oups I didn't see there was a pre-commit setup

src/chainconsumer/chain.py

@@ -8,10 +8,9 @@
 There are also a few helper functions and objects in here, like the `MaxPosterior` class which
 provides the log posterior and the coordinate at which it can be found for the chain."""
 from __future__ import annotations
-from typing import TYPE_CHECKING
 
 import logging
-from typing import Any, TypeAlias
+from typing import TYPE_CHECKING, Any, TypeAlias
 
 import numpy as np
 import pandas as pd
@@ -365,12 +364,11 @@ def get_correlation(self, columns: list[str] | None = None) -> Named2DMatrix:
 
     @classmethod
     def from_emcee(
-            cls,
-            sampler: "emcee.EnsembleSampler",
-            columns: list[str],
-            name: str = 'Chain',
-            **kwargs: Any,
-
+        cls,
+        sampler: emcee.EnsembleSampler,
+        columns: list[str],
+        name: str = "Chain",
+        **kwargs: Any,
     ) -> Chain:
         """
         Constructor from an emcee sampler
@@ -382,17 +380,16 @@ def from_emcee(
             kwargs: Any other arguments to pass to the Chain constructor.
         """
 
-        df = pd.DataFrame.from_dict({col:val for col, val in zip(columns, sampler.get_chain(flat=True).T)})
+        df = pd.DataFrame.from_dict({col: val for col, val in zip(columns, sampler.get_chain(flat=True).T)})
 
         return cls(samples=df, name=name, **kwargs)
 
     @classmethod
     def from_numpyro(
-            cls,
-            mcmc: "numpyro.infer.MCMC",
-            name: str = 'Chain',
-            **kwargs: Any,
-
+        cls,
+        mcmc: numpyro.infer.MCMC,
+        name: str = "Chain",
+        **kwargs: Any,
     ) -> Chain:
         """
         Constructor from an emcee sampler
@@ -409,11 +406,10 @@ def from_numpyro(
 
     @classmethod
     def from_arviz(
-            cls,
-            arviz_id: "arviz.InferenceData",
-            name: str = 'Chain',
-            **kwargs: Any,
-
+        cls,
+        arviz_id: arviz.InferenceData,
+        name: str = "Chain",
+        **kwargs: Any,
     ) -> Chain:
         """
         Constructor from an arviz InferenceData object

tests/test_translators.py

@@ -1,11 +1,11 @@
+import arviz as az
+import emcee
+import numpy as np
 import numpyro
 import numpyro.distributions as dist
+from jax import random
 from numpyro.infer import MCMC, NUTS
-import jax.random as random
-import emcee
-import numpy as np
-import scipy.stats as stats
-import arviz as az
+from scipy import stats
 
 from chainconsumer import Chain
 
@@ -20,12 +20,12 @@ def run_numpyro_mcmc(n_steps, n_chains):
 
     def model(data=None):
         # Prior
-        mu = numpyro.sample('mu', dist.Normal(0, 10))
-        sigma = numpyro.sample('sigma', dist.HalfNormal(10))
+        mu = numpyro.sample("mu", dist.Normal(0, 10))
+        sigma = numpyro.sample("sigma", dist.HalfNormal(10))
 
         # Likelihood
-        with numpyro.plate('data', size=len(data)):
-            numpyro.sample('obs', dist.Normal(mu, sigma), obs=data)
+        with numpyro.plate("data", size=len(data)):
+            numpyro.sample("obs", dist.Normal(mu, sigma), obs=data)
 
     # Running MCMC
     kernel = NUTS(model)
@@ -72,29 +72,25 @@ def log_probability(theta, data):
 
 
 class TestTranslators:
-
     n_steps: int = 2000
     n_chains: int = 4
     n_params: int = 2
 
     def test_arviz_translator(self):
-
         numpyro_mcmc = run_numpyro_mcmc(self.n_steps, self.n_chains)
         arviz_id = az.from_numpyro(numpyro_mcmc)
         chain = Chain.from_arviz(arviz_id)
 
-        assert chain.samples.shape == (self.n_steps * self.n_chains, self.n_params + 1) #+1 for weight column
+        assert chain.samples.shape == (self.n_steps * self.n_chains, self.n_params + 1)  # +1 for weight column
 
     def test_numpyro_translator(self):
-
         numpyro_mcmc = run_numpyro_mcmc(self.n_steps, self.n_chains)
         chain = Chain.from_numpyro(numpyro_mcmc)
 
         assert chain.samples.shape == (self.n_steps * self.n_chains, self.n_params + 1)
 
     def test_emcee_translator(self):
-
         emcee_sampler = run_emcee_mcmc(self.n_steps, self.n_chains)
-        chain = Chain.from_emcee(emcee_sampler, ['mu', 'sigma'])
+        chain = Chain.from_emcee(emcee_sampler, ["mu", "sigma"])
 
         assert chain.samples.shape == (self.n_steps * self.n_chains, self.n_params + 1)