Dancing in Chains

Fig. 33 Freedom in Fetters—a Princely Freedom. Chopin, the last of the modern musicians, who gazed at and worshipped beauty, like Leopardi; Chopin, the Pole, the inimitable (none that came before or after him has a right to this name)—Chopin had the same princely punctilio in convention (grammar, space) that Raphael shows in the use of the simplest traditional colours. The only difference is that Chopin applies them not to colour but to melodic and rhythmic traditions (prosody, time). He admitted the validity of these traditions because he was born under the sway of etiquette. But in these fetters he plays and dances as the freest and daintiest of spirits, and, be it observed, he does not spurn the chain. Source: Human All-Too-Human Part II

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

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['Genome,  5%', 'Culture', 'Nourish It', 'Know It', "Move It", 'Injure It'],  # Static
        'Voir': ['Exposome, 15%'],  
        'Choisis': ['Metabolome, 50%', 'Basal Metabolic Rate'],  
        'Deviens': ['Unstructured-Intense', 'Weekly-Calendar', 'Proteome, 25%'],  
        "M'èléve": ['NexToken Prediction', 'Hydration', 'Fat-Muscle Ratio', 'Amor Fatì, 5%', 'Existential Cadence']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Exposome, 15%'],  
        'paleturquoise': ['Injure It', 'Basal Metabolic Rate', 'Proteome, 25%', 'Existential Cadence'],  
        'lightgreen': ["Move It", 'Weekly-Calendar', 'Hydration', 'Amor Fatì, 5%', 'Fat-Muscle Ratio'],  
        'lightsalmon': ['Nourish It', 'Know It', 'Metabolome, 50%', 'Unstructured-Intense', 'NexToken Prediction'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Genome,  5%', 'Exposome, 15%'): '1/99',
        ('Culture', 'Exposome, 15%'): '5/95',
        ('Nourish It', 'Exposome, 15%'): '20/80',
        ('Know It', 'Exposome, 15%'): '51/49',
        ("Move It", 'Exposome, 15%'): '80/20',
        ('Injure It', 'Exposome, 15%'): '95/5',
        ('Exposome, 15%', 'Metabolome, 50%'): '20/80',
        ('Exposome, 15%', 'Basal Metabolic Rate'): '80/20',
        ('Metabolome, 50%', 'Unstructured-Intense'): '49/51',
        ('Metabolome, 50%', 'Weekly-Calendar'): '80/20',
        ('Metabolome, 50%', 'Proteome, 25%'): '95/5',
        ('Basal Metabolic Rate', 'Unstructured-Intense'): '5/95',
        ('Basal Metabolic Rate', 'Weekly-Calendar'): '20/80',
        ('Basal Metabolic Rate', 'Proteome, 25%'): '51/49',
        ('Unstructured-Intense', 'NexToken Prediction'): '80/20',
        ('Unstructured-Intense', 'Hydration'): '85/15',
        ('Unstructured-Intense', 'Fat-Muscle Ratio'): '90/10',
        ('Unstructured-Intense', 'Amor Fatì, 5%'): '95/5',
        ('Unstructured-Intense', 'Existential Cadence'): '99/1',
        ('Weekly-Calendar', 'NexToken Prediction'): '1/9',
        ('Weekly-Calendar', 'Hydration'): '1/8',
        ('Weekly-Calendar', 'Fat-Muscle Ratio'): '1/7',
        ('Weekly-Calendar', 'Amor Fatì, 5%'): '1/6',
        ('Weekly-Calendar', 'Existential Cadence'): '1/5',
        ('Proteome, 25%', 'NexToken Prediction'): '1/99',
        ('Proteome, 25%', 'Hydration'): '5/95',
        ('Proteome, 25%', 'Fat-Muscle Ratio'): '10/90',
        ('Proteome, 25%', 'Amor Fatì, 5%'): '15/85',
        ('Proteome, 25%', 'Existential Cadence'): '20/80'
    }

# Calculate positions for nodes
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()
    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
    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)
    
    # 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='gray',
        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™: Heredity, Lifestyle, Badluck", fontsize=25)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 34 G1-G3: Ganglia & N1-N5 Nuclei. These are cranial nerve, dorsal-root (G1 & G2); basal ganglia, thalamus, hypothalamus (N1, N2, N3); and brain stem and cerebelum (N4 & N5).