fix some code format

Author: JimChen

pygad.py

@@ -339,12 +339,18 @@ def __init__(self,
                     shape=(initial_population.shape[0], initial_population.shape[1]), dtype=object)
                 for gene_idx in range(initial_population.shape[1]):
                     if self.gene_type[gene_idx][1] is None:
-                        self.initial_population[:, gene_idx] = numpy.asarray(initial_population[:, gene_idx],
-                                                                             dtype=self.gene_type[gene_idx][0])
+                        self.initial_population[:, gene_idx] = numpy.asarray(
+                            initial_population[:, gene_idx],
+                            dtype=self.gene_type[gene_idx][0]
+                        )
                     else:
-                        self.initial_population[:, gene_idx] = numpy.round(numpy.asarray(initial_population[:, gene_idx],
-                                                                                         dtype=self.gene_type[gene_idx][0]),
-                                                                           self.gene_type[gene_idx][1])
+                        self.initial_population[:, gene_idx] = numpy.round(
+                            numpy.asarray(
+                                initial_population[:, gene_idx],
+                                dtype=self.gene_type[gene_idx][0]
+                            ),
+                            self.gene_type[gene_idx][1]
+                        )
 
             # A NumPy array holding the initial population.
             self.population = self.initial_population.copy()
@@ -525,7 +531,7 @@ def __init__(self,
                         mutation_percent_genes = 10
                         # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated.
                         mutation_num_genes = numpy.uint32(
-                            (mutation_percent_genes*self.num_genes)/100)
+                            (mutation_percent_genes * self.num_genes)/100)
                         # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1.
                         if mutation_num_genes == 0:
                             if self.mutation_probability is None:
@@ -546,7 +552,7 @@ def __init__(self,
 
                             # Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated.
                             mutation_num_genes = numpy.uint32(
-                                (mutation_percent_genes*self.num_genes) / 100)
+                                (mutation_percent_genes * self.num_genes) / 100)
                             # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1.
                             if mutation_num_genes == 0:
                                 if self.mutation_probability is None:
@@ -1455,7 +1461,7 @@ def cal_pop_fitness(self):
                     fitness = self.solutions_fitness[self.solutions.index(
                         list(sol))]
                 # If the solutions are not saved (i.e. `save_solutions=False`), check if this solution is a parent from the previous generation and its fitness value is already calculated. If so, use the fitness value instead of calling the fitness function.
-                if not (self.last_generation_parents is None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0):
+                if (self.last_generation_parents is not None) and len(numpy.where(numpy.all(self.last_generation_parents == sol, axis=1))[0] > 0):
                     # Index of the parent in the parents array (self.last_generation_parents). This is not its index within the population.
                     parent_idx = numpy.where(
                         numpy.all(self.last_generation_parents == sol, axis=1))[0][0]
@@ -1982,7 +1988,7 @@ def uniform_crossover(self, parents, offspring_size):
         offspring = self.generate_new_genes(size=offspring_size)
 
         for k in range(offspring_size[0]):
-            if not (self.crossover_probability is None):
+            if self.crossover_probability is not None:
                 probs = numpy.random.random(size=parents.shape[0])
                 indices = numpy.where(probs <= self.crossover_probability)[0]
 
@@ -3287,11 +3293,25 @@ def plot_fitness(self,
 
         fig = matplotlib.pyplot.figure()
         if plot_type == "plot":
-            matplotlib.pyplot.plot(self.best_solutions_fitness, linewidth=linewidth, color=color)
+            matplotlib.pyplot.plot(
+                self.best_solutions_fitness,
+                linewidth=linewidth,
+                color=color
+            )
         elif plot_type == "scatter":
-            matplotlib.pyplot.scatter(range(self.generations_completed + 1), self.best_solutions_fitness, linewidth=linewidth, color=color)
+            matplotlib.pyplot.scatter(
+                range(self.generations_completed + 1),
+                self.best_solutions_fitness,
+                linewidth=linewidth,
+                color=color
+            )
         elif plot_type == "bar":
-            matplotlib.pyplot.bar(range(self.generations_completed + 1), self.best_solutions_fitness, linewidth=linewidth, color=color)
+            matplotlib.pyplot.bar(
+                range(self.generations_completed + 1),
+                self.best_solutions_fitness,
+                linewidth=linewidth,
+                color=color
+            )
         matplotlib.pyplot.title(title, fontsize=font_size)
         matplotlib.pyplot.xlabel(xlabel, fontsize=font_size)
         matplotlib.pyplot.ylabel(ylabel, fontsize=font_size)
@@ -3364,8 +3384,10 @@ def plot_new_solution_rate(self,
         matplotlib.pyplot.ylabel(ylabel, fontsize=font_size)
 
         if save_dir is not None:
-            matplotlib.pyplot.savefig(fname=save_dir,
-                                      bbox_inches='tight')
+            matplotlib.pyplot.savefig(
+                fname=save_dir,
+                bbox_inches='tight'
+            )
         matplotlib.pyplot.show()
 
         return fig
@@ -3474,7 +3496,11 @@ def plot_genes(self,
                                 # axs[row_idx, col_idx].remove()
                                 break
                             if plot_type == "plot":
-                                axs[row_idx, col_idx].plot(solutions_to_plot[:, gene_idx], linewidth=linewidth, color=fill_color)
+                                axs[row_idx, col_idx].plot(
+                                    solutions_to_plot[:, gene_idx],
+                                    linewidth=linewidth,
+                                    color=fill_color
+                                )
                             elif plot_type == "scatter":
                                 axs[row_idx, col_idx].scatter(
                                     range(solutions_to_plot.shape[0]),
@@ -3483,8 +3509,12 @@ def plot_genes(self,
                                     color=fill_color
                                 )
                             elif plot_type == "bar":
-                                axs[row_idx, col_idx].bar(range(solutions_to_plot.shape[0]), solutions_to_plot[:,
-                                                          gene_idx], linewidth=linewidth, color=fill_color)
+                                axs[row_idx, col_idx].bar(
+                                    range(solutions_to_plot.shape[0]),
+                                    solutions_to_plot[:, gene_idx],
+                                    linewidth=linewidth,
+                                    color=fill_color
+                                )
                             axs[row_idx, col_idx].set_xlabel("Gene " + str(gene_idx), fontsize=font_size)
                             gene_idx += 1