Import matplotlib.pyplot as plt import networkx as nx # Crear un grafo dirigido

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import matplotlib.pyplot as plt import networkx as nx # Crear un grafo dirigido g = nx.Digraph() # Añadir nodos con sus respectivas etiquetas en español g.add_node("1", label="Start") g.add_node("2", label="Current Power Distribution") g.add_node("3", label="Consumer Assessment") g.add_node("4", label="Optimization with AI") g.add_node("5", label="Optimized Distribution") g.add_node("6", label="Vehicle Application") # Conectar los nodos g.add_edges_From([("1", "2"), ("2", "3"), ("3", "4"), ("4", "5"), ("5", "6")]) # Get the positions for the nodes in a pos hierarchical layout = nx.spring_Layout(g, seed=42) # Draw the nx graph.draw(g, pos, with_labels=False, node_Size=3000, node_Color="lightgreen", Font_Size=10) # Add labels in Spanish labels = nx.get_node_attributes(g, 'label') nx.draw_networkx_labels(g, pos, labels=labels, Font_Size=9, Font_Color="Black") # Add plt title.title("AI-powered Electric Vehicle Energy Optimization Scheme", FontSize=12) plt.show()

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import matplotlib . pyplot as plt import networkx as nx # Crear un grafo dirigido g = nx . Digraph() # Añadir nodos con sus respectivas etiquetas en español g . add_node("1" , label="Start") g . add_node("2" , label="Current Power Distribution") g . add_node("3" , label="Consumer Assessment") g . add_node("4" , label="Optimization with AI") g . add_node("5" , label="Optimized Distribution") g . add_node("6" , label="Vehicle Application") # Conectar los nodos g . add_edges_From([("1" , "2") , ("2" , "3") , ("3" , "4") , ("4" , "5") , ("5" , "6")]) # Get the positions for the nodes in a pos hierarchical layout = nx . spring_Layout(g , seed=42) # Draw the nx graph . draw(g , pos , with_labels=False , node_Size=3000 , node_Color="lightgreen" , Font_Size=10) # Add labels in Spanish labels = nx . get_node_attributes(g , 'label') nx . draw_networkx_labels(g , pos , labels=labels , Font_Size=9 , Font_Color="Black") # Add plt title . title("AI-powered Electric Vehicle Energy Optimization Scheme" , FontSize=12) plt . show()

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import matplotlib.pyplot as plt import networkx as nx # Crear un grafo dirigido g = nx.Digraph() # Añadir nodos con sus respectivas etiquetas en español g.add_node("1", label="Start") g.add_node("2", label="Current Power Distribution") g.add_node("3", label="Consumer Assessment") g.add_node("4", label="Optimization with AI") g.add_node("5", label="Optimized Distribution") g.add_node("6", label="Vehicle Application") # Conectar los nodos g.add_edges_From([("1", "2"), ("2", "3"), ("3", "4"), ("4", "5"), ("5", "6")]) # Get the positions for the nodes in a pos hierarchical layout = nx.spring_Layout(g, seed=42) # Draw the nx graph.draw(g, pos, with_labels=False, node_Size=3000, node_Color="lightgreen", Font_Size=10) # Add labels in Spanish labels = nx.get_node_attributes(g, 'label') nx.draw_networkx_labels(g, pos, labels=labels, Font_Size=9, Font_Color="Black") # Add plt title.title("AI-powered Electric Vehicle Energy Optimization Scheme", FontSize=12) plt.show()

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