diff --git a/tspex/core/auxiliary_functions.py b/tspex/core/auxiliary_functions.py
index 9938936..68fd417 100644
--- a/tspex/core/auxiliary_functions.py
+++ b/tspex/core/auxiliary_functions.py
@@ -46,8 +46,7 @@ def dpm(vector):
     """
 
     n = len(vector)
-    dispersion_measure = np.std(vector, ddof=1) * np.sqrt(n)
-    return dispersion_measure
+    return np.std(vector, ddof=1) * np.sqrt(n)
 
 
 def tukey_biweight(vector, c=5, epsilon=1e-4):
@@ -76,8 +75,7 @@ def tukey_biweight(vector, c=5, epsilon=1e-4):
     i = np.abs(u) > 1
     w = (1 - u ** 2) ** 2
     w[i] = 0
-    tbi = np.sum(w * vector) / np.sum(w)
-    return tbi
+    return np.sum(w * vector) / np.sum(w)
 
 
 def entropy(vector):
@@ -106,8 +104,7 @@ def entropy(vector):
     else:
         p = vector / np.sum(vector)
         p = p[np.nonzero(p)[0]]
-        h = -1 * np.dot(p, np.log2(p))
-        return h
+        return -1 * np.dot(p, np.log2(p))
 
 
 def roku(vector):
@@ -136,8 +133,7 @@ def roku(vector):
 
     tbi = tukey_biweight(vector)
     vector_p = np.abs(vector - tbi)
-    h = entropy(vector_p)
-    return h
+    return entropy(vector_p)
 
 
 def js_distance(p, q):
@@ -168,5 +164,4 @@ def js_distance(p, q):
     left = entropy((p + q) / 2)
     right = (entropy(p) + entropy(q)) / 2
     js = left - right
-    jsd = np.sqrt(js)
-    return jsd
+    return np.sqrt(js)
diff --git a/tspex/core/specificity_functions.py b/tspex/core/specificity_functions.py
index e5e1f85..a455ea6 100644
--- a/tspex/core/specificity_functions.py
+++ b/tspex/core/specificity_functions.py
@@ -78,8 +78,7 @@ def counts(vector, **kwargs):
         if cts == 0:
             return 0.0
         else:
-            cts_transformed = (1 - (cts / n)) * (n / (n - 1))
-            return cts_transformed
+            return (1 - (cts / n)) * (n / (n - 1))
 
 
 def tau(vector, **kwargs):
@@ -110,8 +109,7 @@ def tau(vector, **kwargs):
     else:
         n = len(vector)
         vector_r = vector / np.max(vector)
-        tau_index = np.sum(1 - vector_r) / (n - 1)
-        return tau_index
+        return np.sum(1 - vector_r) / (n - 1)
 
 
 def gini(vector, **kwargs):
@@ -149,8 +147,7 @@ def gini(vector, **kwargs):
         index = np.arange(1, n + 1)
         gini_coefficient = (np.sum((2 * index - n - 1) * vector)) / (n * np.sum(vector))
         if transform:
-            transformed_gini_coefficient = gini_coefficient * (n / (n - 1))
-            return transformed_gini_coefficient
+            return gini_coefficient * (n / (n - 1))
         else:
             return gini_coefficient
 
@@ -187,8 +184,7 @@ def simpson(vector, **kwargs):
         simpson_index = np.sum(p ** 2)
         if transform:
             min_simpson = 1 / len(vector)
-            transformed_simpson_index = (simpson_index - min_simpson) / (1 - min_simpson)
-            return transformed_simpson_index
+            return (simpson_index - min_simpson) / (1 - min_simpson)
         else:
             return simpson_index
 
@@ -227,8 +223,7 @@ def shannon_specificity(vector, **kwargs):
         n = len(vector)
         ss = np.log2(n) - entropy(vector)
         if transform:
-            ss_transformed = ss / np.log2(n)
-            return ss_transformed
+            return ss / np.log2(n)
         else:
             return ss
 
@@ -271,8 +266,7 @@ def roku_specificity(vector, **kwargs):
         n = len(vector)
         rs = np.log2(n) - roku(vector)
         if transform:
-            rs_transformed = rs / np.log2(n)
-            return rs_transformed
+            return rs / np.log2(n)
         else:
             return rs
 
@@ -303,8 +297,7 @@ def tsi(vector, **kwargs):
     if not np.any(vector):
         return 0.0
     else:
-        tissue_specificity_index = vector / np.sum(vector)
-        return tissue_specificity_index
+        return vector / np.sum(vector)
 
 
 def zscore(vector, **kwargs):
@@ -342,8 +335,7 @@ def zscore(vector, **kwargs):
         zs = (vector - np.mean(vector)) / std
         if transform:
             max_zs = (n - 1) / np.sqrt(n)
-            zs_transformed = (zs + max_zs) / (2 * max_zs)
-            return zs_transformed
+            return (zs + max_zs) / (2 * max_zs)
         else:
             return zs
 
@@ -407,8 +399,7 @@ def spm_dpm(vector, **kwargs):
     """
 
     spm_vector = spm(vector)
-    spm_dispersion = dpm(spm_vector)
-    return spm_dispersion
+    return dpm(spm_vector)
 
 
 def js_specificity(vector, **kwargs):
@@ -475,5 +466,4 @@ def js_specificity_dpm(vector, **kwargs):
     """
 
     js_vector = js_specificity(vector)
-    js_specificity_dispersion = dpm(js_vector)
-    return js_specificity_dispersion
+    return dpm(js_vector)