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Access posterior data by key #92

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Merged
michaelosthege merged 2 commits into from
May 23, 2026
Merged

Access posterior data by key #92

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1963ef6
Access posterior data by key
michaelosthege May 23, 2026
8b353f5
Use future-proof type hints
michaelosthege May 23, 2026
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12 changes: 5 additions & 7 deletions bletl/growth.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,11 +2,11 @@
import logging
from typing import Dict, Optional, Sequence, Tuple, Union

import arviz
import calibr8
import numpy
import pymc as pm
import pytensor.tensor as pt
import xarray as xa
from packaging import version

_log = logging.getLogger(__file__)
Expand Down Expand Up @@ -94,8 +94,8 @@ def theta_map(self) -> Dict[str, numpy.ndarray]:
return self._theta_map

@property
def idata(self) -> Optional[arviz.InferenceData]:
"""ArviZ InferenceData object of the MCMC trace."""
def idata(self) -> Optional[xa.DataTree]:
"""MCMC trace obtained from running :func:`GrowthRateResult.sample`."""
return self._idata

@property
Expand All @@ -114,15 +114,15 @@ def mu_mcmc(self) -> Optional[numpy.ndarray]:
if not self.idata:
return None
assert hasattr(self.idata, "posterior")
return self.idata.posterior.mu_t.stack(sample=("chain", "draw")).values.T
return self.idata["posterior"].mu_t.stack(sample=("chain", "draw")).values.T

@property
def x_mcmc(self) -> Optional[numpy.ndarray]:
"""Posterior samples of biomass curve."""
if self.idata is None:
return None
assert hasattr(self.idata, "posterior")
return self.idata.posterior["X"].stack(sample=("chain", "draw")).T
return self.idata["posterior"]["X"].stack(sample=("chain", "draw")).T

def sample(self, **kwargs) -> None:
"""Runs MCMC sampling with default settings on the growth model.
Expand All @@ -134,7 +134,6 @@ def sample(self, **kwargs) -> None:
"""
if importlib.util.find_spec("nutpie"):
sample_kwargs = dict(
return_inferencedata=True,
target_accept=0.95,
nuts_sampler="nutpie",
init="adapt_diag",
Expand All @@ -144,7 +143,6 @@ def sample(self, **kwargs) -> None:
)
else:
sample_kwargs = dict(
return_inferencedata=True,
target_accept=0.95,
init="adapt_diag",
initvals=self.theta_map,
Expand Down
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