diff --git a/smt/applications/ego.py b/smt/applications/ego.py
index a93367862..2f29d92a7 100644
--- a/smt/applications/ego.py
+++ b/smt/applications/ego.py
@@ -23,6 +23,7 @@
 )
 from smt.sampling_methods import LHS
 
+
 class Evaluator(object):
     """
     An interface for evaluation of a function at x points (nsamples of dimension nx).
@@ -264,11 +265,21 @@ def _setup_optimizer(self, fun):
                 self.design_space,
                 work_in_folded_space=True,
             )
-            self._sampling = self.mixint.build_sampling_method(LHS, criterion="ese",random_state=self.options['random_state'], new_sampler=True)
+            self._sampling = self.mixint.build_sampling_method(
+                LHS,
+                criterion="ese",
+                random_state=self.options["random_state"],
+                new_sampler=True,
+            )
 
         else:
             self.mixint = None
-            self._sampling = lambda n: self.design_space.sample_valid_x(n,criterion="ese",random_state=self.options['random_state'], new_sampler=True)[0]
+            self._sampling = lambda n: self.design_space.sample_valid_x(
+                n,
+                criterion="ese",
+                random_state=self.options["random_state"],
+                new_sampler=True,
+            )[0]
             self.categorical_kernel = None
 
         # Build DOE
diff --git a/smt/utils/design_space.py b/smt/utils/design_space.py
index fe8135863..5d432adcc 100644
--- a/smt/utils/design_space.py
+++ b/smt/utils/design_space.py
@@ -636,7 +636,9 @@ class DesignSpace(BaseDesignSpace):
 
     """
 
-    def __init__(self, design_variables: Union[List[DesignVariable], list, np.ndarray], seed=None):
+    def __init__(
+        self, design_variables: Union[List[DesignVariable], list, np.ndarray], seed=None
+    ):
         self.sampler = None
         self.new_sampler = True
 
@@ -748,12 +750,12 @@ def _sample_valid_x(self, n: int, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
         x_limits_unfolded = self.get_unfolded_num_bounds()
         if "random_state" in kwargs.keys():
             self.seed = kwargs["random_state"]
-        if "new_sampler" in kwargs.keys() and kwargs["new_sampler"] :
+        if "new_sampler" in kwargs.keys() and kwargs["new_sampler"]:
             kwargs.pop("new_sampler", None)
-            if self.new_sampler :
+            if self.new_sampler:
                 self.sampler = LHS(xlimits=x_limits_unfolded, **kwargs)
-                self.new_sampler=False
-        if self.sampler is None :
+                self.new_sampler = False
+        if self.sampler is None:
             self.sampler = LHS(xlimits=x_limits_unfolded, **kwargs)
         x = self.sampler(n)