Reformat

src/jimgw/prior.py

@@ -105,7 +105,9 @@ def __repr__(self):
 
     def __init__(self, parameter_names: list[str], **kwargs):
         super().__init__(parameter_names)
-        assert self.n_dim == 1, "StandardNormalDistribution needs to be 1D distributions"
+        assert (
+            self.n_dim == 1
+        ), "StandardNormalDistribution needs to be 1D distributions"
 
     def sample(
         self, rng_key: PRNGKeyArray, n_samples: int
@@ -131,7 +133,8 @@ def sample(
 
     def log_prob(self, x: dict[str, Float]) -> Float:
         variable = x[self.parameter_names[0]]
-        return -0.5 * variable ** 2 - 0.5 * jnp.log(2 * jnp.pi)
+        return -0.5 * variable**2 - 0.5 * jnp.log(2 * jnp.pi)
+
 
 class SequentialTransform(Prior):
     """
@@ -249,6 +252,7 @@ def __init__(
             ],
         )
 
+
 @jaxtyped(typechecker=typechecker)
 class PeriodicUniform(SequentialTransform):
     xmin: float

src/jimgw/transforms.py

@@ -138,6 +138,7 @@ def __init__(
         super().__init__(name_mapping)
         self.transform_func = lambda x: 1 / (1 + jnp.exp(-x))
 
+
 class Modulo(UnivariateTransform):
     """
     Modulo transform following
@@ -158,6 +159,7 @@ def __init__(
         self.modulo = modulo
         self.transform_func = lambda x: jnp.mod(x, self.modulo)
 
+
 class ArcSine(UnivariateTransform):
     """
     ArcSine transformation