvNBj1Y7R9ttH0fnmvkSuavtto+/3o1O/Wqeft5HPnvCPj83a5w23+CQAAAAAAIlRIxoQDAAAAABCNCMIBAAAAAAgSgnAAAAAAAIKEIBwAAAAAgCAhCI9C06dPl7p160piYqK0adNGtmzZEupTcrQNGzZI9+7dpUaNGuJyueTtt98O9Sk52uTJk6V169ZSsWJFSU5Olp49e8q+fftCfVpRUdYXLVokDRs2tOObNm0qy5cvl0h9v3PnzrXyWnjRx0VqnbNu3Tq58cYbLbvrtddea+8/Et+rvs/zv1ddNIt2pNZ9Ti+30ezEiRPSu3dvSUpKksqVK0v//v3l9OnTl/RYzavcpUuXkPzuKO556/FDhw6VBg0aSNmyZaV27dryy1/+UrKyskr1PJ38f2Bxzv2Pf/yj/OhHP5IrrrjClo4dO4bst31JY4yFCxfa37LWe04475MnT8rgwYOlevXq9v/q9ddfX+y/F4LwKPP666/bHMSaav/jjz+W5s2bS+fOnSUzMzPUp+ZYOr+9fo5agHH51q9fbxXbpk2bZNWqVZKbmyudOnWyzxmlV9Y3btwoDz74oP2Y+uSTT+w/Ql127doVsXWb/oA8evSod/niiy8kEuucgwcPSrdu3eTOO++UHTt2yLBhw+RnP/uZvP/++xKp9asGr4W/Ww1qI7Huc3q5jXYayO7evdu+76VLl9pFp4EDB17SY1966SULWpxw3keOHLHlhRdesL9NvQio08rp321pcfL/gcU9d734qOf+wQcfSEZGhtSqVcvqjq+++iqsz9vj888/l1/96ld2ISEUinveOTk5ctddd9l5v/nmm/b/jV4Iufrqq4v3wjpFGaLHTTfd5B48eLD3fn5+vrtGjRruyZMnh/S8IoUWqcWLF4f6NCJKZmamfa7r168P9alEdFm///773d26dfPZ1qZNG/fPf/5zdyS+3zlz5rgrVarkjoY658knn3TfcMMNPtt+8pOfuDt37uyOtPf6wQcf2HH//e9/3dFQ9zm93EazPXv22Pe7detW77b33nvP7XK53F999dVFH/vJJ5+4r776avfRo0eD/rvjcs67sDfeeMMdHx/vzs3NLZXzdPL/gZf7Wz0vL89dsWJF97x589zhft55eXnuW265xf2nP/3J3a9fP3ePHj3cwVbc8545c6a7fv367pycnMt6XVrCo4heudm+fbt1U/GIiYmx+3rlDAhHnu5qVapUCfWpRHRZ1+2Fj1d6JdgJdUNJ6zbtPlmnTh1rNejRo4e17EQiJ3+3JZWammrdBLW14qOPPgr16ZRa3ReN322k0O9Iu3K3atXKu02/S627Nm/eXOTjvv32W3nooYesd0i1atXEKeft7+9beyOVKVMm4Ofo5P8DA/FbXf9GtCdNMH83lfS8J0yYYD2VSrNXRKDP+91335W2bdtaz6WUlBRp0qSJPPfcc5Kfn1+s1yYIjyL/+c9/7A9E/2AK0/tOGC+H6FNQUGBdZ2+99Var5FB6ZV23O7VuKMn71bGJf/7zn+Wdd96Rv/71r/a3dsstt8iXX34pkaao7zY7O1u+++47iSQaeM+aNUv+/ve/26IXWNq1a2ddDCOx7nNyuY12+h2dP0xCA1INnC72/aWnp1tdpRcOnXTe59fZEydOvOSu99H0f2AgfquPHDnS8micf1Eh3M77ww8/lNmzZ1tX7lApyXl/9tln1g1dH6fjwJ9++mn57W9/K88++2yxXjvwl58AIED0KqOOx9KKGggkvYqti4f+qG3UqJH8/ve/tx+HcCa9uKJL4e/1wIEDMm3aNPnLX/4iTkHd51yjRo2S559//qLH7N27t0TPrS1wa9eutTHLTjrvwvTin+aoaNy4sYwbN+6ynw++pkyZYknOdJx4OCcbPXXqlPTp08cC8CuvvFKcdpFUL0T94Q9/kNjYWGnZsqWNv//Nb35j48ovFUF4FNE/cv1jOX78uM92vR+KLk3AxQwZMsSb8KVmzZp8WKVc1nW7U+uGQNRtcXFx0qJFC9m/f79EmqK+W+0KqpmKI91NN93kqAt5xan7nFxuI9UTTzwhjzzyyEWPqV+/vn1H5yd+ysvLs0ziRX1/GoDrRSXtDl5YWlqaJbXSwCscz7tw4HX33XfbDACLFy+2erc0OPn/wMv5/0wT32kQvnr1amnWrJmE83kfOHDAEpvp7BeFg1tPzwpNdnbNNdeE3Xl7elzp364+zkMv4mvLuXZvj4+Pv6TXpjt6FNE/Cr1as2bNGp8/eL1fuEUICCXNv6Q/QvU/aP3BUa9ePb6QIJR13V74eKWZb51QNwSibtNuZTt37rT/XCONk7/bQNCM8E74XktS90X7dxuOrrrqKpvm6mKL1ln6Hek0Rzoe1UO/d627dIqkolqr//nPf9rftGdR2tNjzpw5YXvenhZwzditz6Et+qXZSuvk/wNL+v/Z1KlTrReXZp0vPF4/XM+7YcOG9n9u4b/lH//4x95ZPHQoUTiet9JhQnrB3nPRQP373/+2/2cuNQA3l5XWDY6zcOFCd0JCgnvu3LmW4XLgwIHuypUru48dOxbqU3OsU6dOWaZSXbRIvfjii7b+xRdfhPrUHGnQoEGWtXrdunWW+dWzfPvtt6E+tYgq63369HGPGjXKe/xHH33kLlOmjPuFF15w79271/3MM8+44+Li3Dt37nRH4vsdP368+/3333cfOHDAvX37dvcDDzzgTkxMdO/evdvt9DpH36e+X4/PPvvMXa5cOfeIESPsu50+fbo7NjbWvWLFCnekvddp06a53377bfenn35qf7uPP/64OyYmxr169Wp3JNR9kVZuo93dd9/tbtGihXvz5s3uDz/80H3ddde5H3zwQe/+L7/80t2gQQPbH06zshT3vLOysizTeNOmTd379+/3+fvW7Nilwcn/Bxb33KdMmWKZ5t98802fz1brz3A+7/OFKjt6cc/70KFDln1+yJAh7n379rmXLl3qTk5Odj/77LPFel2C8Cj06quvumvXrm0FVtPyb9q0KdSn5GieKXHOX7QyQfH5+yx10SmlELiyfscdd1zwN6pTxlx//fV2vE5ptWzZsoh9v8OGDfMem5KS4u7atav7448/dkdCnaO3+n7Pf0xqaqq9X51axSnlqbjv9fnnn3dfc801dkGlSpUq7nbt2rnXrl3rjpS6LxLLbTT75ptvLHitUKGCOykpyf3oo4/6BE4HDx60vwEtB+EUhBf3vIsqx7rosaXFyf8HFufc69Sp4/ez1QsJ4Xze4RKEl+S8N27caBeWNHjX/1MnTZpU7AtKLv3n8hryAQAAAADApWBMOAAAAAAAQUIQDgAAAABAkBCEAwAAAAAQJAThAAAAAAAECUE4AAAAAABBQhAOAAAAAECQEIQDAAAAABAkBOEAAAAAAAQJQTgAAAAAAEFCEA4AAAAAQJAQhAMAAAAAECQE4QAAAAAASHD8/0AUzl9kCFWHAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "for i, v in enumerate([\"alpha\", \"inv_sigma_h\", \"rho\"]):\n",
+    "    ax[i].hist(posterior_samples_stacked[v].ravel(), label=v)\n",
+    "    ax[i].set_title(v)\n",
+    "    ax[i].axvline(\n",
+    "        true_rho\n",
+    "        if v == \"rho\"\n",
+    "        else true_inv_sigma_h\n",
+    "        if v == \"inv_sigma_h\"\n",
+    "        else true_alpha,\n",
+    "        color=\"k\",\n",
+    "        ls=\"--\",\n",
+    "    )\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "107f5ddd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From eb8e4f90c8fdd8c5d2da4df02e4ae4fc52215763 Mon Sep 17 00:00:00 2001
From: Dan Waxman <dan.waxman1@gmail.com>
Date: Mon, 2 Mar 2026 10:04:35 -0500
Subject: [PATCH 4/5] Slightly longer run

---
 docs/deep_dives/particle_hmc.ipynb | 94 ++++++++++++++++--------------
 1 file changed, 51 insertions(+), 43 deletions(-)

diff --git a/docs/deep_dives/particle_hmc.ipynb b/docs/deep_dives/particle_hmc.ipynb
index 57d6a975..af225131 100644
--- a/docs/deep_dives/particle_hmc.ipynb
+++ b/docs/deep_dives/particle_hmc.ipynb
@@ -194,7 +194,7 @@
     "\n",
     "\n",
     "def data_conditioned_model(obs_times, obs_values):\n",
-    "    filter_config = MarginalPFConfig(n_particles=100, resampling_method=PFResamplingConfig(base_method=\"systematic\", differential_method=\"stop_gradient\"))\n",
+    "    filter_config = MarginalPFConfig(n_particles=200, resampling_method=PFResamplingConfig(base_method=\"systematic\", differential_method=\"stop_gradient\"))\n",
     "    # filter_config = EKFConfig()\n",
     "    # filter_config = PFConfig(n_particles=1_000, resampling_method=PFResamplingConfig(base_method=\"systematic\", differential_method=\"stop_gradient\"))\n",
     "    with Filter(filter_config=filter_config):\n",
@@ -241,7 +241,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -344,7 +344,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1000/1000 [02:55<00:00,  5.69it/s]\n"
+      "100%|██████████| 1000/1000 [05:48<00:00,  2.87it/s]\n"
      ]
     }
    ],
@@ -365,7 +365,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1000/1000 [02:55<00:00,  5.70it/s]\n"
+      "100%|██████████| 1000/1000 [05:53<00:00,  2.83it/s]\n"
      ]
     }
    ],
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "id": "f680d58c",
    "metadata": {},
    "outputs": [
@@ -385,6 +385,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/danwaxman/Documents/dynestyx/.venv/lib/python3.12/site-packages/arviz/__init__.py:50: FutureWarning: \n",
+      "ArviZ is undergoing a major refactor to improve flexibility and extensibility while maintaining a user-friendly interface.\n",
+      "Some upcoming changes may be backward incompatible.\n",
+      "For details and migration guidance, visit: https://python.arviz.org/en/latest/user_guide/migration_guide.html\n",
+      "  warn(\n",
       "/Users/danwaxman/Documents/dynestyx/dynestyx/inference/filters.py:296: UserWarning: PFJax stop-gradient mode requires resampling at every step for its correction term to be valid. Overriding ess_threshold_ratio from 0.7 to 1.0.\n",
       "  run_pfjax_discrete(\n"
      ]
@@ -424,39 +429,39 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>alpha</th>\n",
-       "      <td>1.576</td>\n",
-       "      <td>0.654</td>\n",
-       "      <td>-0.232</td>\n",
-       "      <td>2.022</td>\n",
-       "      <td>0.263</td>\n",
-       "      <td>0.337</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>1.11</td>\n",
+       "      <td>1.829</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>1.669</td>\n",
+       "      <td>1.981</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>202.0</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>1.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>inv_sigma_h</th>\n",
-       "      <td>1.113</td>\n",
-       "      <td>0.520</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>1.729</td>\n",
-       "      <td>0.230</td>\n",
-       "      <td>0.115</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>1.26</td>\n",
+       "      <td>1.361</td>\n",
+       "      <td>0.239</td>\n",
+       "      <td>0.933</td>\n",
+       "      <td>1.793</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>171.0</td>\n",
+       "      <td>1.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rho</th>\n",
-       "      <td>-0.155</td>\n",
-       "      <td>0.228</td>\n",
-       "      <td>-0.442</td>\n",
-       "      <td>0.445</td>\n",
-       "      <td>0.063</td>\n",
-       "      <td>0.069</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>1.06</td>\n",
+       "      <td>-0.219</td>\n",
+       "      <td>0.108</td>\n",
+       "      <td>-0.419</td>\n",
+       "      <td>-0.016</td>\n",
+       "      <td>0.018</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1.05</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -464,17 +469,17 @@
       ],
       "text/plain": [
        "              mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
-       "alpha        1.576  0.654  -0.232    2.022      0.263    0.337      12.0   \n",
-       "inv_sigma_h  1.113  0.520   0.020    1.729      0.230    0.115       6.0   \n",
-       "rho         -0.155  0.228  -0.442    0.445      0.063    0.069      18.0   \n",
+       "alpha        1.829  0.085   1.669    1.981      0.006    0.003     202.0   \n",
+       "inv_sigma_h  1.361  0.239   0.933    1.793      0.042    0.011      34.0   \n",
+       "rho         -0.219  0.108  -0.419   -0.016      0.018    0.009      35.0   \n",
        "\n",
        "             ess_tail  r_hat  \n",
-       "alpha            11.0   1.11  \n",
-       "inv_sigma_h      15.0   1.26  \n",
-       "rho              11.0   1.06  "
+       "alpha           371.0   1.02  \n",
+       "inv_sigma_h     171.0   1.08  \n",
+       "rho              45.0   1.05  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -514,7 +519,7 @@
     "postprocessed_2 = jax.jit(jax.vmap(_postprocess_single))(jnp.arange(num_sample))\n",
     "posterior_samples_2 = {k: v[None, ...] for k, v in postprocessed_2.items()}\n",
     "\n",
-    "num_warmup = 250\n",
+    "num_warmup = 500\n",
     "posterior_samples_stacked = {k: jnp.stack([posterior_samples_1[k][..., num_warmup:], posterior_samples_2[k][..., num_warmup:]], axis=0).squeeze(1) for k in [\"alpha\", \"inv_sigma_h\", \"rho\"]}\n",
     "\n",
     "az.summary(posterior_samples_stacked, var_names=[\"alpha\", \"inv_sigma_h\", \"rho\"])\n"
@@ -522,13 +527,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "id": "323b4be3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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BzvDDT+luPro08WQSchK0A4AFaeBdU7I/XxIB6qj74sWL/VwzAACAH3kCdt21RncWsuNuEfWh12qazd+TY0hnStYXQTsAn2knfNppp3nLAEIT72UAsKdA9+86Jd4TsJ+YXwciCQkJphk0cNc2qu9UeYJ2AD7TT0937txJiwEhjvcyANhToPt3zxp2/bmomqdttK3qG7STPR4AAAAAUG/MwGzYtiFoBwAAAADAooI6PX716tXyxBNPmH2E9+3bJ2+88Ybk5OR4pw+MHz9e3n77bcnNzZXU1FQZMGCAPPbYY5W2Kzp06JDccccd8s9//tObHflPf/qTJCcnB/F/BtjTsWPH5IILLvC+fz3rdACEFt7LAGBPVurfA5lRPu1/GdpPxqpVq8zOPYcPHzbfz0qCGrQXFRVJ586d5aabbpIrr7yy0mOaaU/3IJ4wYYI5Rxvvrrvukl//+tdmKySPIUOGmID/vffeM4H+jTfeKLfeeisZk4EG4HK5vO8/LQNoWAUFBebvYV3WzaWkpNR6Hu9lAPBfoOmPgNFfrNK/aztmte8ghUcLAvLzkhulyLatWyzze7BV0D5w4EBzVEVH1jUQr2jOnDnSo0cP+fbbb6VNmzayefNmWbZsmaxfv166detmzpk9e7ZceumlMn369Eoj8kB12mV1kL17vquxgVqe2kpyt22hEQEENGBvm9lODh/6wefnNG6SLjt35PoUuAMA/BNo2j1grA/94EPbsc/I6ZLctGFjssKDe+XDOfean3kyv4OysjKxqpDKHn/kyBGzkN8zXWHt2rWm7AnYlU6h12ny69atkyuuuKLK71NaWmqOihdGCF8asOfMXF7jOUtGDQhYfQBA6Qi7BuzZExZKXKPGtTZK6dHD8u4j15vnEbQDQN3VJ9D0V8BoV9qOKRltxYr69u0rnTp1kujoaPnb3/4mP/xw/ENyXbp9//33y6ZNm6RLly4yf/586dChg/d5zzzzjBkg3r17t2RmZpol3dddd12D1jVkEtGVlJSYxrv22mu9FyP6aZjud1eRNnqTJk3MY9WZOnWqGcn3HK1bt27w+gMAUB8asCekptd6+BLYAwB8DzR9ORp6FBkN68UXX5TY2Fj5+OOPZd68eea+Bx98UJ588kmzzEBjS13K7aE52HTJ9j333CNfffWVDB8+3CzPfv/99xu0niERtOta9auvvlrcbrf5ZONkjRs3zozaew79lAQAAAAAED6ysrJk2rRpZiTdM5o+ZcoUufDCC+XMM8+UsWPHypo1a8wAstIR9htuuEFuu+02ad++vYwePdrkZtP7wzpo9wTsu3btMmvcK0750ykoeXl5lc4vLy83GeVrmp4SFxdnvk/FAwAAAAAQPrp27fqT+8455xxvuUWLFuarJ+bUnGrnn39+pfP1tt4ftmvaPQH7tm3bzJSD9PT0So/36tXLrB/RdQeeBl+5cqXJlNizZ88g1Rqwt1NOOSXYVQDgB7yXAcCe6N99l5SU9JP7YmJivGXNp2aFXZOCGrQXFhbK9u3bvbd37NghGzduNGvS9VONq666ymz7tnTpUnE6nd516vq4rj3o2LGjXHLJJTJs2DCzBkGD/JEjR8o111xD5niggTq2gwcP0rZAiOO9DAD2RP/esDT+1PXvQ4cO9d6nt3UqvW2Ddl3crxvYe+iaAKWNMGnSJHnzzTfNbc3aV5GOumu2P7Vo0SITqPfv399kjR88eLDMmjUroP8PAAAAAIC9jRkzxswEP/fcc82uZf/85z/l9ddfl+XLa96JKqSDdg28NblcdWp6zENH3RcvXuznmgEAAAAAToZuiWeHn+GRk5Mjf/rTn0ziOc0ir1u+6ZZwngHlsFzTDsBajh07JgMHDjTld955RxISEoJdJQD1wHsZAOzJKv17WlqaJDdKMXvYB0JyoxTzM+ti1apVtQ4o64zvE+8bMWKEOQKJoB2AzzQJxwcffOAtAwhNvJcBwJ6s0r/rTl7btm4xScMDIS0trcbdw0IdQTsAAAAAwK80iLZzIB1Ilt+nHQAAAACAcEXQDgAAAACARTE9HvBBmaNc4hOTaj2v5amtJHfbFtoUAAAAgF8QtAM+cLuckjOzcobJqiwZNYD2BAAAsID9+/fXKRFabm5ug9bHzkhQ3LBtQ9AOoE4SExNpMcAGeC8DsHvAntW+gxQeLajzcx1lDgllgezfY2NjJTIyUvbu3StNmzY1tyMiIgL2861Mt4orKyuTgwcPmjbStqkvgnYAPktKSpKioiJaDAhxvJcB2J2OsGvA3mfkdElu2tKn5+Rt3SgbFk2TcqdTQlWg+3cNRjMzM2Xfvn0mcEfVH6K0adPGtFV9EbQDAAAAsCUN2FMy2vp0buHBPQ1eHzvSEWQNSsvLy8UZwh94NISoqCiJjo4+6dkHBO0AAAAAgHrToDQmJsYc8D+2fAPgs5KSEhk0aJA5tAwgNPFeBgB7on+3J0baAfhMpzy9/fbb3jKA0MR7GQDsif7dnhhpBwAAQEhZvXq1XHbZZdKyZUszLXfJkiU/ydo8ceJEadGihSQkJMiAAQNk27Ztlc45dOiQDBkyRFJSUiQtLU1uvvlmKSwsDPD/BABqR9AOAACAkKLZsTt37ixz586t8vFp06bJrFmzZN68ebJu3TqTUTs7O7vS0i4N2L/++mt57733ZOnSpeaDgFtvvTWA/wsA8A3T4wEACGMHDhzweU9fPU9HJYFgGzhwoDmqoqPsM2fOlPHjx8vll19u7lu4cKE0b97cjMhfc801snnzZlm2bJmsX79eunXrZs6ZPXu2XHrppTJ9+nQzgg8AVkHQDgBAGDv99NN9Prdxk3TZuSOXwB2WtmPHDtm/f7+ZEu+RmpoqPXv2lLVr15qgXb/qlHhPwK70fN1HWUfmr7jiiiDVHgB+iqAdAIAw1v/+5yQpPaPW80qPHpZ3H7leiouLCdphaRqwKx1Zr0hvex7Tr82aNav0uO6l3KRJE+85JyotLTWHR0FBQQPUHgB+iqAdAIAwFtcoTRJS04NdDcDypk6dKpMnTw52NQCEIRLRAfCZJvLRtYJ6aBlAaNL37759+0w5OjY+2NUB/CojI8Obr6Eive15TL/m5eVVery8vNxklPecc6Jx48bJkSNHvMfu3bv5zcFyuFazJ4J2AAAA2EZmZqYJvFesWFFpKruuVe/Vq5e5rV/z8/Nlw4YN3nNWrlwpLpfLrH2vSlxcnFkaUvEAgEBgejwAAABCiu6nvn379krJ5zZu3GjWpLdp00ZGjRoljz76qGRlZZkgfsKECSYjfE5Ojjm/Y8eOcskll8iwYcPMtnAOh0NGjhxpktSROR6A1RC0A/CZ7m973XXXmfJf//pXiY9nWi0Qqu9lDVaU01EW7OoAdfbZZ5/JRRdd5L09evRo83Xo0KGyYMECue+++8xe7rrvuo6o9+7d22zxVvHv1qJFi0yg3r9/f5M1fvDgwWZvd6A+cnNz63S+7l5Q3VKMk8G1mj0RtAPwmdPplL///e+mrBdFAEL3vbx06VJTdrtdwa4OUGd9+/Y1+VWqExERIQ8//LA5qqOj8osXL6b1cVJKj+brC04GDRpUp+clN0qRbVu3+D1w51rNngjaAQAAAKAeHCVF+umn9Bg2RdJbZfr0nMKDe+XDOfeaWSANMdoO+yERHQBY0OrVq+Wyyy4zayt1xGjJkiWVHtcRpokTJ0qLFi0kISFBBgwYINu2bat0jmZBHjJkiEmWpNPwbr75ZrMOFAAA+FdiegtJyWjr05HctCXNjzohaAcAC9K1mJ07d5a5c+dW+fi0adPM2ktNoKQZkXWLl+zsbLOWzUMD9q+//lree+89MxVaPwjQ9Z2wN93Gav/+/TUeJ26FBQAArIvp8QBgQQMHDjRHVXSUfebMmTJ+/Hi5/PLLzX0LFy6U5s2bmxF5zX68efNmk3Rp/fr10q1bN3PO7Nmz5dJLL5Xp06eTHdmGHCXFIhGR5sOeunCVOxusTgAA4OQRtANAiNGtjXS0VKfEe6Smppq9hdeuXWuCdv2qU+I9AbvS8zVDso7MX3HFFVV+79LSUnNU3NsYocHpKBVxu6Tf2OcluUmzGs8tLyuRt8f/5vjz3ATtAABYGUE7AIQYDdiVjqxXpLc9j+nXZs0qB27R0dEmW7LnnKpMnTpVJk+e3CD1RmDENUqThNT0Gs8pLz3GrwMAgBBB0A7AZ4mJid5EZlqG/YwbN86737FnpL1169ZBrRP8Lyo2Xvrd/5ysfHyYRMXE0cQAYONrNf2wXjPVW2EfedQPQTsAn2kWc014huDy/BHVZGKaPd5Db3fp0sV7jiYkq6i8vNxklK/pj3BcXJw5YP/3cnRsnLcMALDntZoG7FntO0jh0QJL7COP+iFoB4AQk5mZaf6Irlixwhuk64i4rlUfMWKEud2rVy/zqfqGDRuka9eu5r6VK1eKy+Uya98BAID96bWABux9Rk73eas59pG3HoJ2AD7TBGXDhw835T//+c+MyDYgndq2ffv2SsnnNm7caNakt2nTRkaNGiWPPvqoZGVlmSB+woQJJiN8Tk6OOb9jx45yySWXyLBhw8y2cA6HQ0aOHGmS1Ol5CG9OR5l89X/PHi+XO4JdHQBAA12reWjArnvEIzQRtAPwmU6vfvHFF01Z9w9nGnXD+eyzz+Siiy7y3vasMx86dKgsWLBA7rvvPrOXu+67rp+i9+7d22zxFh8f733OokWLTKDev39/kzV+8ODBZm93wO1yyt5/f+QtAwDsea0GeyBoBwAL6tu3r9mPvaY1aw8//LA5qqOj8osXL26gGgIAACAQIgPyUwAAAAAAQGgF7atXr5bLLrvMrK/UUaMlS5ZUelxHmSZOnGiyIyckJMiAAQNk27Ztlc7RTMhDhgyRlJQUszXBzTff7N3mAAAAAACAUBbUoF3XY3bu3Lna9RbTpk0z6y81iZJmRdbtC7Kzs6WkpMR7jgbsX3/9tbz33nuydOlS80GArvEEAAAAACDUBXVN+8CBA81RFR1lnzlzpowfP14uv/xyc9/ChQulefPmZkReMyBv3rzZJF5av369dOvWzZwze/ZsufTSS2X69OlkSAYAAAAAhDTLrmnX7Y32799vpsR7pKammv2F165da27rV50S7wnYlZ6vWZJ1ZB4AAAAAgFBm2aBdA3alI+sV6W3PY/q1WbNmlR6Pjo42GZM951S3f2FBQUGlA0DtEhMTJS8vzxxaBhCaomLjpe/oOcfLMXHBrg4AwE+4VrMnywbtDWnq1Klm1N5ztG7dOthVAkKCJoxs2rSpObQMIDTp+zc2KcVbBgDYA9dq9mTZoD0jI8N8PXDgQKX79bbnMf2qI34VlZeXm4zynnOqMm7cODly5Ij32L17d4P8HwAAAAAAsGXQnpmZaQLvFStWeO/Taey6Vr1Xr17mtn7Nz8+XDRs2eM9ZuXKluFwus/a9OnFxcWaLuIoHgNrp0pLbb7/dHFoGEJqcjjLZ/M6Lx8vljmBXBwDgJ1yr2VNQs8frfurbt2+vlHxu48aNZk16mzZtZNSoUfLoo49KVlaWCeInTJhgMsLn5OSY8zt27CiXXHKJDBs2zGwL53A4ZOTIkSazvJ4HwL90JsvTTz/t3ZJRPwADEHrcLqfs/myFtwwAsOe1GuwhqEH7Z599JhdddJH39ujRo83XoUOHyoIFC+S+++4ze7nrvus6ot67d2+zxVt8fLz3OYsWLTKBev/+/U3W+MGDB5u93QEAAAAACHVBDdr79u1r9mOvKZHCww8/bI7q6Kj84sWLG6iGAAAAAAAEj2XXtAMAAAAAEO6COtIO1Fe7rA6yd893NZ7T8tRWkrttC40MAAAAy8nNza3zc9LS0mrcJQv2RNCOkKQBe87M5TWes2TUgIDVBwAAAPBF6dF8XQcsgwYNqnODJTdKkW1btxC4hxmCdgAAAAAIEEdJkYjbLT2GTZH0Vpk+P6/w4F75cM69JkE3o+3hhaAdgM8SEhLM1oyeMoDQFBUTJ33ueFI+nH2PREXHBrs6ABCWEtNbSEpGW79+T67V7ImgHYDPdFvFtm39+8cFQOBFREZKQlpTbxkAYA9cq9kTQTsAAAFSUFAgxcXFPp2bl5fX4PUBAADWR9AOwGdlZWXy4IMPmvKUKVMkNpZptUBdAva2me3k8KEf6tRornKn3xvZWe6QLctf+t/3d/j9+wMArHGtBnsgaAfgM4fDIdOnTzflSZMmEbQDdaAj7BqwZ09YKHGNGtd6fsG+HbLqqTvF6fZ/0O52lsuute+YsstVt+9flxkAiYmJkpKSUuf6AQD8c60GeyBoBwAggDRgT0hNr/W8kqOHxUocJcW6AF46d+7s83MaN0mXnTtyCdwBADgJBO0AAKBWTkepiNsl/cY+L8lNmtV6funRw/LuI9ebGQaMtgMAUH8E7QAAwGdxjdJ8mikAAGgYubm51T5WMdnpli1bZP/+/fwabICgHQAAAAAsrvRovkhEhAwaNMin87t27eotO8pIOhrKCNoBAECD8TVxHUnrAKBmjpIiEbdbegybIumtMqs8p7ysRN6bcpMp//LBF+TQzm9kw6JpUu70f1JTBA5BOwAACHriOpLWAaiJTvPOz8/3yxTyUJeY3kJSMtpW+Vh56TFvOaX5aVJypG7bjMKaCNoB+CwhIUG++uorbxlAaIqKiZPzhv9R1vz5AYmKjg164jqS1gGoLWDPat9BCo8W1Lmhwm1auPbvlzz0N28Z9kDQDsBnkZGRctZZZ9FiQIiLiIyU5GatvOWGROI6ACdLR9g1YO8zcrokN23p03Pytm4My2nh2qentmwX7GrAzwjaAQAAAFieBuzVTQs/UeHBPQ1eHyBQGvbjdQC2UlZWJpMmTTKHlgGEJme5Q7Z/8Lopu8rDa+oowoPT6ZQJEyZIZmamWc51+umnyyOPPCJut9t7jpYnTpwoLVq0MOcMGDBAtm3bFtR6A/7o37/651/MoWXYA0E7AJ85HA6ZPHmyObQMIDS5neWSu3qJKbtc4TV1FOHh8ccfl2eeeUbmzJkjmzdvNrenTZsms2fP9p6jt2fNmiXz5s2TdevWSVJSkmRnZ0tJSUlQ6w6cbP/+9dIXzKFl2APT4wEAAGAra9askcsvv9y7n3Xbtm3lpZdekk8//dQ7yj5z5kwZP368OU8tXLhQmjdvLkuWLJFrrrkmqPUHgIoI2gEAQEjt6a7Y1x01Oe+88+TZZ5+VrVu3Svv27eXLL7+Ujz76SGbMmGEe37Fjh8lIrlPiPVJTU6Vnz56ydu1agnYAlkLQDgAAQmpPd8W+7qjJ2LFjpaCgQM444wyJiooya9ynTJkiQ4YMMY9rwK50ZL0ive157ESlpaXm8NDvDwCBQNAOAACCqi57uiv2dUdtXn31VVm0aJEsXrzYbFW6ceNGGTVqlLRs2VKGDh1arwacOnWqyekCAIFG0A4AACyBPd3hL2PGjDGj7Z616Weffbbs2rXLBN4atGdkZJj7Dxw4YLLHe+jtLl26VPk9x40bJ6NHj6400t66dWt+aQAaHNnjgSBol9VB4hOTajz0HAAAUHfFxcUSGVn5MlenybtcLlPWreA0cF+xYkWlIFyzyPfq1avK7xkXFycpKSmVDgAIBEbagSDYu+c7yZm5vMZzloz6MTmOVcTHx3sz72oZQGiKjImVnjdPknXPT5Ko6JhgVwfwu8suu8ysYW/Tpo2ZHv/FF1+YJHQ33XSTeTwiIsJMl3/00UclKyvLBPG6r7tOn8/JyeE3gpDu3weM+4u3DHsgaAfgMx2l6N69Oy0GhLjIyChJbdnOlCMio4JdHcDvdD92DcJvu+02syuBBuPDhw+XiRMnes+57777pKioSG699VbJz8+X3r17y7Jly/hQGiHfv6e3PTPY1YCfMT0eAEKUZkPWi1IdIUpISJDTTz9dHnnkEbP/sIeW9SJV12zqObq90bZt24JabwBoaI0aNTL7sOs69mPHjsl///tfM6oeG/vjyKOOtj/88MMmW3xJSYksX77cbA8HAFZD0A7AZ2VlZfLEE0+YQ8sIrscff1yeeeYZmTNnjmzevNncnjZtmhlh8tDbs2bNknnz5pm1mklJSZKdnW0uUBG+nOUO2bHmLVN2lTuCXR0AgB/792/eXWQOLcMemB4PwGcOh8NMJ1Q65bDiiAUCb82aNXL55ZfLoEGDzO22bdvKSy+95M07oKPsOtI0fvx4c55auHCh2Yd4yZIl3qzKCD9uZ7lsW/GKKbtczmBXBwDgx/79y9fnmvLP+l5Ju9oEI+0AEKLOO+88k/l469at5vaXX34pH330kQwcONDc3rFjh5n2qVPiPVJTU6Vnz56ydu3aKr9naWmpyaBc8QAAAEDwMNIOACFK9yDWoPqMM84wSQJ1jbtmSx4yZIh5XAN2pSPrFeltz2Mn0j2MJ0+eHIDaAwAAwBeMtANAiHr11Vdl0aJFsnjxYvn888/lxRdflOnTp5uv9TVu3Dg5cuSI99i9e7df6wwAAIC6YaQdAELUmDFjzGi7Z2362WefbTIl62j50KFDJSMjw9x/4MABkz3eQ2936dKlyu8ZFxdnDgAAAFgDI+0AEKKKi4slMrJyN67T5F0ulynrVnAauOu6dw+dTq9Z5Hv16hXw+gIAAKDuGGkHgBB12WWXmTXsbdq0kbPOOku++OILmTFjhtx0003ePYhHjRpl9ibOysoyQbzu696yZUvJyckJdvUBAADgA4J22FaZo1ziE5NqP4/9xn0WHx8v77//vreM4NL92DUI1+338vLyTDA+fPhwmThxovcc3aKvqKhIbr31VsnPz5fevXvLsmXL+P2FuciYWOl23Tj57K9TJSo6JtjVAQD4sX+/aPQcbxn2YOmgXTMhT5o0Sf72t7+ZTMd6QXrDDTeYPYd1BMmzD/FDDz0kzz33nLkgPf/88+WZZ54xo0oIb26XU3Jmrqr1vFdG9AlIfexAp1737ds32NXA/zRq1Mjsw65HdbSvfPjhh80BeERGRkmTth2Pv0Yio2gYALBR/96sw8+DXQ2E05r2xx9/3ATgc+bMkc2bN5vb06ZNM6NLHnp71qxZMm/ePLNOMykpSbKzs6WkpCSodQcAAAAAwNYj7WvWrJHLL79cBg0aZG63bdtWXnrpJfn000+9o+w6wqQj73qeWrhwodmDeMmSJd6MygD8w+FwyLPPPmvKOt06JoZptUAocjnL5dv1y71lAIA9aJ/+39X/Z8qnX3A8PkLos/RI+3nnnWeyHm/dutXc/vLLL+Wjjz6SgQMHmts7duww0+YHDBjgfU5qaqr07NlT1q5dW+33LS0tNRmUKx4AxKf1/yNHjjQHuQCA0OUqd8g3yxYeLxO0A4Ct+vfPX37SHFqGPVh6pF33H9aA+owzzjBraXWNu2ZKHjJkiHlcA3alI+sV6W3PY1XRPYwnT57cwLUHAAAAAMDGI+2vvvqqLFq0SBYvXiyff/65vPjiizJ9+nTz9WSMGzdOjhw54j12797ttzoDAAAAABAWI+1jxowxo+2etelnn3227Nq1y4yUDx06VDIyMsz9Bw4ckBYtWnifp7e7dOlS7feNi4szBwAAAAAAVmbpkfbi4mKJjKxcRZ0m73K5TDkzM9ME7rru3UOn02sW+V69egW8vgAAAAAAhM1I+2WXXWbWsLdp00bOOuss+eKLL2TGjBly0003efcfHjVqlDz66KNmX3YN4idMmGD2c8/JyQl29QEAAAAAsG/QrvuxaxB+2223SV5engnGhw8fLhMnTvSec99990lRUZHZfio/P1969+4ty5Ytk/j4+KDWHeGpzFEu8YlJtZ9XVhaQ+gAAAAAIbZYO2hs1amT2YdejOjra/vDDD5sDCDa3yyk5M1fVet4rI/pIKNJcEEuXLvWWAYSmyOgYOfea0fLFyzMkMiom2NUBAPixf+8z8glvGfZg6aAdgLVER0fLoEGDgl0NACcpMipammYdT9gaGRVFewKAjfr3lmefH+xqwM8I2oEQn2rf8tRWkrttS0DqBAAAACCwCNqBEJ9qv2TUAAkUh8MhixYtMuUhQ4ZITAzTroBQ5HKWy54vP/SWAQD2oH36rnXvmvJpPbODXR34CUE7AJ9pAr0bb7zRlH/zm98QtAMhylXukK/ffO54maAdAGzVv3/64hRTbt21X7Crg3DYpx0AAAAAgHBG0A4AAAAAgEURtAMAAAAAYFEE7QAAAAAAWBSJ6GAp7bI6yN493/mUEA0AAAAA7I6gHZaiAXvOzOW1nvfKiD4BqQ8AAAAABBNBOwCfxcXFyauvvuotAwhNkdExcs7gkfLvf8yRyKiYYFcHAODH/v28Wx/1lmEPBO0AfO8woqPN/uwAQltkVLRknNlD/v0PLUcFuzoAAD/27+zPbj8kogMAAAAAwKIYaQfgs/LycnnjjTdM+YorrjAj7wBCj8tZLvs3ffq/sjPY1QEA+LF/37NxtSmf2uUC2tUmuOIG4LPS0lK5+uqrTbmwsJCgHQhRrnKHWc9uyk5HsKsDAPBj/77m2fGmPHjWCto1nKfHt2vXTn744Yef3J+fn28eA4BwRf8IAPSPABD0oH3nzp3irGI6nY7C7dmzxx/1AoCQRP8IAPSPABC06fFvvvmmt/zuu+9Kamqq97YG8StWrJC2bdv6tYIAECrefvtt85X+EQDoHwEgKEF7Tk6O+RoRESFDhw6t9FhMTIwJ2J988km/VQ4AQsnvfvc7+kcAoH8EgOAF7S6Xy3zNzMyU9evXyymnnOLf2gBACNO8Hp07d6Z/BAD6RwAIbvb4HTt2+K8GAGAj9I8AQP8IAJbY8k3Xr+uRl5fnHYH3eOGFF/xRNwAWExsbK/Pnz/eWUTX6R1hdZHSMnPXrYfL1m89JZBS7vyJw6B+Bhu/fewx90FuGPdTrL/XkyZPl4Ycflm7dukmLFi3MGk4A9qe5K2644YZgV8PS6B8RCjRQP7VzH4J2BBT9IxCY/j3zvEE0tc3UK2ifN2+eLFiwQK677jr/1wgAQhj9IwBYo3/UbYjvv/9+eeedd6S4uFh+9rOfmdliOuik3G63PPTQQ/Lcc8+ZnCTnn3++PPPMM5KVlcWvEEDo79NeVlYm5513nv9rA8DSysvL5a233jKHlvFT9I8IBS5nuRzctvF/ZWewq4MwEcj+8fDhwyYI1xliGrRv2rTJ7HDUuHFj7znTpk2TWbNmmQ8T1q1bJ0lJSZKdnS0lJSUBqSPQUP373v98bA4tI4yD9ltuuUUWL17s/9oAsLTS0lL51a9+ZQ4t46foHxEKXOUO+eLlGcfLTkewq4MwEcj+8fHHH5fWrVubkfUePXqYnY8uvvhiOf30072j7DNnzpTx48fL5ZdfLuecc44sXLhQ9u7dK0uWLAlIHYGG6t8/nDPGHFpGGE+P108gn332WVm+fLnp5PRTzIpmzDh+IQAA4Yb+EQCC3z+++eabZtT8N7/5jXzwwQdy6qmnym233SbDhg3z7vSxf/9+GTBggPc5qamp0rNnT1m7dq1cc801P/me+mF1xQ+sCwoK/FZfAPB70P7vf/9bunTpYspfffVVpcdISgcgnNE/AkDw+8fc3FyzPn306NHywAMPyPr16+XOO+80O58MHTrUBOyqefPmlZ6ntz2PnWjq1KkmmR4AhETQ/v777/u/JgBgA/SPQODotrO+SkxMlJSUlAatD6zTP+p2xJpw7o9//KO5fe6555oPCnT9ugbt9TFu3DjzIUDFkXadgg8ADY3NWQEAQEhxlBSLRERK586dfX5O4ybpsnNHLoF7mNAtic8888xK93Xs2FH+8Y9/mHJGRob5euDAAXOuh972zAY4UVxcnDkAICSC9osuuqjGaUwrV648mToBQMgKdP/IlkYIR05HqYjbJf3GPi/JTZrVen7p0cPy7iPXm22/GG0Pj/5RM8dv2bKl0n1bt26V0047zZQ1MZ0G7itWrPAG6TpyrlnkR4wY4bd6AEDQgvYTP4F0OByyceNGM+2ovlOOAMAOAtk/erY00gth3dKoadOmsm3btiq3NHrxxRfNReqECRNMcibd/ig+Pt6v9QlXeqGvwaA/p3LDN3GN0iQhNZ3mChGB7B/vvvtus72cTo+/+uqr5dNPPzVJ8PRQ+uHBqFGj5NFHHzX7snv6x5YtW0pOTo5f6wIAQQnan3rqqSrvnzRpkhQWFp5snQBYlCbwmTNnjreM4PaPFbc08tALT48TtzRSuqWRJlrSLY2qyo6MugfsbTPbyeFDP/j8HFd58PdFj4yOkTMuuV6+WbZQIqNYKQf79Y/du3eXN954w6xDf/jhh03fqP3hkCFDvOfcd999UlRUJLfeeqvk5+dL7969ZdmyZXygiZCm/fvPr7nHW4Y9+PUv9e9//3uzF+b06dP9+W0BWIRuz3P77bcHuxohqSH6R7Y0Cj4dYdeAPXvCQolr9OMMh6oU7Nshq566U5xuCwTtUdHSpvsAgnZYQkNdP/7qV78yR3V0tF0Dej0Au9D+PeuiwcGuBvws0p/fTPe1ZLolAASmf/RsaaRTO999912zDlO3NNKp8Kq+WxrpXsWeg8zIvtGAXadp13TEJqed5G8csCeuHwGgAUbar7zyykq3dQrmvn375LPPPjPrgQDYk9PplA8//NCU+/TpI1FRUcGukuUEsn9kSyPU/7XjlEM7N5uy2xX8kX+EB64fgcD0799v+9KUT8nyfYcN2DBo19GXiiIjI6VDhw5metHFF1/sr7oBsJiSkhKT9Ezp+sOkpKRgV8lyAtk/sqUR6svlKJPP/jrVlJ3lDhoSAcH1IxCY/v39GSNNefCsFTR5OAftFZMeNTS2MwIQSgLZP7KlEYBQEsj+EYB/luHVRVpamtlKERZLRLdhwwbZvPn49LqzzjpLzj33XPEntjMCEKoaun9UbGkE1E1dtt1LTExkT/cQ7h8B1F/p0XzN1CiDBg2q0/OSG6XItq1bCNytErTrHz3dKmjVqlXmExWlW2XotNmXX37Z7BXsD2xnBCDUBKp/VGxpBPjGUVIsEhEpnTv7vr6zcZN02bkjl8A9RPtHAPXnKCnSpDzSY9gUSW/141ayNSk8uFc+nHOveU8z2m6RoP2OO+6Qo0ePytdffy0dO3Y0923atEmGDh1qMhe/9NJLlt3OCAAaUqD6Rw+2NAJq53SUasY96Tf2eUlu0qzW80uPHpZ3H7nebOmXkpJCE4do/wjg5CSmt5CUjLZ1eg5T6i0UtC9btkyWL1/u7XDVmWeeKXPnzvVroiXPdkajR4+WBx54QNavX2869djYWNPB12c7I1VaWmoOj4KCAr/VGUB4C1T/CKDu4hqlme33EBz0j4B9MaXegkG7bjMUExPzk/v1Pn3MytsZefYhnjx5st/qCQCB7h8BINTQPwL2xZT6hhVZnyf169dP7rrrLtm7d2+lLO+aFKl///4Nvp3Rt99+a8qe9RIHDhyodI7ermktxbhx4+TIkSPeY/fu3X6rM2BnGnhOmzbNHFUFpghc/wicjIioaMnq/1tTjoyMojEREPSPQGD6985X3m4OLQdrSr0vR3LTlgGvX1gF7XPmzDFTytu2bSunn366OTIzM819s2fPDsp2Rh5ah3Xr1kmvXr2q/b5xcXFmjVrFA0DtdGnKmDFjzKFlBK9/BE5GVHSMZJ53PCtwZDQfwCEw6B+BwPTvZ2QPMYeWYQ/1+vildevW8vnnn5t1m9988413BLxiQjh/YDsjAKEmUP0jAIQa+kcACEDQvnLlShk5cqR88sknZnT6l7/8pTmUTjPXvTZ1vXmfPn3EH9jOCLAWp9NpAlL185//XKKimFZbUY8ePeTTTz8NSP8InAyXyylH9uaastvlpDHR4OgfgcD174e/PT5TuXGbDjR7OAbtM2fONNutVTWdXLdaGz58uMyYMcOvF6VsZwRYR0lJibnwUoWFhZKUlBTsKlmKJsgMZP8I1JfLUSbrnp9kys5yBw2JBkf/CASuf18+9RZTHjzrxyXECKM17V9++aVccskl1T6u2xlt2LDBH/UCgJBT0xR4+kcA4Yz+EQACFLRrVvaaMkZHR0fLwYMHT6I6ABC6tA+s6TH6RwDhiv4RAAIUtJ966qlmn/Tq/Pvf/zbbtAFAONq8eXO1j9E/Aghn9I8AEKCg/dJLL5UJEyaYda0nOnbsmDz00ENmDToAhKMpU6bQPwIA/SMABC8R3fjx4+X111+X9u3bmyzyHTocz0io2xrNnTvXZJZ+8MEH/VtDAAgRhw8fpn8EAPpHAAhe0N68eXNZs2aNjBgxQsaNGydut9vcHxERIdnZ2SZw13MAIBz961//kvvuu4/+EQDoHwH4KDf3+BakvkpLS5OMjIywat86Be3qtNNOk7ffftuMKG3fvt0E7llZWdK4ceOGqSEAy9BElLoMxlNGZW3atKF/REiIiIqWdhfkSO7qJRIZGRXs6iAM0D8Cgevfz/rVTd6ylZUezdfRXxk0aFCdnpfcKEW2bd0SVoF7vX+TGqR3797dv7UBYGmxsbEyadLxvZ1RPfpHWF1UdIz87MIrjwft0XwAh8ChfwQavn/vdNnxfdqtzlFSJOJ2S49hUyS9VaZPzyk8uFc+nHOv5OfnE7QDAAAAANDQEtNbSEpGWxq6BtaeMwHAUlwul3fbno4dO0pkZJ02oABgEW6XSwrzvvOWAQD2oH16wf6dpkwgbB8E7QB8pls7durUyZQLCwslKSmJ1gNCkNNRKmv+/MDxcnlZsKsDIMzs37/fTG9uqERl4d6/L5v8e1MePGtFsKsDPyFoBwAAABCwgD2rfQcpPFpQ5+c6yhwNUifA6gjaAQAAAASEjrBrwN5n5HRJbtrSp+fkbd0oGxZNk3Kns8HrB1gRQTsAAACAgNKA3dc114UH9zR4fQArI4sUAAAAAAAWRdAOAAAAAIBFEbQDAAAAAGBRrGkH4LOYmBi59957vWUAoSkiKlpO6zVQdq19RyIjo4JdHQCAH/v3Dr/8nbcMe+A3CcBnsbGx8sQTT9BiQIiLio6RDgOuPR60R/MBHADYqX/vctXIYFcDfsb0eAAAAAAALIqRdpy0dlkdZO+e72o9r+WprSR32xZaPIS5XC759ttvTblNmzYSGcnnfkAocrtcciz/oLcMALAH7dOLDx0w5cQmzYNdHfgJQTtOmgbsOTOX13reklEDaO0Qd+zYMcnMzDTlwsJCSUpKCnaVANSD01EqH86+53i5vIw2BAAb9e9LHxxsyoNnrQh2deAnDJMBAAAAAGBRBO0AAAAAAFgUQTsAAAAAABZF0A4AAAAAgEURtAMAAAAAYFEE7QAAAAAAWBRBOwCfRUdHy2233WYOLQMITRGRUdK6W39vGbC7xx57TCIiImTUqFHe+0pKSuT222+X9PR0SU5OlsGDB8uBA8f3twZClfbpP7vwSnPQv9sHV90AfBYXFydz586lxYAQFxUTKx0HDpXdn62QqOiYYFcHaFDr16+XP//5z3LOOedUuv/uu++Wt956S1577TVJTU2VkSNHypVXXikff/wxvxGEdP/e9Xf3Brsa8DNG2gEAAGBLhYWFMmTIEHnuueekcePG3vuPHDkizz//vMyYMUP69esnXbt2lfnz58uaNWvkk08+CWqdAeBEBO0AfOZ2u+XgwYPm0DKA0KTv37KiAm8ZsCud/j5o0CAZMGBApfs3bNggDoej0v1nnHGGtGnTRtauXRuEmgL+oX16ydHD5qB/tw+mxwPwWXFxsTRr1sw7epGUlETrASHIWVYiq2aMPF52lAa7OkCDePnll+Xzzz830+NPtH//fomNjZW0tLRK9zdv3tw8VpXS0lJzeBQUHP/gC7Ba//5/9w4y5cGzVgS7OvATRtoRMGWOcolPTKrxKCsr4zcC1ANJlgDgR7t375a77rpLFi1aJPHx8X5pmqlTp5q1756jdevWNDmAgGCkHQHjdjklZ+aqGs95ZUSfgNUHsAuSLAGA/GT6e15envz85z/33ud0OmX16tUyZ84ceffdd81AQX5+fqXRds0en5GRUWVzjhs3TkaPHl1ppJ3AHUAgMNIOACGMJEsA8FP9+/eX//znP7Jx40bv0a1bN5OUzlOOiYmRFSt+nD68ZcsW+fbbb6VXr17V7qCSkpJS6QCAQGCkHQBskmTp0Ucf9TnJ0i9+8Ysqvx9rNgHYQaNGjaRTp06V7tM8LLonu+f+m2++2YycN2nSxATgd9xxhwnYq+sfASBYCNoBIET5O8mSZ83m5MmTG6S+AGAlTz31lERGRsrgwYPNB5bZ2dny9NNPB7taABDa0+NJtAQADZdkybNmU/cv9hz6cwDADlatWiUzZ8703ta+c+7cuXLo0CEpKiqS119/vdr17AAQTCEz0k6iJSD4oqOjZejQod4y7JVkybNmUw/YW0RklLQ8p7fs/fdHpgwAsAft09v2utRbhj1Eh1qipYprNnUU6Pnnn5fFixdLv379zH3z58+Xjh07yieffMKaJMDPNJhbsGAB7WqhJEsV3XjjjWbd+v33328yGnuSLOnUT1+SLCF8RMXESqfLbzVBe1R0TLCrAwDwY//e84bxtKfNhETQTqIlAKiMJEsAAADhwfJBO4mWgJPXLquD7N3zXa3ntTy1leRu21Lt4263W4qLi005MTFRIiIi+PVYGEmWUNN7ubys1FsGANiD9unOshJTjor1X84bBFd0KCRaeu+99/yeaEm3+PAoKCgwU0kBu9KAPWfm8lrPWzLqx+3BqqIBe3JysnfZim6fA2slWarIk2RJD6AivaBb+fiw42XH8eAdAGCP/v0fd/Y35cGzVgS7OgiHoJ1ESwCAQNMPcj0zSmqjyQABAADCNmgn0RIAINABe9vMdnL40A91ep6r3NlgdQIAAOHN0kE7iZYAAIGkI+wasGdPWChxjRrXen7Bvh2y6qk7xekmaAcAAGEYtPuCREsAAH/TgD0hNb3W80qOHqbxAQBAgwq5oJ1ESwAAAACAcBEZ7AoAAAAAAACbjLQDCJ6oqCi56qqrvGUAoSkiMlKad+wuBzavl4gIPr8HADv1761+fpG3DHsgaAfgM933+7XXXqPFgBAXFRMnna+6Q/71yPUSFRMb7OoAAPzYv58/fArtaTN8/AIAAAAAgEURtAMAAAAAYFFMjwdCXJmjXOITk2o+p6zMLz+rqKhIkpOTTbmwsFCSkmr+uQCsqbz0mJkab8plJcGuDgDAj/37P+7sb8qDZ62gXW2CoB0IcW6XU3JmrqrxnFdG9AlYfQAAAAD4D9PjAQAAAACwKIJ2AAAAAAAsiqAdAAAAAACLImgHAAAAAMCiCNoBAAAAALAosscD8FlUVJRceuml3jKA0BQRGSmn/KyzfL/9S4mI4PN7ALBT/96iUy9vGfZA0A7AZ/Hx8fLWW2/RYggpBQUFUlxc7NO5eXl5Eg6iYuLk59feY/Zqj4qJDXZ1AAB+7N8vuONJ2tNmCNoBALYO2NtmtpPDh36o0/Nc5c4GqxMAAEBdELQDAGxLR9g1YM+esFDiGjWu9fyCfTtk1VN3itNN0A4AAKyBoB2Az4qKiqRZs2beacRJSUm0HkKCBuwJqem1nldy9LCEg/LSY7L8sVuOl8tKgl0dAIAf+/cl9w4y5ZzpLGm0C4J2AHXi69pgANbmcpQFuwoAgAbg5MNY2yGlIAAAAAAAFkXQDgAAAACARTE9HgAA4CS3AExMTJSUlBTaEQACIDc3t07np6WlSUZGhoQqgnYAAIAKHCXFIhGR0rlzZ5/bpXGTdNm5I5fAHQAaUOnRfJGICBk06HiyPV8lN0qRbVu3hGzgTtAOAABQgdNRKuJ2Sb+xz0tyk+M7ZtSk9OhhefeR602iTkbbAaDhOEqKRNxu6TFsiqS3yvTpOYUH98qHc+6V/Px8gnYA9hcZGSkXXnihtwwgREVESuPTzpDDu76RiIiIYNfGsuIapfm0VSAAWEZEpDRtf663bFeJ6S0kJaOthAtG2gH4LCEhQVatWkWLASEuOjZOul//gPzrkeslKiYu2NUBAPixf+93z1za02YI2gEAAADUy/79+82044ZKIAaAoB0AAABAPQP2rPYdpPBoQZ2f6yhz0OaAjxhpB+CzoqIiadv2+PqhnTt3SlJSEq0HhKDy0mPy/pO3Hy+XlQS7OgBClI6wa8DeZ+R0SW7a0qfn5G3dKBsWTZNyp7PB6xeu/fvSBwab8q/++I9gVwd+QtAOoE6+//57WgywAUfx0WBXAYBNaMDua1KwwoN7Grw+4a600PflCggN9k0pCAAAAABAiCNoBwAAAADAopgeDwAAAIBM8IBFEbQDAADAVqZOnSqvv/66fPPNN5KQkCDnnXeePP7449KhQwfvOSUlJXLPPffIyy+/LKWlpZKdnS1PP/20NG/eXMIRmeAB6yJoBwAAgK188MEHcvvtt0v37t2lvLxcHnjgAbn44otl06ZN3p1P7r77bnnrrbfktddek9TUVBk5cqRceeWV8vHHH0s4IhM8YF0E7QB8FhkZKd26dfOWAYSoiEhJaZEpBft2SERERLBrA/jdsmXLKt1esGCBNGvWTDZs2CAXXHCBHDlyRJ5//nlZvHix9OvXz5wzf/586dixo3zyySfyi1/8Imx/K2SCD3ERkdL4tDO8ZdgDv8kw1S6rg8QnJtV46DlARTrFcP369ebQMoI//VNHkRo1amQuRnNycmTLli2VztHpnzralJ6eLsnJyTJ48GA5cOBA0OoMa4iOjZNf3DLZlKNi4oJdHaDBaZCumjRpYr5q8O5wOGTAgAHec8444wxp06aNrF27lt8IQrp/v/iBF8yhZdgDI+1hau+e7yRn5vIaz1ky6sc/ZACsh+mfAFA7l8slo0aNkvPPP186derkXb8dGxsraWlplc7V9ez6WFV03bseHgUFBZZufv1/6JR3X+Xm5jZofQDYNGgniQgAVI/pnwBQO51t9NVXX8lHH3100telkycfn6FidSSVA+zF0kE7o0iAtRQXF8uZZ55pyprMJzExMdhVwklM/6xqzWaojSShfsrLSmT1rNGm7Cz78fcN2I0ml1u6dKmsXr1aWrVq5b0/IyNDysrKzEh0xdF2XT6kj1Vl3LhxMnr08feNp39s3bq1WBFJ5cK7f39n0u9MeeCkxcGuDsIhaGcUKbjKHOVmbXut55WVBaQ+CD632y27du3ylmG/6Z+hNJKEk+B2S8mR748Xhfcy7Ef/Rt1xxx3yxhtvyKpVqyQzM7PS4127dpWYmBhZsWKFyfWhNCfIt99+K7169arye8bFxZkjlJBULgy53VL8w//+xnOtZhuWDtobYhQJvnO7nJIzc1Wt570yog/NCthk+mcojSQBQE19omaG/7//+z+TrNPzQaVu7aaJVPXrzTffbPo7va5MSUkxQb4G7Fw/ArCa6HAbRVJM/wRgJ/6c/hmKI0mAVeTl5fl8ri4v0kARDeOZZ54xX/v27Vvpft3W7YYbbjDlp556ymxfqiPtem2YnZ0tTz/9NL8SwKZy65hsUa+dqrteCrTocBtFUkz/BGAHDTH9E0DdOUqKzX7InTt39vk5jZuky84duQTuDcSXJVzx8fEyd+5ccwCwr9Kj+SIRETJo0KA6PS+5UYps27rFEoF7dLiNIimmfwKwA6Z/AtbgdJTqmjLpN/Z5SW7SrNbzS48elncfud4k92S0HQAalqOkyKzv7zFsiqS3qjzAUZ3Cg3vlwzn3mjiToD1Io0hM/wRgB0z/BKwlrlGaJKSmB7saAIAqJKa3kJSMthKKLD3SzigSYC0RERHeLd+0jOBi+ifqLSJCkk45VYq+3yMRwnsZAGwjIkJSWvxvoJNrNduwdNDOKBJgLZo46euvvw52NQCcpOjYeDl/xFT51yPXS1QsiQcBwE79+8BJi4JdDYRT0M4oEgAAAAAgnFk6aAcA4ES6d7wm8PL3FlwAAABWRNAOwGcaKHXv3t2U169fb6bLA4EO2NtmtpPDh36o0/Nc5c4Gq1MoKi8rkY+fGWfKzrLSYFcHAODH/v29P95syr984Hna1SYI2gHUacnKpk2bvGUgGB8cacCePWGhxDVqXOv5Bft2yKqn7hSnm6C9ErfbJKEzReG9DAC24Xabv32eMuyBoB2AV5mjXOITk6ptEQJ1WIUG7L5srVVy9HBA6gMAANBQCNoBeLldTsmZuaraFikvPSb/uLM/LQYAAAAESGSgfhAAAAAAAKgbgnYAAAAAACyKoB0AAAAAAItiTTsA30VEmOO0Nm0kQssAQlNEhMSnniIlR76XCOG9DAC2EREhiekZ3jLsgaAdgO8dRmy8xMUnyM6dO2k1IMTfyxfcOUP+9cj1EhUbF+zqAAD82L9f9sfXaU+bYXo8AAAAAAAWxUi7zbTL6iB793xX63llZWUBqQ8AAAAAoP4I2m1GA/acmctrPe+VEX0CUh/YS3lZqZSVlEj37t1l9erVkpCQEOwqAajne/mTvzxkyk5HKW0IADbq31dOH2HK/e59JtjVgZ8QtAPwndslbrdLPvvsM3G5XLQcEKrcLinYt+N40e0Odm0AAP7idsnhXd94y7AH1rQDAAAAAGBRjLQDAAAAFrZ//37Jz8/3+fzc3NwGrQ+AwCJoBwAAACwcsGe17yCFRwvq/FxHmaNB6gQgsAjaAQAAAIvSEXYN2PuMnC7JTVv69Jy8rRtlw6JpUu50Nnj9ADQ8gnYAAADA4jRgT8lo69O5hQf3NHh9AAQOQTuAOjvllFNoNSDExSQ2Ekfx0WBXAwDgZ3HJabSpzRC0A/C9w4hLkLiERDl48CCtBoT4e/mie+bKvx65XqJj44NdHQCAH/v3nCffpj1thi3fAAAAAACwKIJ2AAAAAAAsiunxIaRdVgfZu+e7Gs8pKysLWH0QfsrLSqWstET69u0r77zzjiQkJAS7SgDq+V5ev/CPpux0lNKGQZKXl+fTeYmJiZKSktLg9QFgj/599ezRpnzBHTOCXR34CUF7CNGAPWfm8hrPeWVEn4DVB2HI7RK3yyUffPCBuFyuYNcGQH25XXJ41zfHi2437RhgjpJikYhI6dy5s0/nN26SLjt35BK4A6id2yUHt37hLcMeCNoBBG1mSMtTW0nuti38BgCEFTO7we2SfmOfl+QmzWo8t/ToYXn3keuluLiYoB0AwhRBO4CgzQxZMmoArQ8gbMU1SpOE1PRgVwMAYHEkogMAAAAAwKIYaQcAAAAA4AS5ublSF2lpaZKRkSH+RtAOAAAAAMD/lB7NF4mIkEGDBkldJDdKkW1bt/g9cCdoB1Bnuv0QgNAWGRMrLgfbhAKA3UTFxge7CiHPUVKk26tIj2FTJL1Vpk/PKTy4Vz6cc6/k5+cTtAMInui4BIlLSJSioiJ+DUCIv5cHjP2L/OuR6yWaizvbKSgoMNnmfcU+8IG1f/9+c1HfUNNzEd60f79q9spgV8M2EtNbSEpG22BXg5F2AAAAOwXsbTPbyeFDP/j8HPaBD2zAntW+gxQeLajzcx1ljgapEwDrY3o8gDopc5RLfGJS7eeVlfnte7GfOwD4RkfYNWDPnrBQ4ho1rvV89oEPLB1h14C9z8jpkty0pU/Pydu6UTYsmiblTmeD1w+ANRG0A/CZ01Eq7vIyaXJGVzn/D3+UqJi4as99ZUSfWr+f2+WUnJmraj2P/dwB/7+XP3/pyf+VWdceCvLy8up0ngbs7AFvXRqw+zrltvDgngavD+zVv3887wFT1ms12ANBOwCfuV0u83XfV2u9ZQChR9+/32//8njZzXvZyhwlxSIRkdK5c+c6Pc9VzqgsEK79u16necqwB4J2AIBfkQQL8O+ombhd0m/s85LcpFnt7799O2TVU3eK0+1skJF8ReI6AAgsgnYAgN+QBAtoGHGN0nya7l5y9HCDj+STuA4AAss2QfvcuXPliSeeMFk59Q/P7NmzpUePHsGuFgCEVf9IEizA3iP5dkxcxzUkAKuzRdD+yiuvyOjRo2XevHnSs2dPmTlzpmRnZ8uWLVukWbPa/wABgF0Fq3+saxKsuibZAhCckXy74RoSQCiwRdA+Y8YMGTZsmNx4443mtl6cvvXWW/LCCy/I2LFjG+RntsvqIHv3fFfreS63SGTEyZ/j6xZagB35c2s4X9+7dtlmLhj9Y12QZAtAMFm9jwQAWwTtGshu2LBBxo0b570vMjJSBgwYIGvXHs+ceKLS0lJzeBw5csS7FtNXe77bLb967M1az3v97my58ql3T/ocz3mOY0U1n+R2136Or+f583sF42da9XsF42f66XuVlx7zlvW8GrOS+rFebme5DJr+Vq3nLR3761rfx76+d335Xh6e89xut4R7/3j06FHztej7vccD8trO37/TTM3tOexRSUqrfZTvaN53sv7FKVJw8Dtxlv9Yz+oU/7Dvf/XZJ+IsD+r5VqpLeVnJj8/7fr9ER9V+ORAubWO1861UF1VWmO99r2tCulDtH+vTR/rj+rGwsNB8zd+T61Mf6en3zNd9OyQ6wrds4DzHfm3g6/PMkpf/OfTtN5b+P9ntOUU/7Pe+z/1+DekOcXv27NH/oXvNmjWV7h8zZoy7R48eVT7noYceMs/hoA14DfAa8OdrYPfu3W4roX/k9U0fx2vAKq8Bq/WP9ekjuX4M/uuIgzaQMO0jQ36kvT70E1Vd4+nhcrnk0KFDkp6eLhERPsxTDxD95KV169aye/du2yR7CRbakrZsSPrpqI46tWzZUkJdqPSPgULfQduGGqu9Zukf7clqrzO7oX3Dp33dPl5DhnzQfsopp0hUVJQcOHCg0v16OyMjo8rnxMXFmaOitLQ0sSp9MQX7BWUXtCVt2VBSU1PFasKhfwwU+g7aNtRY6TVrxf6xPn0k/aO1X2d2RPuGR/um+tBHRkqIi42Nla5du8qKFSsqjQzp7V69egW1bgAQTPSPAEAfCSD0hfxIu9KpnEOHDpVu3bqZvYd1S6OioiJvJlAACFf0jwBAHwkgtNkiaP/tb38rBw8elIkTJ8r+/fulS5cusmzZMmnevLmEMp2G9dBDD/1kqipoy2DidRla7No/Bgqvd9o21PCarRv6SF5nVsT7mPY9UYRmo/vJvQAAAAAAIOhCfk07AAAAAAB2RdAOAAAAAIBFEbQDAAAAAGBRBO0AAAAAAFgUQXsQrV69Wi677DJp2bKlREREyJIlS2p9TmlpqTz44INy2mmnmcySbdu2lRdeeEHCWX3acdGiRdK5c2dJTEyUFi1ayE033SQ//PCDhLupU6dK9+7dpVGjRtKsWTPJycmRLVu21Pq81157Tc444wyJj4+Xs88+W95+++2A1BcIRv+xatUq+fnPf2764J/97GeyYMECfhEn2a7apnreiYfueIAf0UcjWA4dOiRDhgyRlJQUSUtLk5tvvlkKCwt9eq7mvB44cKDPfWw4qmv76vl33HGHdOjQQRISEqRNmzZy5513ypEjRwJabyubO3euiZP02rRnz57y6aefhvS1LEF7EOle8ho46ovKV1dffbWsWLFCnn/+eRNMvfTSS+YNG87q2o4ff/yxXH/99aZD/Prrr82bVN/Iw4YNk3D3wQcfyO233y6ffPKJvPfee+JwOOTiiy82bVydNWvWyLXXXmva84svvjCBvh5fffVVQOsOBKL/2LFjhwwaNEguuugi2bhxo4waNUpuueUWeffdd/kFnES7eujftX379nkP/fAQP6KPRrBoQKnXTHptsHTpUvPB3K233urTc2fOnGkCdvivfffu3WuO6dOnm+st/fBYt3PVazGIvPLKKzJ69Gizdfbnn39u/h5lZ2dLXl5e6F7L6pZvCD79Vbzxxhs1nvPOO++4U1NT3T/88EPA6mXHdnziiSfc7dq1q3TfrFmz3KeeemoD1y705OXlmTb94IMPqj3n6quvdg8aNKjSfT179nQPHz48ADUEAtt/3Hfffe6zzjqr0n2//e1v3dnZ2fwqTqJd33//fXPe4cOHacc6oI9GIGzatMm8P9evX1/pmjQiIsK9Z8+eGp/7xRdfmOurffv2+dQXhKOTad+KXn31VXdsbKzb4XC4w12PHj3ct99+u/e20+l0t2zZ0j116tSQvZZlpD2EvPnmm9KtWzeZNm2anHrqqdK+fXu599575dixY8GuWkjp1auX7N6920x70evJAwcOyN///ne59NJLg101y/FMs2rSpEm156xdu1YGDBhQ6T79NFPvB+yG13vD6tKli1my9Mtf/tLMikLN6KMRqH5Pp2zrNaiH/t2PjIyUdevWVfu84uJi+d3vfmdm3GRkZPDL8nP7VtUf6PT66OjosG7rsrIy2bBhQ6VrU21LvV3dtWko/G0P799qiMnNzZWPPvrIrLV444035Pvvv5fbbrvNrMWeP39+sKsXMs4//3yzpv23v/2tlJSUSHl5uVl7WddpnHbncrnM1F9tr06dOlV7nq45bd68eaX79DZrUWFH1b3eCwoKzAeourYQdaeB+rx588xFq+Zu+ctf/iJ9+/Y1F6yaPwA/RR+NQPZ7Jy5V0cBQP9Cv6W/93XffLeedd55cfvnlAahl+LVvRRoTPPLIIz4vWbCz77//XpxOZ5V/q7/55puQvZZlpD3E/kDrmiANOHv06GFGhmfMmCEvvvgio+11sGnTJrnrrrtk4sSJ5pM4XQO0c+dO+cMf/tBwv7wQpGvbdS3Pyy+/HOyqALA5zc0yfPhw6dq1q7nI1wSr+vWpp54KdtUsiz4aJ2vs2LFVJoCseFQX5PgyO3TlypVmPXu4asj2rUg/NNZcK2eeeaZMmjTJL3WH9TDSHmIjETotPjU11Xtfx44dzRTv7777TrKysoJav1DKvqujx2PGjDG3zznnHElKSpI+ffrIo48+ato53I0cOdKbCKVVq1Y1nqtT3nSJQUV6m6lwsKPqXu86JZFRdv/SD6d1dhl+ij4a/nDPPffIDTfcUOM57dq1M/3eiQm8dJaiZjCv7m+9Buz//e9/zbTvigYPHmyut3THCLtryPb1OHr0qFxyySVm1x+dhRsTEyPh7pRTTpGoqKg6XZuGwrUsQXsI0UBTM53rFhDJycnmvq1bt5p1GrUFVqi8xurE9T765lbHcyaFL/3/6xYi2vHrH9TMzEyfcgTojgY6ld5Ds5/q/YDd6Ov6xG1geL03DM3Oz4eoldFHw5+aNm1qDl/6vfz8fDM7UWfDeIJynQGqW2lVN8qsO2tUpNto6ewZXZIYDhqyfT0j7LruWrcf1ZkNunwWIrGxsaYd9dpUM8ArbUu9rR94huy1bLAz4YWzo0ePmqyaeuivYsaMGaa8a9cu8/jYsWPd1113XaXzW7Vq5b7qqqvcX3/9tcnonZWV5b7lllvc4ayu7Th//nx3dHS0++mnn3b/97//dX/00Ufubt26mUyT4W7EiBFmh4JVq1aZTK+eo7i42HuOtqW2qcfHH39s2nP69OnuzZs3ux966CF3TEyM+z//+U+Q/hdAw/Ufubm57sTERPeYMWPM633u3LnuqKgo97Jly2j2k2jXp556yr1kyRL3tm3bTN9x1113uSMjI93Lly+nXSugj0awXHLJJe5zzz3XvW7dOnPdpNef1157rffx7777zt2hQwfzeHXIHu+/9j1y5IjJbn722We7t2/fXumarby83B3uXn75ZXdcXJx7wYIFJjv/rbfe6k5LS3Pv378/ZK9lCdqDyLPFzYnH0KFDzeP69cILL6z0HH0hDRgwwJ2QkGAC+NGjR1cKqMJRfdpRt3g788wzTTu2aNHCPWTIENMhhruq2lEP/aDDQ9vS07YVtxlp37692WpEt8N66623glB7IDD9hz6nS5cu5vWu20dWfH+gfu36+OOPu08//XR3fHy8u0mTJu6+ffu6V65cSXOegD4awaLbDWsQmZyc7E5JSXHfeOON5sM5jx07dpjXp773q0PQ7r/2ra6P1UPPhds9e/Zsd5s2bczfah2Y++STT0L6WjZC/wn2aD8AAAAAAPgpsscDAAAAAGBRBO0AAAAAAFgUQTsAAAAAABZF0A4AAAAAgEURtAMAAAAAYFEE7QAAAAAAWBRBOwAAAAAAFkXQDgAAAACARRG0AwAAAABgUQTtAAAAAABYFEE7AAAAAAAWRdAOAAAAAIBY0/8DAYOhtgVlYfIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1200x400 with 3 Axes>"
       ]
@@ -538,9 +543,12 @@
     }
    ],
    "source": [
+    "import arviz as az\n",
+    "import seaborn as sns\n",
+    "\n",
     "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
     "for i, v in enumerate([\"alpha\", \"inv_sigma_h\", \"rho\"]):\n",
-    "    ax[i].hist(posterior_samples_stacked[v].ravel(), label=v)\n",
+    "    sns.histplot(posterior_samples_stacked[v].ravel(), ax=ax[i], label=v)\n",
     "    ax[i].set_title(v)\n",
     "    ax[i].axvline(\n",
     "        true_rho\n",

From 3fe6f742c4d3eb7563def4d24d78bc4bfd249428 Mon Sep 17 00:00:00 2001
From: Dan Waxman <dan.waxman1@gmail.com>
Date: Thu, 12 Mar 2026 14:28:00 -0400
Subject: [PATCH 5/5] format after merge

---
 dynestyx/inference/integrations/pfjax/discrete.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dynestyx/inference/integrations/pfjax/discrete.py b/dynestyx/inference/integrations/pfjax/discrete.py
index 800b8907..4a0baa40 100644
--- a/dynestyx/inference/integrations/pfjax/discrete.py
+++ b/dynestyx/inference/integrations/pfjax/discrete.py
@@ -6,6 +6,8 @@
 import numpyro
 from jax import lax, random
 from jax.scipy.special import logsumexp
+from pfjax.particle_filter import particle_filter as pfjax_particle_filter
+from pfjax.particle_filter import particle_filter_rb as pfjax_particle_filter_rb
 
 from dynestyx.inference.filter_configs import (
     BaseFilterConfig,
@@ -20,8 +22,6 @@
     _validate_control_dim,
     _validate_controls,
 )
-from pfjax.particle_filter import particle_filter as pfjax_particle_filter
-from pfjax.particle_filter import particle_filter_rb as pfjax_particle_filter_rb
 
 
 def _config_to_filter_kwargs(config: BaseFilterConfig) -> dict: