Cleaned up graph code

src/database/matplotlib_graph.py

@@ -12,30 +12,37 @@ class Matplotlib_Graph:
 
     def __init__(self, database):
         self.database = database
+        # Type
         self.type_of_plot = plt.plot
+        # Size
         self.size = [6,6]
+        # Labels
         self.xlabel = None
         self.ylabel = None
         self.title = None
+        # Scale
         self.yscale = "linear"
+        # Data points
+        self.x = None
+        self.y = None
 
 
-    def plt_setup(self, X, Y, yscale, xlabel, ylabel, title, type_of_plot, size):
+    def plot(self):
-        """Setup for plt"""
+        """Plot all the graph attributes"""
 
-        if yscale == "log":
+        if self.yscale == "log":
             # If using log then the values have to be positive numbers
-            Y  =  [abs(ele) for ele in Y]
+            self.y  =  [abs(ele) for ele in self.y]
 
         # Setup data
-        plt.figure(figsize = size)
+        plt.figure(figsize = self.size)
         plt.yscale(self.yscale)
-        type_of_plot(X, Y)
+        self.type_of_plot(self.x, self.y)
 
         # labels
-        plt.xlabel(xlabel)
+        plt.xlabel(self.xlabel)
-        plt.ylabel(ylabel)
+        plt.ylabel(self.ylabel)
-        plt.title(title)
+        plt.title(self.title)
 
         # Show the plot
         plt.show()
@@ -48,12 +55,16 @@ class Matplotlib_Graph:
         generations = self.database.get_total_generations()
 
         # Create the generations list - [0,1,2,etc]
-        X = list(range(0, generations))
+        self.x = list(range(0, generations))
 
         # Query for Y data
-        Y = self.database.get_generation_total_fitness()
+        self.y = self.database.get_generation_total_fitness()
 
-        self.plt_setup(X, Y, self.yscale, 'Generation', 'Generation Total Fitness', 'Relationship Between Generations and Generation Total Fitness', self.type_of_plot, self.size)
+        self.xlabel = 'Generation'
+        self.ylabel = 'Generation Total Fitness'
+        self.title  = 'Relationship Between Generations and Generation Total Fitness'
+
+        self.plot()
 
 
     def highest_value_chromosome(self):
@@ -63,13 +74,16 @@ class Matplotlib_Graph:
         generations = self.database.get_total_generations()
 
         # Create the generations list - [0,1,2,etc]
-        X = list(range(0, generations))
+        self.x  = list(range(0, generations))
 
         # Query for Y data
-        Y = self.database.get_highest_chromosome()
+        self.y = self.database.get_highest_chromosome()
 
-        self.plt_setup(X, Y, self.yscale, 'Generation', 'Highest Fitness', 'Relationship Between Generations and Highest Fitness', self.type_of_plot, self.size)
+        self.xlabel = 'Generation'
+        self.ylabel = 'Highest Fitness'
+        self.title  = 'Relationship Between Generations and Highest Fitness'
 
+        self.plot()
 
     def lowest_value_chromosome(self):
         """Generation by Min value Chromosome """
@@ -78,19 +92,16 @@ class Matplotlib_Graph:
         generations = self.database.get_total_generations()
 
         # Create the generations list - [0,1,2,etc]
-        X = list(range(0, generations))
+        self.x = list(range(0, generations))
-
-        print(X)
 
         # Query for Y data
-        Y = self.database.get_lowest_chromosome()
+        self.y = self.database.get_lowest_chromosome()
 
-        if(self.yscale == "log"):
+        self.xlabel = 'Generation'
-            # If using log then the values have to be positive numbers
+        self.ylabel = 'Lowest Fitness'
-            Y  =  [abs(ele) for ele in Y]
+        self.title  = 'Relationship Between Generations and Lowest Fitness'
-
-        self.plt_setup(X, Y, self.yscale, 'Generation', 'Lowest Fitness', 'Relationship Between Generations and Lowest Fitness', self.type_of_plot, self.size)
 
+        self.plot()
 
     # Getter and setters
     @property

src/run_testing.py

@@ -9,18 +9,6 @@ ga.population_size = 100
 ga.chromosome_length = 10
 ga.generation_goal = 150
 
-# Create random genes from 0 to 10
-ga.gene_impl = lambda: random.randint(0, 10)
-
-# Minimize the sum of the genes
-ga.fitness_function_impl = lambda chromosome: sum(gene.get_value() for gene in chromosome.get_gene_list())
-ga.target_fitness_type = 'min'
-
 ga.evolve()
 
-ga.print_generation()
+ga.graph.highest_value_chromosome()
-ga.print_population()
-
-ga.graph.type_of_plot = "line"
-ga.graph.yscale = "log"
-ga.graph.lowest_value_chromosome()