diff --git a/sbayes/results.py b/sbayes/results.py
index a6b3284f..f1b47a3a 100644
--- a/sbayes/results.py
+++ b/sbayes/results.py
@@ -192,10 +192,7 @@ def read_dictionary(dataframe, search_key):
         param_dict = {}
         for column_name in dataframe.columns:
             if column_name.startswith(search_key):
-                param_dict[column_name] = dataframe[column_name].to_numpy(
-                    dtype=np.float
-                )
-
+                param_dict[column_name] = dataframe[column_name].to_numpy(dtype=float)
         return param_dict
 
     def parse_weights(self, parameters: pd.DataFrame) -> dict[str, NDArray]:
@@ -218,7 +215,7 @@ def parse_weights(self, parameters: pd.DataFrame) -> dict[str, NDArray]:
         weights = {}
         for f in self.feature_names:
             weights[f] = np.column_stack(
-                [parameters[f"w_{c}_{f}"].to_numpy(dtype=np.float) for c in components]
+                [parameters[f"w_{c}_{f}"].to_numpy(dtype=float) for c in components]
             )
 
         return weights
diff --git a/sbayes/sampling/loggers.py b/sbayes/sampling/loggers.py
index a4eddbd6..c0543816 100644
--- a/sbayes/sampling/loggers.py
+++ b/sbayes/sampling/loggers.py
@@ -86,7 +86,7 @@ def write_header(self, sample: Sample):
             self.match_clusters = False
 
         # Initialize cluster_sum array for matching
-        self.cluster_sum = np.zeros((sample.n_clusters, sample.n_objects), dtype=np.int)
+        self.cluster_sum = np.zeros((sample.n_clusters, sample.n_objects), dtype=int)
 
         # Cluster sizes
         for i in range(sample.n_clusters):
@@ -233,7 +233,7 @@ def write_header(self, sample: Sample):
             # Nothing to match
             self.match_clusters = False
 
-        self.cluster_sum = np.zeros((sample.n_clusters, sample.n_objects), dtype=np.int)
+        self.cluster_sum = np.zeros((sample.n_clusters, sample.n_objects), dtype=int)
 
     def _write_sample(self, sample):
         if self.match_clusters:
diff --git a/sbayes/sampling/operators.py b/sbayes/sampling/operators.py
index 3091768f..b55dd75e 100644
--- a/sbayes/sampling/operators.py
+++ b/sbayes/sampling/operators.py
@@ -604,6 +604,7 @@ def propose_new_sources(
         n_features = sample_old.n_features
 
         MODE = "gibbs"
+
         if MODE == "gibbs":
             sample_new, log_q, log_q_back = self.gibbs_sample_source(
                 sample_new, sample_old, object_subset=changed_objects
@@ -1025,7 +1026,7 @@ def grow_cluster(self, sample: Sample) -> tuple[Sample, float, float]:
             return sample, 0, -np.inf
 
         # Choose a random candidate and add it to the cluster
-        object_add = random.choice(candidates.nonzero()[0])
+        object_add = np.random.choice(candidates.nonzero()[0])
         sample_new.clusters.add_object(z_id, object_add)
 
         # Transition probability when growing
@@ -1068,7 +1069,7 @@ def shrink_cluster(self, sample: Sample) -> tuple[Sample, float, float]:
 
         # Cluster is big enough: shrink
         removal_candidates = self.get_removal_candidates(cluster_current)
-        object_remove = random.choice(removal_candidates)
+        object_remove = np.random.choice(removal_candidates)
         sample_new.clusters.remove_object(z_id, object_remove)
 
         # Transition probability when shrinking.