Dancing in Chains

Uganda's postcolonial history is a fractal unfolding of the aesthetic imperative vs. moral imposition: really a question of agency!

Dear Alien, although you're not our brother: are you friend or foe (or other)?

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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': ['Mr. Jones,  5%', 'Pigs Doctrine', "Major's Vision", 'Beasts of England', "Napoleons Strategy", 'Snowball Plan'],
        'Voir': ['Rebellion, 20%'],  
        'Choisis': ['Napoleon Enforcer, 50%', 'Snowball Idealist'],  
        'Deviens': ['Exhaution of Animals', 'Dogs, Loyal Enforcers', 'Squealors Propaganda, 20%'],  
        "M'èléve": ['Final Oppression', 'Surveillance', 'Indoctrination', 'Absolute Rule, 5%', 'New Hierarchy']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Rebellion, 20%'],  
        'paleturquoise': ['Snowball Plan', 'Snowball Idealist', 'Squealors Propaganda, 20%', 'New Hierarchy'],  
        'lightgreen': ["Napoleons Strategy", 'Dogs, Loyal Enforcers', 'Surveillance', 'Absolute Rule, 5%', 'Indoctrination'],  
        'lightsalmon': ["Major's Vision", 'Beasts of England', 'Napoleon Enforcer, 50%', 'Exhaution of Animals', 'Final Oppression'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights
def define_edges():
    return {
        ('Mr. Jones,  5%', 'Rebellion, 20%'): '1/99',
        ('Pigs Doctrine', 'Rebellion, 20%'): '5/95',
        ("Major's Vision", 'Rebellion, 20%'): '20/80',
        ('Beasts of England', 'Rebellion, 20%'): '51/49',
        ("Napoleons Strategy", 'Rebellion, 20%'): '80/20',
        ('Snowball Plan', 'Rebellion, 20%'): '95/5',
        ('Rebellion, 20%', 'Napoleon Enforcer, 50%'): '20/80',
        ('Rebellion, 20%', 'Snowball Idealist'): '80/20',
        ('Napoleon Enforcer, 50%', 'Exhaution of Animals'): '49/51',
        ('Napoleon Enforcer, 50%', 'Dogs, Loyal Enforcers'): '80/20',
        ('Napoleon Enforcer, 50%', 'Squealors Propaganda, 20%'): '95/5',
        ('Snowball Idealist', 'Exhaution of Animals'): '5/95',
        ('Snowball Idealist', 'Dogs, Loyal Enforcers'): '20/80',
        ('Snowball Idealist', 'Squealors Propaganda, 20%'): '51/49',
        ('Exhaution of Animals', 'Final Oppression'): '80/20',
        ('Exhaution of Animals', 'Surveillance'): '85/15',
        ('Exhaution of Animals', 'Indoctrination'): '90/10',
        ('Exhaution of Animals', 'Absolute Rule, 5%'): '95/5',
        ('Exhaution of Animals', 'New Hierarchy'): '99/1',
        ('Dogs, Loyal Enforcers', 'Final Oppression'): '1/9',
        ('Dogs, Loyal Enforcers', 'Surveillance'): '1/8',
        ('Dogs, Loyal Enforcers', 'Indoctrination'): '1/7',
        ('Dogs, Loyal Enforcers', 'Absolute Rule, 5%'): '1/6',
        ('Dogs, Loyal Enforcers', 'New Hierarchy'): '1/5',
        ('Squealors Propaganda, 20%', 'Final Oppression'): '1/99',
        ('Squealors Propaganda, 20%', 'Surveillance'): '5/95',
        ('Squealors Propaganda, 20%', 'Indoctrination'): '10/90',
        ('Squealors Propaganda, 20%', 'Absolute Rule, 5%'): '15/85',
        ('Squealors Propaganda, 20%', 'New Hierarchy'): '20/80'
    }

# Define edges to be highlighted in black
def define_black_edges():
    return {
        ('Mr. Jones,  5%', 'Rebellion, 20%'): '1/99',
        ('Pigs Doctrine', 'Rebellion, 20%'): '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("Animal Farm", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 33 Does our narrative herein transcent agent-space-time? How does it with a Tsar in 20th century Russia?