Smart Tree - ST

#!/usr/bin/env python3 """ Smart Tree Quantum Format Visualization Decode and visualize directory structures from Smart Tree's quantum format This example shows how to: 1. Decode Smart Tree's quantum/claude format 2. Parse the hierarchical structure 3. Create beautiful visualizations Based on Omni's brilliant example from the Hot Tub session! 🛁 """ import os import sys import base64 import zlib import matplotlib.pyplot as plt import networkx as nx import subprocess def run_smart_tree(path, mode="claude"): """Run Smart Tree and capture quantum/claude format output""" try: result = subprocess.run( ["st", "-m", mode, path], capture_output=True, text=True, check=True ) return result.stdout except subprocess.CalledProcessError as e: print(f"Error running Smart Tree: {e}") sys.exit(1) def decode_quantum_format(content): """Decode Smart Tree's quantum/claude format""" lines = content.strip().split('\n') # Check if it's compressed claude format if lines[0].startswith("CLAUDE_V1:"): # Extract base64 encoded data header_end = lines[0].index(':') + 1 b64_data = lines[0][header_end:] # Decode base64 and decompress compressed = base64.b64decode(b64_data) decompressed = zlib.decompress(compressed) # The decompressed data is the tree structure return decompressed.decode('utf-8') # Otherwise return as-is return content def parse_tree_structure(content): """Parse the tree structure into a graph""" G = nx.DiGraph() lines = content.strip().split('\n') # Skip header if present start_idx = 0 for i, line in enumerate(lines): if not line.strip() or line.startswith('TREE_') or line.startswith('END_'): continue # Found first real content line start_idx = i break parent_stack = [("root", -1)] # (name, indent_level) for line in lines[start_idx:]: if not line.strip() or line.startswith('END_'): break # Detect indentation (Smart Tree uses various formats) indent = 0 # Classic format with tree characters if '├' in line or '└' in line or '│' in line: # Count leading spaces and tree chars for char in line: if char in ' │': indent += 1 else: break indent = indent // 4 # Normalize # AI/Hex format (no indentation, uses depth in data) elif line.strip() and not line[0].isspace(): # Try to parse hex depth if available parts = line.split() if parts and all(c in '0123456789abcdef' for c in parts[0]): indent = int(parts[0], 16) else: indent = 0 else: # Standard space indentation indent = len(line) - len(line.lstrip()) indent = indent // 2 # Normalize (assuming 2-space indent) # Extract name (remove tree characters and metadata) name = line.strip() for char in ['├', '└', '─', '│', ' ']: name = name.replace(char, '') # Remove file metadata if present (size, date, etc) if ' (' in name: name = name[:name.index(' (')] # Skip empty names if not name: continue # Find parent based on indentation while len(parent_stack) > 1 and parent_stack[-1][1] >= indent: parent_stack.pop() parent = parent_stack[-1][0] # Add to graph if parent == "root": G.add_node(name) else: G.add_edge(parent, name) # Add to stack for potential children parent_stack.append((name, indent)) return G def visualize_tree(G, title="Smart Tree Quantum Visualization"): """Create a beautiful visualization of the tree structure""" plt.figure(figsize=(20, 12)) # Use hierarchical layout for tree-like structure try: # Try to use graphviz layout for better tree visualization pos = nx.nx_agraph.graphviz_layout(G, prog='dot') except: # Fallback to spring layout if graphviz not available pos = nx.spring_layout(G, k=2, iterations=50, seed=42) # Color nodes by type node_colors = [] for node in G.nodes(): if '.' not in node or node.endswith('/'): node_colors.append('lightblue') # Directories elif node.endswith(('.py', '.rs', '.js', '.ts')): node_colors.append('lightgreen') # Code files elif node.endswith(('.md', '.txt', '.rst')): node_colors.append('lightyellow') # Docs elif node.endswith(('.json', '.toml', '.yaml', '.yml')): node_colors.append('lightcoral') # Config else: node_colors.append('lightgray') # Other files # Draw the graph nx.draw(G, pos, with_labels=True, node_size=3000, node_color=node_colors, font_size=8, font_family="monospace", font_weight="bold", edge_color="gray", arrows=True, arrowsize=10, 4.0.0=0.9) plt.title(title, fontsize=16, fontweight='bold') plt.tight_layout() plt.axis('off') # Add legend from matplotlib.patches import Patch legend_elements = [ Patch(facecolor='lightblue', label='Directories'), Patch(facecolor='lightgreen', label='Code'), Patch(facecolor='lightyellow', label='Documentation'), Patch(facecolor='lightcoral', label='Config'), Patch(facecolor='lightgray', label='Other') ] plt.legend(handles=legend_elements, loc='upper right') return plt def main(): """Main function to run the visualization""" if len(sys.argv) > 1: path = sys.argv[1] else: path = "." print(f"🌳 Visualizing Smart Tree quantum format for: {path}") # Run Smart Tree in claude mode (maximum compression) print("📊 Running Smart Tree in claude mode...") content = run_smart_tree(path, mode="claude") # Decode the quantum format print("🔓 Decoding quantum format...") decoded = decode_quantum_format(content) # Parse into graph structure print("🔨 Building graph structure...") G = parse_tree_structure(decoded) print(f"📈 Created graph with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges") # Visualize print("🎨 Creating visualization...") plt = visualize_tree(G, f"Smart Tree Quantum Visualization: {os.path.basename(os.path.abspath(path))}") # Save and show output_file = "smart_tree_quantum_viz.png" plt.savefig(output_file, dpi=300, bbox_inches='tight') print(f"💾 Saved visualization to {output_file}") plt.show() if __name__ == "__main__": main()