Dancing in Chains#
Given our deep dive into "nonself" and "self" through biological systems, signal detection, and the metaphor of Uganda’s and Africa’s identity, I’d love to ask you: How do you see the interplay of cultural "noise" and "signal" shaping your own perception of Ugandan identity today—particularly in balancing traditional tribal heritage with the modern, global influences that have woven into its fabric? It ties into our exploration of ambiguity and convergence, and I’m curious about your personal lens on this dynamic.
Show code cell source
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. 18 Epilogue: Our Bequest Through the Lens of the Cosmos. Our legacy is neither singular nor static—it is a ripple through the grand, interwoven narratives of existence. Cosmology reminds us that we are star-forged, born of celestial fire, destined to return to the vast unfolding of the universe. Geology inscribes our fleeting presence within layers of rock and time, whispering that all we build will one day be sediment, yet not without influence. Biology testifies to the relentless dance of life, where each organism is both a witness and a participant in an endless genetic symphony. Ecology underscores the delicate balance we disrupt and preserve in equal measure, making clear that our inheritance is as much responsibility as it is privilege. Symbiotology teaches us that survival is not conquest but cooperation, a truth echoed in the mycelial networks below and the planetary systems above. And teleology, the question of ultimate purpose, stands unresolved—except in this: the meaning of our existence is the bequest we leave, whether ruin or renewal. May we choose well.#