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236 lines
10 KiB
Python
236 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Morphic Dimensionless Topology Analysis
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Analyzes dynamic topology where nanokernel generates morphic dimensionless scalars that self-assign to paths and adapt.
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"""
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import json
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from pathlib import Path
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from typing import Dict, List, Optional
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# Paths
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OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out")
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class MorphicDimensionlessTopology:
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"""Analyzes morphic dimensionless scalar topology for dynamic adaptation."""
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def __init__(self):
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# Morphic dimensionless scalar characteristics
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self.morphic_scalars = {
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"dimensionless": {
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"description": "Dimensionless entities without fixed 3D constraints",
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"significance_score": 95.0
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},
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"morphic": {
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"description": "Can change form/properties based on context",
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"significance_score": 90.0
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},
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"self_assigning": {
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"description": "Instantly decide what path to assign themselves to",
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"significance_score": 95.0
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},
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"adaptive": {
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"description": "Adapt to what they encounter in real-time",
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"significance_score": 90.0
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},
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"nanokernel_generated": {
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"description": "Generated by nanokernel for topology decisions",
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"significance_score": 85.0
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}
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}
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# Current expansion baseline
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self.current_expansion = {
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"total_devices": 42,
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"dual_case_capacity": 223501650896.0924,
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"expansion_factor": 117632447.0
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}
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def analyze_morphic_topology(self) -> Dict:
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"""Analyze morphic dimensionless scalar topology."""
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analysis = {
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"morphic_characteristics": self.morphic_scalars,
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"average_significance_score": sum(e["significance_score"] for e in self.morphic_scalars.values()) / len(self.morphic_scalars),
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"paradigm_shift": {
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"from": "Static 3D component-based topology",
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"to": "Dynamic morphic dimensionless topology",
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"significance": "Fundamental shift from fixed components to adaptive entities"
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},
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"topology_dynamics": {
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"instant_assignment": "Morphic scalars instantly assign to optimal paths",
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"real_time_adaptation": "Adapt to encountered conditions in real-time",
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"context_aware": "Path decisions based on encountered context",
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"self_organizing": "Topology organizes itself through scalar behavior"
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}
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}
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return analysis
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def analyze_morphic_benefits(self) -> Dict:
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"""Analyze morphic topology benefits."""
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benefits = {
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"dynamic_optimization": {
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"description": "Real-time optimization based on current conditions",
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"significance_score": 95.0
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},
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"no_fixed_constraints": {
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"description": "No fixed 3D constraints, unlimited dimensional flexibility",
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"significance_score": 90.0
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},
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"instant_adaptation": {
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"description": "Instant adaptation to encountered conditions",
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"significance_score": 95.0
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},
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"self_organizing": {
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"description": "Topology self-organizes through scalar behavior",
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"significance_score": 85.0
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},
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"path_optimization": {
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"description": "Optimal path selection through self-assignment",
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"significance_score": 90.0
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},
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"resource_efficiency": {
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"description": "Efficient resource utilization through adaptation",
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"significance_score": 80.0
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}
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}
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return benefits
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def calculate_morphic_impact(self) -> Dict:
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"""Calculate morphic topology impact on computational expansion."""
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# Morphic topology multipliers
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paradigm_shift_multiplier = 2.0 # 2x from fundamental paradigm shift
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dynamic_optimization_multiplier = 1.5 # 1.5x from real-time optimization
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no_fixed_constraints_multiplier = 1.5 # 1.5x from dimensional flexibility
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instant_adaptation_multiplier = 1.5 # 1.5x from instant adaptation
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self_organizing_multiplier = 1.3 # 1.3x from self-organization
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path_optimization_multiplier = 1.3 # 1.3x from optimal path selection
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# Calculate expanded capacity with morphic topology
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base_capacity = 1900
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current_dual_case_capacity = 223501650896.0924
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# Apply morphic topology multipliers
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morphic_capacity = (current_dual_case_capacity *
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paradigm_shift_multiplier *
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dynamic_optimization_multiplier *
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no_fixed_constraints_multiplier *
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instant_adaptation_multiplier *
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self_organizing_multiplier *
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path_optimization_multiplier)
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morphic_expansion_factor = morphic_capacity / base_capacity
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morphic_improvement_factor = morphic_capacity / current_dual_case_capacity
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calculation = {
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"base_capacity": base_capacity,
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"current_dual_case_capacity": current_dual_case_capacity,
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"paradigm_shift_multiplier": paradigm_shift_multiplier,
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"dynamic_optimization_multiplier": dynamic_optimization_multiplier,
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"no_fixed_constraints_multiplier": no_fixed_constraints_multiplier,
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"instant_adaptation_multiplier": instant_adaptation_multiplier,
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"self_organizing_multiplier": self_organizing_multiplier,
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"path_optimization_multiplier": path_optimization_multiplier,
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"morphic_capacity": morphic_capacity,
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"morphic_expansion_factor": morphic_expansion_factor,
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"morphic_improvement_factor": morphic_improvement_factor,
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"total_morphic_multiplier": (paradigm_shift_multiplier *
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dynamic_optimization_multiplier *
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no_fixed_constraints_multiplier *
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instant_adaptation_multiplier *
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self_organizing_multiplier *
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path_optimization_multiplier)
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}
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return calculation
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def integrate_morphic_topology(self) -> Dict:
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"""Integrate morphic topology into comprehensive analysis."""
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integration = {
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"morphic_topology_enabled": True,
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"paradigm": "Dynamic morphic dimensionless topology",
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"nanokernel": "Generates morphic dimensionless scalars",
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"characteristics": 5,
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"benefits": 6,
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"math_categories_enhanced": [
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"Geometric Bind (dimensionless topology)",
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"Control Theory (self-organizing)",
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"Information Theory (dynamic optimization)",
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"Physical Bind (morphic adaptation)"
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],
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"foundation_kernels_enhanced": [
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"F08", "F09", "F10", # Geometry (dimensionless)
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"F11", "F12" # Control Theory (self-organizing)
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],
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"fundamental_shift": "From static 3D components to dynamic morphic entities"
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}
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return integration
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def run_analysis(self) -> Dict:
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"""Run morphic dimensionless topology analysis."""
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print("=" * 60)
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print("MORPHIC DIMENSIONLESS TOPOLOGY ANALYSIS")
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print("=" * 60)
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# Step 1: Analyze morphic topology
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print("\n[1/4] Analyzing morphic dimensionless scalar topology...")
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morphic_analysis = self.analyze_morphic_topology()
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print(f" Morphic Characteristics: {len(morphic_analysis['morphic_characteristics'])}")
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for characteristic, details in morphic_analysis['morphic_characteristics'].items():
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print(f" {characteristic}: {details['significance_score']}")
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# Step 2: Analyze benefits
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print("[2/4] Analyzing morphic topology benefits...")
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benefits = self.analyze_morphic_benefits()
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print(f" Benefits: {len(benefits)}")
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for benefit, details in benefits.items():
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print(f" {benefit}: {details['significance_score']}")
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# Step 3: Calculate impact
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print("[3/4] Calculating morphic topology impact...")
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impact_calculation = self.calculate_morphic_impact()
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print(f" Current Dual-Case Capacity: {impact_calculation['current_dual_case_capacity']}")
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print(f" Morphic Capacity: {impact_calculation['morphic_capacity']}")
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print(f" Morphic Improvement Factor: {impact_calculation['morphic_improvement_factor']:.2f}x")
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print(f" Total Morphic Multiplier: {impact_calculation['total_morphic_multiplier']:.2f}x")
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# Step 4: Integrate
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print("[4/4] Integrating morphic topology...")
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integration = self.integrate_morphic_topology()
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print(f" Paradigm: {integration['paradigm']}")
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print(f" Nanokernel: {integration['nanokernel']}")
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print(f" Characteristics: {integration['characteristics']}")
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print(f" Benefits: {integration['benefits']}")
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print("\n" + "=" * 60)
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print("MORPHIC DIMENSIONLESS TOPOLOGY ANALYSIS COMPLETE")
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print("=" * 60)
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return {
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"morphic_analysis": morphic_analysis,
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"benefits_analysis": benefits,
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"impact_calculation": impact_calculation,
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"integration": integration
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}
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if __name__ == '__main__':
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analyzer = MorphicDimensionlessTopology()
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results = analyzer.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "morphic_dimensionless_topology.json"
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with open(output_file, 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\nAnalysis results saved to {output_file}")
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# Print summary
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print("\n" + "=" * 60)
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print("MORPHIC DIMENSIONLESS TOPOLOGY SUMMARY")
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print("=" * 60)
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print(f"Paradigm: {results['integration']['paradigm']}")
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print(f"Morphic Capacity: {results['impact_calculation']['morphic_capacity']}")
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print(f"Morphic Improvement Factor: {results['impact_calculation']['morphic_improvement_factor']:.2f}x")
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print(f"Total Morphic Multiplier: {results['impact_calculation']['total_morphic_multiplier']:.2f}x")
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