06a42cc746
- Add CLAUDE.md with project guidance for Claude Code - Add LaTeX/ with paper and figure generation scripts - Remove papers/ directory (replaced by LaTeX/) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
141 lines
4.8 KiB
Python
141 lines
4.8 KiB
Python
"""
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Generate Slipnet graph visualization (Figure 1)
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Shows conceptual depth as node color gradient, with key Slipnet nodes and connections.
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"""
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import matplotlib.pyplot as plt
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import networkx as nx
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import numpy as np
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# Define key Slipnet nodes with their conceptual depths
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nodes = {
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# Letters (depth 10)
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'a': 10, 'b': 10, 'c': 10, 'd': 10, 'z': 10,
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# Numbers (depth 30)
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'1': 30, '2': 30, '3': 30,
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# String positions (depth 40)
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'leftmost': 40, 'rightmost': 40, 'middle': 40, 'single': 40,
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# Directions (depth 40)
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'left': 40, 'right': 40,
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# Alphabetic positions (depth 60)
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'first': 60, 'last': 60,
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# Bond types (depth 50-80)
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'predecessor': 50, 'successor': 50, 'sameness': 80,
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# Group types (depth 50-80)
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'predecessorGroup': 50, 'successorGroup': 50, 'samenessGroup': 80,
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# Relations (depth 90)
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'identity': 90, 'opposite': 90,
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# Categories (depth 20-90)
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'letterCategory': 30, 'stringPositionCategory': 70,
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'directionCategory': 70, 'bondCategory': 80, 'length': 60,
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}
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# Define edges with their link lengths (inverse = strength)
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edges = [
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# Letter to letterCategory
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('a', 'letterCategory', 97), ('b', 'letterCategory', 97),
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('c', 'letterCategory', 97), ('d', 'letterCategory', 97),
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('z', 'letterCategory', 97),
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# Successor/predecessor relationships
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('a', 'b', 50), ('b', 'c', 50), ('c', 'd', 50),
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('b', 'a', 50), ('c', 'b', 50), ('d', 'c', 50),
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# Bond types to bond category
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('predecessor', 'bondCategory', 60), ('successor', 'bondCategory', 60),
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('sameness', 'bondCategory', 30),
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# Group types
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('sameness', 'samenessGroup', 30),
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('predecessor', 'predecessorGroup', 60),
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('successor', 'successorGroup', 60),
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# Opposite relations
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('left', 'right', 80), ('right', 'left', 80),
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('first', 'last', 80), ('last', 'first', 80),
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# Position relationships
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('left', 'directionCategory', 50), ('right', 'directionCategory', 50),
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('leftmost', 'stringPositionCategory', 50),
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('rightmost', 'stringPositionCategory', 50),
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('middle', 'stringPositionCategory', 50),
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# Slippable connections
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('left', 'leftmost', 90), ('leftmost', 'left', 90),
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('right', 'rightmost', 90), ('rightmost', 'right', 90),
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('leftmost', 'first', 100), ('first', 'leftmost', 100),
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('rightmost', 'last', 100), ('last', 'rightmost', 100),
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# Abstract relations
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('identity', 'bondCategory', 50),
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('opposite', 'bondCategory', 80),
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]
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# Create graph
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G = nx.DiGraph()
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# Add nodes with depth attribute
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for node, depth in nodes.items():
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G.add_node(node, depth=depth)
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# Add edges with link length
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for source, target, length in edges:
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G.add_edge(source, target, length=length, weight=100-length)
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# Create figure
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fig, ax = plt.subplots(figsize=(16, 12))
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# Use hierarchical layout based on depth
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pos = {}
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depth_groups = {}
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for node in G.nodes():
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depth = G.nodes[node]['depth']
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if depth not in depth_groups:
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depth_groups[depth] = []
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depth_groups[depth].append(node)
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# Position nodes by depth (y-axis) and spread horizontally
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for depth, node_list in depth_groups.items():
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y = 1.0 - (depth / 100.0) # Invert so shallow nodes at top
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for i, node in enumerate(node_list):
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x = (i - len(node_list)/2) / max(len(node_list), 10) * 2.5
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pos[node] = (x, y)
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# Get node colors based on depth (blue=shallow/concrete, red=deep/abstract)
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node_colors = [G.nodes[node]['depth'] for node in G.nodes()]
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# Draw edges with thickness based on strength (inverse of link length)
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edges_to_draw = G.edges()
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edge_widths = [0.3 + (100 - G[u][v]['length']) / 100.0 * 3 for u, v in edges_to_draw]
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nx.draw_networkx_edges(G, pos, edgelist=edges_to_draw, width=edge_widths,
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alpha=0.3, arrows=True, arrowsize=10,
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connectionstyle='arc3,rad=0.1', ax=ax)
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# Draw nodes
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nx.draw_networkx_nodes(G, pos, node_color=node_colors,
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node_size=800, cmap='coolwarm',
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vmin=0, vmax=100, ax=ax)
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# Draw labels
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nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)
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# Add colorbar
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sm = plt.cm.ScalarMappable(cmap='coolwarm',
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norm=plt.Normalize(vmin=0, vmax=100))
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sm.set_array([])
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cbar = plt.colorbar(sm, ax=ax, fraction=0.046, pad=0.04)
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cbar.set_label('Conceptual Depth', rotation=270, labelpad=20, fontsize=12)
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ax.set_title('Slipnet Graph Structure\n' +
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'Color gradient: Blue (concrete/shallow) → Red (abstract/deep)\n' +
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'Edge thickness: Link strength (inverse of link length)',
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fontsize=14, fontweight='bold', pad=20)
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ax.axis('off')
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plt.tight_layout()
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plt.savefig('figure1_slipnet_graph.pdf', dpi=300, bbox_inches='tight')
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plt.savefig('figure1_slipnet_graph.png', dpi=300, bbox_inches='tight')
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print("Generated figure1_slipnet_graph.pdf and .png")
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plt.close()
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