Stable

You, from crimes
Art, to enchant
Relieved, by prayer
Spirits, to enforce
Ending, in despair
— Prospero

CG-BEST represents our Dionysian bequethal.

Dionysus as chaotic energy (static), Athena as the filtering force (temperament), and Apollo shaping the resulting harmony (jazz). Source: DALL-E

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

# Define the relabeled network layers with tattoo-world labels
def define_layers():
    return {
        'Initiation': ['First Ink', 'Street Symbols', 'Gang Affiliation', 'Prison Time', "Underworld Rank", 'Elite Criminal Status'],
        'Recognition': ['Tattoo Codekeepers'],  
        'Authority': ['Hitman Mark', 'Kingpin Insignia'],  
        'Regulation': ['Betrayal Marks', 'Excommunication Symbols', 'Loyalty Seals', ],  
        "Execution": ['Death Mark', 'Blood Oath', 'Punishment Tally', 'Ritual Branding', 'Legacy Inscriptions']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Tattoo Codekeepers'],  
        'paleturquoise': ['Elite Criminal Status', 'Kingpin Insignia', 'Loyalty Seals', 'Legacy Inscriptions'],  
        'lightgreen': ["Underworld Rank", 'Excommunication Symbols', 'Blood Oath', 'Ritual Branding', 'Punishment Tally'],  
        'lightsalmon': ['Gang Affiliation', 'Prison Time', 'Hitman Mark', 'Betrayal Marks', 'Death Mark'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights
def define_edges():
    return {
        ('First Ink', 'Tattoo Codekeepers'): '1/99',
        ('Street Symbols', 'Tattoo Codekeepers'): '5/95',
        ('Gang Affiliation', 'Tattoo Codekeepers'): '20/80',
        ('Prison Time', 'Tattoo Codekeepers'): '51/49',
        ("Underworld Rank", 'Tattoo Codekeepers'): '80/20',
        ('Elite Criminal Status', 'Tattoo Codekeepers'): '95/5',
        ('Tattoo Codekeepers', 'Hitman Mark'): '20/80',
        ('Tattoo Codekeepers', 'Kingpin Insignia'): '80/20',
        ('Hitman Mark', 'Betrayal Marks'): '49/51',
        ('Hitman Mark', 'Excommunication Symbols'): '80/20',
        ('Hitman Mark', 'Loyalty Seals'): '95/5',
        ('Kingpin Insignia', 'Betrayal Marks'): '5/95',
        ('Kingpin Insignia', 'Excommunication Symbols'): '20/80',
        ('Kingpin Insignia', 'Loyalty Seals'): '51/49',
        ('Betrayal Marks', 'Death Mark'): '80/20',
        ('Betrayal Marks', 'Blood Oath'): '85/15',
        ('Betrayal Marks', 'Punishment Tally'): '90/10',
        ('Betrayal Marks', 'Ritual Branding'): '95/5',
        ('Betrayal Marks', 'Legacy Inscriptions'): '99/1',
        ('Excommunication Symbols', 'Death Mark'): '1/9',
        ('Excommunication Symbols', 'Blood Oath'): '1/8',
        ('Excommunication Symbols', 'Punishment Tally'): '1/7',
        ('Excommunication Symbols', 'Ritual Branding'): '1/6',
        ('Excommunication Symbols', 'Legacy Inscriptions'): '1/5',
        ('Loyalty Seals', 'Death Mark'): '1/99',
        ('Loyalty Seals', 'Blood Oath'): '5/95',
        ('Loyalty Seals', 'Punishment Tally'): '10/90',
        ('Loyalty Seals', 'Ritual Branding'): '15/85',
        ('Loyalty Seals', 'Legacy Inscriptions'): '20/80'
    }

# Define edges to be highlighted in black
def define_black_edges():
    return {
        ('First Ink', 'Tattoo Codekeepers'): '1/99',
        ('Street Symbols', 'Tattoo Codekeepers'): '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™: Criminal Ink Network: Ukubona Ubuntu", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 28 Sea, Ship, Nonself, Identity Negotiation, Island. Our neatest narrative yet!

I elect to be optimistic. I'd rather be wrong than choose pessimism. That sea of nihilism is too much to bearw
— Elon Musk