diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py
index 2f0086ac..243d6a8f 100644
--- a/src/jimgw/jim.py
+++ b/src/jimgw/jim.py
@@ -104,19 +104,38 @@ def posterior(self, params: Float[Array, " n_dim"], data: dict):
 
     def sample(self, key: PRNGKeyArray, initial_position: Array = jnp.array([])):
         if initial_position.size == 0:
-            initial_position = jnp.zeros((self.sampler.n_chains, self.prior.n_dim)) + jnp.nan
-        
-            while not jax.tree.reduce(jnp.logical_and, jax.tree.map(lambda x: jnp.isfinite(x), initial_position)).all():
-                non_finite_index = jnp.where(jnp.any(~jax.tree.reduce(jnp.logical_and, jax.tree.map(lambda x: jnp.isfinite(x), initial_position)),axis=1))[0]
+            initial_position = (
+                jnp.zeros((self.sampler.n_chains, self.prior.n_dim)) + jnp.nan
+            )
+
+            while not jax.tree.reduce(
+                jnp.logical_and,
+                jax.tree.map(lambda x: jnp.isfinite(x), initial_position),
+            ).all():
+                non_finite_index = jnp.where(
+                    jnp.any(
+                        ~jax.tree.reduce(
+                            jnp.logical_and,
+                            jax.tree.map(lambda x: jnp.isfinite(x), initial_position),
+                        ),
+                        axis=1,
+                    )
+                )[0]
 
                 key, subkey = jax.random.split(key)
                 guess = self.prior.sample(subkey, self.sampler.n_chains)
                 for transform in self.sample_transforms:
                     guess = jax.vmap(transform.forward)(guess)
-                guess = jnp.array(jax.tree.leaves({key: guess[key] for key in self.parameter_names})).T
-                finite_guess = jnp.where(jnp.all(jax.tree.map(lambda x: jnp.isfinite(x), guess),axis=1))[0]
+                guess = jnp.array(
+                    jax.tree.leaves({key: guess[key] for key in self.parameter_names})
+                ).T
+                finite_guess = jnp.where(
+                    jnp.all(jax.tree.map(lambda x: jnp.isfinite(x), guess), axis=1)
+                )[0]
                 common_length = min(len(finite_guess), len(non_finite_index))
-                initial_position = initial_position.at[non_finite_index[:common_length]].set(guess[:common_length])
+                initial_position = initial_position.at[
+                    non_finite_index[:common_length]
+                ].set(guess[:common_length])
         self.sampler.sample(initial_position, None)  # type: ignore
 
     def maximize_likelihood(
diff --git a/src/jimgw/single_event/transforms.py b/src/jimgw/single_event/transforms.py
index 0cfae761..8ac4b126 100644
--- a/src/jimgw/single_event/transforms.py
+++ b/src/jimgw/single_event/transforms.py
@@ -111,34 +111,55 @@ def named_inverse_transform(x):
 
         self.inverse_transform_func = named_inverse_transform
 
+
 def named_m1_m2_to_Mc_q(x):
     Mc, q = m1_m2_to_Mc_q(x["m_1"], x["m_2"])
     return {"M_c": Mc, "q": q}
 
+
 def named_Mc_q_to_m1_m2(x):
     m1, m2 = Mc_q_to_m1_m2(x["M_c"], x["q"])
     return {"m_1": m1, "m_2": m2}
 
-ComponentMassesToChirpMassMassRatioTransform = BijectiveTransform((["m_1", "m_2"], ["M_c", "q"]))
+
+ComponentMassesToChirpMassMassRatioTransform = BijectiveTransform(
+    (["m_1", "m_2"], ["M_c", "q"])
+)
 ComponentMassesToChirpMassMassRatioTransform.transform_func = named_m1_m2_to_Mc_q
-ComponentMassesToChirpMassMassRatioTransform.inverse_transform_func = named_Mc_q_to_m1_m2
+ComponentMassesToChirpMassMassRatioTransform.inverse_transform_func = (
+    named_Mc_q_to_m1_m2
+)
+
 
 def named_m1_m2_to_Mc_eta(x):
     Mc, eta = m1_m2_to_Mc_eta(x["m_1"], x["m_2"])
     return {"M_c": Mc, "eta": eta}
 
+
 def named_Mc_eta_to_m1_m2(x):
     m1, m2 = Mc_eta_to_m1_m2(x["M_c"], x["eta"])
     return {"m_1": m1, "m_2": m2}
 
-ComponentMassesToChirpMassSymmetricMassRatioTransform = BijectiveTransform((["m_1", "m_2"], ["M_c", "eta"]))
-ComponentMassesToChirpMassSymmetricMassRatioTransform.transform_func = named_m1_m2_to_Mc_eta
-ComponentMassesToChirpMassSymmetricMassRatioTransform.inverse_transform_func = named_Mc_eta_to_m1_m2
+
+ComponentMassesToChirpMassSymmetricMassRatioTransform = BijectiveTransform(
+    (["m_1", "m_2"], ["M_c", "eta"])
+)
+ComponentMassesToChirpMassSymmetricMassRatioTransform.transform_func = (
+    named_m1_m2_to_Mc_eta
+)
+ComponentMassesToChirpMassSymmetricMassRatioTransform.inverse_transform_func = (
+    named_Mc_eta_to_m1_m2
+)
+
 
 def named_q_to_eta(x):
     return {"eta": q_to_eta(x["q"])}
+
+
 def named_eta_to_q(x):
     return {"q": eta_to_q(x["eta"])}
+
+
 MassRatioToSymmetricMassRatioTransform = BijectiveTransform((["q"], ["eta"]))
 MassRatioToSymmetricMassRatioTransform.transform_func = named_q_to_eta
 MassRatioToSymmetricMassRatioTransform.inverse_transform_func = named_eta_to_q
diff --git a/src/jimgw/transforms.py b/src/jimgw/transforms.py
index 3ad51e62..7dfa23df 100644
--- a/src/jimgw/transforms.py
+++ b/src/jimgw/transforms.py
@@ -89,7 +89,9 @@ def transform(self, x: dict[str, Float]) -> tuple[dict[str, Float], Float]:
         output_params = self.transform_func(transform_params)
         jacobian = jax.jacfwd(self.transform_func)(transform_params)
         jacobian = jnp.array(jax.tree.leaves(jacobian))
-        jacobian = jnp.log(jnp.absolute(jnp.linalg.det(jacobian.reshape(self.n_dim, self.n_dim))))
+        jacobian = jnp.log(
+            jnp.absolute(jnp.linalg.det(jacobian.reshape(self.n_dim, self.n_dim)))
+        )
         jax.tree.map(
             lambda key: x_copy.pop(key),
             self.name_mapping[0],
@@ -126,7 +128,9 @@ def inverse(self, y: dict[str, Float]) -> tuple[dict[str, Float], Float]:
         output_params = self.inverse_transform_func(transform_params)
         jacobian = jax.jacfwd(self.inverse_transform_func)(transform_params)
         jacobian = jnp.array(jax.tree.leaves(jacobian))
-        jacobian = jnp.log(jnp.absolute(jnp.linalg.det(jacobian.reshape(self.n_dim, self.n_dim))))
+        jacobian = jnp.log(
+            jnp.absolute(jnp.linalg.det(jacobian.reshape(self.n_dim, self.n_dim)))
+        )
         jax.tree.map(
             lambda key: y_copy.pop(key),
             self.name_mapping[1],