Clean code

ahmedfgad · ahmedfgad · commit d51b4d8cd46b · 2024-12-06T14:30:53.000-05:00
diff --git a/pygad/helper/unique.py b/pygad/helper/unique.py
@@ -171,7 +171,7 @@ def unique_int_gene_from_range(self,
                                    max_val, 
                                    mutation_by_replacement, 
                                    gene_type, 
-                                   step=None):
+                                   step=1):
 
             """
             Finds a unique integer value for the gene.
@@ -182,38 +182,24 @@ def unique_int_gene_from_range(self,
             max_val: Maximum value of the range to sample a number randomly.
             mutation_by_replacement: Identical to the self.mutation_by_replacement attribute.
             gene_type: Exactly the same as the self.gene_type attribute.
-    
+            step: Defaults to 1.
+
             Returns:
                 selected_value: The new value of the gene. It may be identical to the original gene value in case there are no possible unique values for the gene.
             """
 
+            # The gene_type is of the form [type, precision]
             if self.gene_type_single == True:
-                if step is None:
-                    # all_gene_values = numpy.arange(min_val, 
-                    #                                max_val, 
-                    #                                dtype=gene_type[0])
-                    all_gene_values = numpy.asarray(numpy.arange(min_val, max_val), 
-                                                    dtype=gene_type[0])
-                else:
-                    # For non-integer steps, the numpy.arange() function returns zeros if the dtype parameter is set to an integer data type. So, this returns zeros if step is non-integer and dtype is set to an int data type: numpy.arange(min_val, max_val, step, dtype=gene_type[0])
-                    # To solve this issue, the data type casting will not be handled inside numpy.arange(). The range is generated by numpy.arange() and then the data type is converted using the numpy.asarray() function.
-                    all_gene_values = numpy.asarray(numpy.arange(min_val, 
-                                                                 max_val, 
-                                                                 step), 
-                                                    dtype=gene_type[0])
+                dtype = gene_type[0]
             else:
-                if step is None:
-                    # all_gene_values = numpy.arange(min_val, 
-                    #                                max_val, 
-                    #                                dtype=gene_type[gene_index][0])
-                    all_gene_values = numpy.asarray(numpy.arange(min_val, 
-                                                                 max_val), 
-                                                    dtype=gene_type[gene_index][0])
-                else:
-                    all_gene_values = numpy.asarray(numpy.arange(min_val, 
-                                                                 max_val, 
-                                                                 step), 
-                                                    dtype=gene_type[gene_index][0])
+                dtype = gene_type[gene_index][0]
+
+            # For non-integer steps, the numpy.arange() function returns zeros if the dtype parameter is set to an integer data type. So, this returns zeros if step is non-integer and dtype is set to an int data type: numpy.arange(min_val, max_val, step, dtype=gene_type[0])
+            # To solve this issue, the data type casting will not be handled inside numpy.arange(). The range is generated by numpy.arange() and then the data type is converted using the numpy.asarray() function.
+            all_gene_values = numpy.asarray(numpy.arange(min_val, 
+                                                         max_val, 
+                                                         step), 
+                                            dtype=dtype)
     
             if mutation_by_replacement:
                 pass
