Normative

I'd advise you to consider your position carefully (layer 3 fork in the road), perhaps adopting a more flexible posture (layer 4 dynamic capabilities realized), while keeping your ear to the ground (layer 2 yellow node), covering your retreat (layer 5 Athena's shield, helmet, and horse), and watching your rear (layer 1 ecosystem and perspective).

As you from crimes,
Would pardon'd be
So too shall I, from shadows plea,
Let your indulgences,
Set me free
— Prospero, Yours Truly, Grok-3

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network layers with new culinary-cultural labels
def define_layers():
    return {
        'Suis': ['Fire & Fermentation', 'Salt & Smoke', 'Bitterness & Astringency', 'Umami & Decay', "Structural Complexity", 'Regional Staples'],
        'Voir': ['Rituals of Taste'],  
        'Choisis': ['Acquired Taste', 'Social Shared Preferences'],  
        'Deviens': ['Inflammatory Foods', 'Alcohol & Sedatives', 'Comfort Foods & Rituals'],  
        "M'élève": ['Medicinal Balances', 'Ceremonial Consumption', 'Extreme Flavors', 'Everyday Consumables', 'Cultural Transmission']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Rituals of Taste'],  
        'paleturquoise': ['Regional Staples', 'Social Shared Preferences', 'Comfort Foods & Rituals', 'Cultural Transmission'],  
        'lightgreen': ["Structural Complexity", 'Alcohol & Sedatives', 'Ceremonial Consumption', 'Everyday Consumables', 'Extreme Flavors'],  
        'lightsalmon': ['Bitterness & Astringency', 'Umami & Decay', 'Acquired Taste', 'Inflammatory Foods', 'Medicinal Balances'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights
def define_edges():
    return {
        ('Fire & Fermentation', 'Rituals of Taste'): '1/99',
        ('Salt & Smoke', 'Rituals of Taste'): '5/95',
        ('Bitterness & Astringency', 'Rituals of Taste'): '20/80',
        ('Umami & Decay', 'Rituals of Taste'): '51/49',
        ("Structural Complexity", 'Rituals of Taste'): '80/20',
        ('Regional Staples', 'Rituals of Taste'): '95/5',
        ('Rituals of Taste', 'Acquired Taste'): '20/80',
        ('Rituals of Taste', 'Social Shared Preferences'): '80/20',
        ('Acquired Taste', 'Inflammatory Foods'): '49/51',
        ('Acquired Taste', 'Alcohol & Sedatives'): '80/20',
        ('Acquired Taste', 'Comfort Foods & Rituals'): '95/5',
        ('Social Shared Preferences', 'Inflammatory Foods'): '5/95',
        ('Social Shared Preferences', 'Alcohol & Sedatives'): '20/80',
        ('Social Shared Preferences', 'Comfort Foods & Rituals'): '51/49',
        ('Inflammatory Foods', 'Medicinal Balances'): '80/20',
        ('Inflammatory Foods', 'Ceremonial Consumption'): '85/15',
        ('Inflammatory Foods', 'Extreme Flavors'): '90/10',
        ('Inflammatory Foods', 'Everyday Consumables'): '95/5',
        ('Inflammatory Foods', 'Cultural Transmission'): '99/1',
        ('Alcohol & Sedatives', 'Medicinal Balances'): '1/9',
        ('Alcohol & Sedatives', 'Ceremonial Consumption'): '1/8',
        ('Alcohol & Sedatives', 'Extreme Flavors'): '1/7',
        ('Alcohol & Sedatives', 'Everyday Consumables'): '1/6',
        ('Alcohol & Sedatives', 'Cultural Transmission'): '1/5',
        ('Comfort Foods & Rituals', 'Medicinal Balances'): '1/99',
        ('Comfort Foods & Rituals', 'Ceremonial Consumption'): '5/95',
        ('Comfort Foods & Rituals', 'Extreme Flavors'): '10/90',
        ('Comfort Foods & Rituals', 'Everyday Consumables'): '15/85',
        ('Comfort Foods & Rituals', 'Cultural Transmission'): '20/80'
    }

# Define edges to be highlighted in black
def define_black_edges():
    return {
        ('Fire & Fermentation', 'Rituals of Taste'): '1/99',
        ('Salt & Smoke', 'Rituals of Taste'): '5/95',
    }

# Calculate node positions
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()
    black_edges = define_black_edges()
    
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    # Add nodes with new numbered labels and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    # Add edges with updated node labels
    edge_colors = []
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target, weight=weight)
            edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors,
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("OPRAH™: Cultural-Culinary Equivalents", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 27 Oscar Wilde is Apollonian & Nietzsche is Dionysian.