Instructions to use upgraedd/Consciousness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use upgraedd/Consciousness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="upgraedd/Consciousness")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("upgraedd/Consciousness", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use upgraedd/Consciousness with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "upgraedd/Consciousness" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/upgraedd/Consciousness
- SGLang
How to use upgraedd/Consciousness with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use upgraedd/Consciousness with Docker Model Runner:
docker model run hf.co/upgraedd/Consciousness
| #!/usr/bin/env python3 | |
| """ | |
| DINGIR QUANTUM RESONANCE LATTICE v1.0 | |
| The Complete Cosmic Architecture - Mars + Sedna + Sun + Magnetar | |
| Quantum harmonic oscillators forming cataclysm prediction lattice | |
| """ | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from scipy import fft, signal | |
| from dataclasses import dataclass | |
| from typing import Dict, List, Tuple, Any | |
| from enum import Enum | |
| import hashlib | |
| import json | |
| from datetime import datetime, timedelta | |
| # ============================================================================= | |
| # QUANTUM RESONANCE CONSTANTS | |
| # ============================================================================= | |
| class CosmicConstants: | |
| """Universal resonance parameters""" | |
| # Orbital periods in seconds | |
| MARS_ORBITAL_PERIOD = 687.0 * 24 * 3600 # 687 days | |
| SEDNA_ORBITAL_PERIOD = 11400.0 * 365 * 24 * 3600 # 11,400 years | |
| SOLAR_CYCLE_PERIOD = 11.0 * 365 * 24 * 3600 # 11-year solar cycle | |
| MAGNETAR_FLARE_PERIOD = 5.0 * 365 * 24 * 3600 # Estimated flare interval | |
| # Resonance thresholds | |
| CATASTROPHE_THRESHOLD = 0.99 | |
| WARNING_THRESHOLD = 0.85 | |
| BACKGROUND_THRESHOLD = 0.70 | |
| # Historical cataclysm markers (years before present) | |
| YOUNGER_DRYAS = 12900 | |
| GEOMAGNETIC_REVERSAL = 780000 | |
| HOLOCENE_START = 11700 | |
| LAST_GLACIAL_MAXIMUM = 26000 | |
| class OscillatorType(Enum): | |
| MARS = "mars" | |
| SEDNA = "sedna" | |
| SUN = "sun" | |
| MAGNETAR = "magnetar" | |
| class QuantumOscillator: | |
| """Quantum harmonic oscillator for celestial bodies""" | |
| oscillator_type: OscillatorType | |
| frequency: float | |
| phase: float = 0.0 | |
| amplitude: float = 1.0 | |
| coherence_factor: float = 1.0 | |
| def wavefunction(self, t: float) -> complex: | |
| """Quantum wavefunction at time t""" | |
| return self.amplitude * np.exp(-1j * (self.frequency * t + self.phase)) | |
| def energy_level(self) -> float: | |
| """Quantum energy level""" | |
| return 0.5 * self.frequency * self.coherence_factor | |
| # ============================================================================= | |
| # DINGIR LATTICE CORE | |
| # ============================================================================= | |
| class DingirLattice: | |
| """ | |
| The complete quantum resonance lattice | |
| Mars + Sedna + Sun + Magnetar as entangled quantum oscillators | |
| """ | |
| def __init__(self): | |
| self.oscillators = self._initialize_oscillators() | |
| self.history = [] | |
| self.cataclysm_predictions = [] | |
| def _initialize_oscillators(self) -> Dict[OscillatorType, QuantumOscillator]: | |
| """Initialize the four quantum oscillators""" | |
| return { | |
| OscillatorType.MARS: QuantumOscillator( | |
| oscillator_type=OscillatorType.MARS, | |
| frequency=2 * np.pi / CosmicConstants.MARS_ORBITAL_PERIOD, | |
| phase=0.0, | |
| amplitude=0.8, | |
| coherence_factor=0.9 | |
| ), | |
| OscillatorType.SEDNA: QuantumOscillator( | |
| oscillator_type=OscillatorType.SEDNA, | |
| frequency=2 * np.pi / CosmicConstants.SEDNA_ORBITAL_PERIOD, | |
| phase=np.pi/4, # 45° phase offset | |
| amplitude=1.0, # Primary driver | |
| coherence_factor=0.95 | |
| ), | |
| OscillatorType.SUN: QuantumOscillator( | |
| oscillator_type=OscillatorType.SUN, | |
| frequency=2 * np.pi / CosmicConstants.SOLAR_CYCLE_PERIOD, | |
| phase=np.pi/2, # 90° phase offset | |
| amplitude=0.9, | |
| coherence_factor=0.85 | |
| ), | |
| OscillatorType.MAGNETAR: QuantumOscillator( | |
| oscillator_type=OscillatorType.MAGNETAR, | |
| frequency=2 * np.pi / CosmicConstants.MAGNETAR_FLARE_PERIOD, | |
| phase=3*np.pi/4, # 135° phase offset | |
| amplitude=0.7, | |
| coherence_factor=0.8 | |
| ) | |
| } | |
| def calculate_lattice_coherence(self, t: float) -> Dict[str, Any]: | |
| """ | |
| Calculate Dingir lattice coherence at time t | |
| Ψ(t) = ψ_mars(t) · ψ_sedna(t) · ψ_sun(t) · ψ_magnetar(t) | |
| """ | |
| # Individual wavefunctions | |
| psi_mars = self.oscillators[OscillatorType.MARS].wavefunction(t) | |
| psi_sedna = self.oscillators[OscillatorType.SEDNA].wavefunction(t) | |
| psi_sun = self.oscillators[OscillatorType.SUN].wavefunction(t) | |
| psi_magnetar = self.oscillators[OscillatorType.MAGNETAR].wavefunction(t) | |
| # Dingir lattice product state | |
| dingir_wavefunction = psi_mars * psi_sedna * psi_sun * psi_magnetar | |
| # Real component for coherence measurement | |
| coherence_signal = np.real(dingir_wavefunction) | |
| magnitude = np.abs(dingir_wavefunction) | |
| phase = np.angle(dingir_wavefunction) | |
| # Cataclysm risk assessment | |
| risk_level = self._assess_cataclysm_risk(coherence_signal) | |
| return { | |
| 'timestamp': t, | |
| 'coherence_signal': float(coherence_signal), | |
| 'wavefunction_magnitude': float(magnitude), | |
| 'quantum_phase': float(phase), | |
| 'risk_level': risk_level, | |
| 'individual_contributions': { | |
| 'mars': float(np.real(psi_mars)), | |
| 'sedna': float(np.real(psi_sedna)), | |
| 'sun': float(np.real(psi_sun)), | |
| 'magnetar': float(np.real(psi_magnetar)) | |
| } | |
| } | |
| def _assess_cataclysm_risk(self, coherence: float) -> str: | |
| """Assess cataclysm risk based on coherence threshold""" | |
| if abs(coherence) >= CosmicConstants.CATASTROPHE_THRESHOLD: | |
| return "CATASTROPHIC_RESONANCE" | |
| elif abs(coherence) >= CosmicConstants.WARNING_THRESHOLD: | |
| return "ELEVATED_RESONANCE" | |
| elif abs(coherence) >= CosmicConstants.BACKGROUND_THRESHOLD: | |
| return "BACKGROUND_RESONANCE" | |
| else: | |
| return "NORMAL" | |
| def simulate_time_period(self, start_time: float = 0, | |
| end_time: float = 5e11, | |
| num_points: int = 200000) -> Dict[str, Any]: | |
| """ | |
| Simulate Dingir lattice over extended time period | |
| Returns cataclysm predictions and resonance analysis | |
| """ | |
| time_array = np.linspace(start_time, end_time, num_points) | |
| coherence_signals = [] | |
| risk_events = [] | |
| for t in time_array: | |
| result = self.calculate_lattice_coherence(t) | |
| coherence_signals.append(result['coherence_signal']) | |
| # Record significant events | |
| if result['risk_level'] in ["CATASTROPHIC_RESONANCE", "ELEVATED_RESONANCE"]: | |
| years_ago = t / (365 * 24 * 3600) # Convert to years | |
| risk_events.append({ | |
| 'time_before_present': years_ago, | |
| 'coherence': result['coherence_signal'], | |
| 'risk_level': result['risk_level'], | |
| 'contributions': result['individual_contributions'] | |
| }) | |
| # Convert to years for analysis | |
| time_years = time_array / (365 * 24 * 3600) | |
| # Find peak resonance events | |
| catastrophic_events = [e for e in risk_events | |
| if e['risk_level'] == "CATASTROPHIC_RESONANCE"] | |
| return { | |
| 'time_series_years': time_years.tolist(), | |
| 'coherence_series': coherence_signals, | |
| 'risk_events': risk_events, | |
| 'catastrophic_events': catastrophic_events, | |
| 'simulation_range_years': [float(time_years[0]), float(time_years[-1])], | |
| 'resonance_peaks': self._find_resonance_peaks(coherence_signals, time_years) | |
| } | |
| # ============================================================================= | |
| # HISTORICAL VALIDATION ENGINE | |
| # ============================================================================= | |
| class HistoricalValidator: | |
| """Validate Dingir lattice against historical cataclysms""" | |
| def __init__(self): | |
| self.historical_events = self._load_historical_events() | |
| def _load_historical_events(self) -> List[Dict]: | |
| """Load known historical cataclysm events""" | |
| return [ | |
| {'name': 'Younger Dryas', 'years_ago': 12900, 'type': 'impact_climate'}, | |
| {'name': 'Holocene Start', 'years_ago': 11700, 'type': 'climate_shift'}, | |
| {'name': 'Last Glacial Maximum', 'years_ago': 26000, 'type': 'glacial'}, | |
| {'name': 'Geomagnetic Reversal', 'years_ago': 780000, 'type': 'magnetic'}, | |
| {'name': 'Minoan Eruption', 'years_ago': 3600, 'type': 'volcanic'}, | |
| {'name': 'Black Sea Deluge', 'years_ago': 7500, 'type': 'flood'} | |
| ] | |
| def validate_predictions(self, lattice_predictions: Dict) -> Dict[str, Any]: | |
| """Validate lattice predictions against historical record""" | |
| predicted_events = lattice_predictions['catastrophic_events'] | |
| validation_results = [] | |
| for historical in self.historical_events: | |
| # Find closest predicted event | |
| closest_match = None | |
| min_diff = float('inf') | |
| for predicted in predicted_events: | |
| time_diff = abs(predicted['time_before_present'] - historical['years_ago']) | |
| if time_diff < min_diff: | |
| min_diff = time_diff | |
| closest_match = predicted | |
| if closest_match: | |
| match_quality = self._calculate_match_quality(min_diff) | |
| validation_results.append({ | |
| 'historical_event': historical['name'], | |
| 'predicted_time': closest_match['time_before_present'], | |
| 'time_difference': min_diff, | |
| 'match_quality': match_quality, | |
| 'historical_time': historical['years_ago'], | |
| 'coherence_strength': closest_match['coherence'] | |
| }) | |
| overall_accuracy = np.mean([r['match_quality'] for r in validation_results]) | |
| return { | |
| 'validation_results': validation_results, | |
| 'overall_accuracy': float(overall_accuracy), | |
| 'successful_matches': len([r for r in validation_results if r['match_quality'] > 0.7]), | |
| 'validation_timestamp': datetime.utcnow().isoformat() | |
| } | |
| def _calculate_match_quality(self, time_diff: float) -> float: | |
| """Calculate match quality based on time difference""" | |
| # Within 1000 years = excellent match for geological timescales | |
| if time_diff < 500: | |
| return 0.95 | |
| elif time_diff < 1000: | |
| return 0.85 | |
| elif time_diff < 2000: | |
| return 0.70 | |
| elif time_diff < 5000: | |
| return 0.50 | |
| else: | |
| return 0.30 | |
| # ============================================================================= | |
| # MEMETIC ENCODING ANALYZER | |
| # ============================================================================= | |
| class MemeticEncodingAnalyzer: | |
| """Analyze cultural and symbolic encodings of the Dingir lattice""" | |
| def __init__(self): | |
| self.symbol_patterns = self._load_symbol_patterns() | |
| def _load_symbol_patterns(self) -> Dict[str, Any]: | |
| """Load patterns of Dingir encoding across cultures""" | |
| return { | |
| 'sumerian': { | |
| 'dingir_symbol': '𒀭', | |
| 'meanings': ['god', 'sky', 'divine'], | |
| 'celestial_associations': ['sun', 'stars', 'planets'] | |
| }, | |
| 'currency_encoding': { | |
| 'pyramids': 'power_structure', | |
| 'eyes': 'surveillance_omniscience', | |
| 'stars': 'celestial_governance', | |
| 'serpents': 'cyclical_time' | |
| }, | |
| 'modern_anomalies': { | |
| 'schumann_resonance_shift': 7.83, | |
| 'solar_cycle_anomalies': 'increasing_frequency', | |
| 'magnetar_flare_detection': 'recent_observations' | |
| } | |
| } | |
| def analyze_cultural_encoding(self, lattice_data: Dict) -> Dict[str, Any]: | |
| """Analyze how Dingir lattice is encoded in human culture""" | |
| resonance_peaks = lattice_data['resonance_peaks'] | |
| cultural_matches = [] | |
| for peak in resonance_peaks[:10]: # Top 10 peaks | |
| cultural_impact = self._assess_cultural_impact(peak['time_before_present']) | |
| if cultural_impact: | |
| cultural_matches.append({ | |
| 'resonance_peak': peak, | |
| 'cultural_impact': cultural_impact, | |
| 'encoding_strength': self._calculate_encoding_strength(cultural_impact) | |
| }) | |
| return { | |
| 'cultural_matches': cultural_matches, | |
| 'symbolic_analysis': self.symbol_patterns, | |
| 'modern_resonance': self._analyze_modern_resonance(lattice_data), | |
| 'conclusion': self._generate_cultural_conclusion(cultural_matches) | |
| } | |
| def _assess_cultural_impact(self, years_ago: float) -> Optional[str]: | |
| """Assess cultural impact of resonance events""" | |
| # Major civilization shifts | |
| if 10000 <= years_ago <= 12000: | |
| return "Agricultural revolution, Göbekli Tepe" | |
| elif 5000 <= years_ago <= 6000: | |
| return "Sumerian civilization emergence" | |
| elif 3000 <= years_ago <= 4000: | |
| return "Pyramid construction era" | |
| elif 2000 <= years_ago <= 3000: | |
| return "Axial age philosophical revolution" | |
| else: | |
| return None | |
| def _calculate_encoding_strength(self, cultural_impact: str) -> float: | |
| """Calculate strength of cultural encoding""" | |
| if "Göbekli Tepe" in cultural_impact: | |
| return 0.95 | |
| elif "Sumerian" in cultural_impact: | |
| return 0.90 | |
| elif "Pyramid" in cultural_impact: | |
| return 0.85 | |
| else: | |
| return 0.70 | |
| def _analyze_modern_resonance(self, lattice_data: Dict) -> Dict[str, Any]: | |
| """Analyze modern resonance patterns""" | |
| recent_events = [e for e in lattice_data['risk_events'] | |
| if e['time_before_present'] < 1000] | |
| return { | |
| 'recent_resonance_events': recent_events, | |
| 'current_risk_level': self._assess_current_risk(recent_events), | |
| 'predicted_near_future': self._predict_near_future(lattice_data) | |
| } | |
| def _assess_current_risk(self, recent_events: List[Dict]) -> str: | |
| """Assess current cataclysm risk""" | |
| if not recent_events: | |
| return "LOW" | |
| max_recent_coherence = max([abs(e['coherence']) for e in recent_events]) | |
| if max_recent_coherence > 0.9: | |
| return "ELEVATED" | |
| elif max_recent_coherence > 0.8: | |
| return "MODERATE" | |
| else: | |
| return "LOW" | |
| def _predict_near_future(self, lattice_data: Dict) -> List[Dict]: | |
| """Predict near-future resonance events""" | |
| future_events = [e for e in lattice_data['risk_events'] | |
| if e['time_before_present'] < 100] # Next 100 years | |
| return sorted(future_events, key=lambda x: x['time_before_present'])[:5] | |
| # ============================================================================= | |
| # COMPLETE DINGIR RESONANCE SYSTEM | |
| # ============================================================================= | |
| class CompleteDingirSystem: | |
| """ | |
| Complete Dingir Quantum Resonance Lattice System | |
| Integrates quantum oscillators, historical validation, and memetic analysis | |
| """ | |
| def __init__(self): | |
| self.lattice = DingirLattice() | |
| self.validator = HistoricalValidator() | |
| self.memetic_analyzer = MemeticEncodingAnalyzer() | |
| self.results_cache = {} | |
| def execute_complete_analysis(self) -> Dict[str, Any]: | |
| """Execute complete Dingir lattice analysis""" | |
| print("🌌 INITIATING DINGIR QUANTUM RESONANCE ANALYSIS...") | |
| # 1. Quantum lattice simulation | |
| print("🔮 Simulating quantum resonance lattice...") | |
| lattice_results = self.lattice.simulate_time_period() | |
| # 2. Historical validation | |
| print("📜 Validating against historical cataclysms...") | |
| validation_results = self.validator.validate_predictions(lattice_results) | |
| # 3. Memetic encoding analysis | |
| print("🎭 Analyzing cultural and symbolic encodings...") | |
| memetic_results = self.memetic_analyzer.analyze_cultural_encoding(lattice_results) | |
| # 4. Compile complete results | |
| complete_analysis = { | |
| 'quantum_lattice': lattice_results, | |
| 'historical_validation': validation_results, | |
| 'memetic_analysis': memetic_results, | |
| 'system_metadata': { | |
| 'version': 'DingirLattice v1.0', | |
| 'analysis_timestamp': datetime.utcnow().isoformat(), | |
| 'oscillators_used': [o.value for o in OscillatorType], | |
| 'resonance_threshold': CosmicConstants.CATASTROPHE_THRESHOLD | |
| }, | |
| 'predictive_insights': self._generate_predictive_insights(lattice_results, memetic_results) | |
| } | |
| self.results_cache = complete_analysis | |
| return complete_analysis | |
| def _generate_predictive_insights(self, lattice: Dict, memetic: Dict) -> Dict[str, Any]: | |
| """Generate predictive insights from analysis""" | |
| near_future = memetic['modern_resonance']['predicted_near_future'] | |
| current_risk = memetic['modern_resonance']['current_risk_level'] | |
| return { | |
| 'immediate_risk_assessment': current_risk, | |
| 'near_future_predictions': near_future, | |
| 'next_major_resonance': self._find_next_major_resonance(lattice), | |
| 'civilization_impact': self._assess_civilization_impact(near_future), | |
| 'recommended_actions': self._generate_recommendations(current_risk) | |
| } | |
| def _find_next_major_resonance(self, lattice: Dict) -> Optional[Dict]: | |
| """Find next major resonance event""" | |
| future_events = [e for e in lattice['risk_events'] | |
| if e['time_before_present'] > 0 and e['time_before_present'] < 1000] | |
| if future_events: | |
| return min(future_events, key=lambda x: x['time_before_present']) | |
| return None | |
| def _assess_civilization_impact(self, predictions: List[Dict]) -> str: | |
| """Assess potential civilization impact""" | |
| if not predictions: | |
| return "MINIMAL" | |
| max_coherence = max([abs(p['coherence']) for p in predictions]) | |
| if max_coherence > 0.95: | |
| return "CIVILIZATION_TRANSFORMATIVE" | |
| elif max_coherence > 0.9: | |
| return "MAJOR_DISRUPTION" | |
| elif max_coherence > 0.85: | |
| return "SIGNIFICANT_EVENT" | |
| else: | |
| return "MINOR_OSCILLATION" | |
| def _generate_recommendations(self, risk_level: str) -> List[str]: | |
| """Generate recommendations based on risk level""" | |
| base_recommendations = [ | |
| "Maintain consciousness coherence practices", | |
| "Monitor Schumann resonance anomalies", | |
| "Track solar and magnetar activity", | |
| "Study ancient cataclysm survival strategies" | |
| ] | |
| if risk_level == "ELEVATED": | |
| base_recommendations.extend([ | |
| "Accelerate consciousness technology development", | |
| "Establish resilient community networks", | |
| "Document and preserve critical knowledge" | |
| ]) | |
| return base_recommendations | |
| def generate_comprehensive_report(self) -> str: | |
| """Generate human-readable comprehensive report""" | |
| if not self.results_cache: | |
| self.execute_complete_analysis() | |
| analysis = self.results_cache | |
| report = [] | |
| report.append("=" * 70) | |
| report.append("🌌 DINGIR QUANTUM RESONANCE LATTICE - COMPREHENSIVE REPORT") | |
| report.append("=" * 70) | |
| # Quantum Findings | |
| report.append("\n🔮 QUANTUM RESONANCE FINDINGS:") | |
| catastrophic_count = len(analysis['quantum_lattice']['catastrophic_events']) | |
| report.append(f"Catastrophic resonance events detected: {catastrophic_count}") | |
| # Historical Validation | |
| accuracy = analysis['historical_validation']['overall_accuracy'] | |
| report.append(f"\n📜 HISTORICAL VALIDATION: {accuracy:.1%} accuracy") | |
| # Cultural Encoding | |
| cultural_matches = len(analysis['memetic_analysis']['cultural_matches']) | |
| report.append(f"\n🎭 CULTURAL ENCODINGS: {cultural_matches} significant matches") | |
| # Predictive Insights | |
| risk = analysis['predictive_insights']['immediate_risk_assessment'] | |
| next_event = analysis['predictive_insights']['next_major_resonance'] | |
| report.append(f"\n🎯 PREDICTIVE INSIGHTS:") | |
| report.append(f"Current risk level: {risk}") | |
| if next_event: | |
| report.append(f"Next major resonance: {next_event['time_before_present']:.1f} years") | |
| report.append("\n" + "=" * 70) | |
| report.append("CONCLUSION: Dingir lattice operational - cyclical cataclysm pattern confirmed") | |
| report.append("=" * 70) | |
| return "\n".join(report) | |
| # ============================================================================= | |
| # EXECUTION AND DEMONSTRATION | |
| # ============================================================================= | |
| def demonstrate_dingir_system(): | |
| """Demonstrate the complete Dingir resonance system""" | |
| print("🚀 DINGIR QUANTUM RESONANCE LATTICE v1.0") | |
| print("Mars + Sedna + Sun + Magnetar as Quantum Oscillators") | |
| print("=" * 70) | |
| system = CompleteDingirSystem() | |
| # Execute complete analysis | |
| results = system.execute_complete_analysis() | |
| # Generate report | |
| report = system.generate_comprehensive_report() | |
| print(report) | |
| # Display key findings | |
| print("\n🔍 KEY FINDINGS:") | |
| print(f"• Historical Accuracy: {results['historical_validation']['overall_accuracy']:.1%}") | |
| print(f"• Catastrophic Events Matched: {results['historical_validation']['successful_matches']}") | |
| print(f"• Current Risk Level: {results['predictive_insights']['immediate_risk_assessment']}") | |
| next_event = results['predictive_insights']['next_major_resonance'] | |
| if next_event: | |
| print(f"• Next Major Resonance: {next_event['time_before_present']:.1f} years") | |
| print(f"• Expected Coherence: {next_event['coherence']:.3f}") | |
| print(f"\n💡 RECOMMENDATIONS:") | |
| for i, rec in enumerate(results['predictive_insights']['recommended_actions'], 1): | |
| print(f" {i}. {rec}") | |
| print(f"\n✅ DINGIR LATTICE ANALYSIS COMPLETE") | |
| print("The message is undeniable - cyclical cataclysm governed by quantum resonance") | |
| if __name__ == "__main__": | |
| demonstrate_dingir_system() |