diff --git a/example/Single_event_runManager.py b/example/Single_event_runManager.py
index 3a89b561..5c678b22 100644
--- a/example/Single_event_runManager.py
+++ b/example/Single_event_runManager.py
@@ -28,7 +28,6 @@
     ]
 )
 
-
 run = SingleEventRun(
     seed=0,
     detectors=["H1", "L1"],
@@ -91,6 +90,8 @@
 
 run_manager = SingleEventPERunManager(run=run)
 run_manager.jim.sample(jax.random.PRNGKey(42))
+
+# plot the corner plot and diagnostic plot
 run_manager.plot_corner()
 run_manager.plot_diagnostic()
-run_manager.jim.get_samples()
+
diff --git a/src/jimgw/single_event/runManager.py b/src/jimgw/single_event/runManager.py
index 124e9de5..66790c1d 100644
--- a/src/jimgw/single_event/runManager.py
+++ b/src/jimgw/single_event/runManager.py
@@ -67,16 +67,14 @@ def jaxarray_representer(dumper: yaml.Dumper, data: ArrayImpl):
 @dataclass
 class SingleEventRun:
     seed: int
-    
+
     detectors: list[str]
     priors: dict[
         str, dict[str, Union[str, float, int, bool]]
     ]  # Transform cannot be included in this way, add it to preset if used often.
     jim_parameters: dict[str, Union[str, float, int, bool, dict]]
     path: str = "./experiment"
-    injection_parameters: dict[str, float] = field(
-        default_factory=lambda: {}
-    )  
+    injection_parameters: dict[str, float] = field(default_factory=lambda: {})
     injection: bool = False
     likelihood_parameters: dict[str, Union[str, float, int, bool, PyTree]] = field(
         default_factory=lambda: {"name": "TransientLikelihoodFD"}
@@ -127,7 +125,7 @@ def __init__(self, **kwargs):
         else:
             print("Neither run instance nor path provided.")
             raise ValueError
-        
+
         if self.run.injection and not self.run.injection_parameters:
             raise ValueError("Injection mode requires injection parameters.")
 
@@ -359,14 +357,14 @@ def plot_data(self, path: str):
         plt.ylabel("Amplitude")
         plt.legend()
         plt.savefig(path)
-        
+
     def sample(self):
         self.jim.sample(jax.random.PRNGKey(self.run.seed))
-        
+
     def get_samples(self):
         return self.jim.get_samples()
-    
-    def plot_corner(self, path: str="corner.jpeg", **kwargs):
+
+    def plot_corner(self, path: str = "corner.jpeg", **kwargs):
         """
         plot corner plot of the samples.
         """
@@ -375,29 +373,38 @@ def plot_corner(self, path: str="corner.jpeg", **kwargs):
         show_titles = kwargs.get("show_titles", True)
         title_fmt = kwargs.get("title_fmt", "g")
         use_math_text = kwargs.get("use_math_text", True)
-        
+
         samples = self.jim.get_samples()
         param_names = list(samples.keys())
         samples = np.array(list(samples.values())).reshape(int(len(param_names)), -1).T
-        corner.corner(samples, labels=param_names, plot_datapoints=plot_datapoint, title_quantiles=title_quantiles, show_titles=show_titles, title_fmt=title_fmt, use_math_text=use_math_text, **kwargs)
+        corner.corner(
+            samples,
+            labels=param_names,
+            plot_datapoints=plot_datapoint,
+            title_quantiles=title_quantiles,
+            show_titles=show_titles,
+            title_fmt=title_fmt,
+            use_math_text=use_math_text,
+            **kwargs,
+        )
         plt.savefig(path)
         plt.close()
-        
-    def plot_diagnostic(self, path: str="diagnostic.jpeg", **kwargs):
+
+    def plot_diagnostic(self, path: str = "diagnostic.jpeg", **kwargs):
         """
         plot diagnostic plot of the samples.
         """
         summary = self.jim.Sampler.get_sampler_state(training=True)
         chains, log_prob, local_accs, global_accs, loss_vals = summary.values()
         log_prob = np.array(log_prob)
-        
+
         plt.figure(figsize=(6, 6))
         axs = [plt.subplot(2, 2, i + 1) for i in range(4)]
         plt.sca(axs[0])
         plt.title("log probability")
         plt.plot(log_prob.mean(0))
         plt.xlabel("iteration")
-        
+
         plt.sca(axs[1])
         plt.title("NF loss")
         plt.plot(loss_vals.reshape(-1))
@@ -413,8 +420,6 @@ def plot_diagnostic(self, path: str="diagnostic.jpeg", **kwargs):
         plt.plot(global_accs.mean(0))
         plt.xlabel("iteration")
         plt.tight_layout()
-        
+
         plt.savefig(path)
         plt.close()
-        
-