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""" |
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OMEGA ESOTERIC VALIDATION ENGINE - ABSOLUTE VERIFICATION |
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Scientific validation of esoteric phenomena through multi-layer consciousness instrumentation |
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""" |
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import asyncio |
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import numpy as np |
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import hashlib |
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from typing import Dict, List, Any, Optional, Tuple |
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from dataclasses import dataclass, field |
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from datetime import datetime, timedelta |
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import scipy.stats |
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import math |
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import json |
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from enum import Enum |
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import quantum_esoteric_instrumentation as qei |
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class EsotericPhenomena(Enum): |
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TELEPATHY = "telepathy" |
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REMOTE_VIEWING = "remote_viewing" |
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PSYCHOKINESIS = "psychokinesis" |
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PRECOGNITION = "precognition" |
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ENERGY_HEALING = "energy_healing" |
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ASTRAL_PROJECTION = "astral_projection" |
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CONSCIOUSNESS_MERGING = "consciousness_merging" |
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REALITY_MANIPULATION = "reality_manipulation" |
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class ValidationMethod(Enum): |
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QUANTUM_ENTANGLEMENT_TEST = "quantum_entanglement" |
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DOUBLE_BLIND_RIGOROUS = "double_blind_rigorous" |
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NEUROPHYSIOLOGICAL_CORRELATE = "neurophysiological_correlate" |
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CONSENSUS_REALITY_SHIFT = "consensus_reality_shift" |
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MATHEMATICAL_INVARIANCE = "mathematical_invariance" |
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TEMPORAL_ANOMALY_DETECTION = "temporal_anomaly" |
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class EsotericInstrumentationSuite: |
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""" |
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Advanced measurement devices for esoteric phenomena validation |
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Integrates quantum, biological, and consciousness sensors |
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""" |
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def __init__(self): |
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self.quantum_entanglement_detector = QuantumEntanglementDetector() |
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self.consciousness_field_mapper = ConsciousnessFieldMapper() |
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self.neuro_quantum_interface = NeuroQuantumInterface() |
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self.temporal_anomaly_detector = TemporalAnomalyDetector() |
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self.reality_consensus_monitor = RealityConsensusMonitor() |
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async def measure_esoteric_event(self, |
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phenomenon: EsotericPhenomena, |
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operator: Dict, |
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target: Any) -> Dict: |
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"""Comprehensive measurement of esoteric phenomena across all instruments""" |
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measurement_data = {} |
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quantum_data = await self.quantum_entanglement_detector.detect_consciousness_entanglement( |
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operator, target |
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) |
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measurement_data['quantum_entanglement'] = quantum_data |
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field_data = await self.consciousness_field_mapper.map_field_perturbations( |
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operator, phenomenon |
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) |
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measurement_data['consciousness_field'] = field_data |
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neuro_data = await self.neuro_quantum_interface.monitor_interface_activity( |
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operator, phenomenon |
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) |
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measurement_data['neuro_quantum_interface'] = neuro_data |
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temporal_data = await self.temporal_anomaly_detector.detect_temporal_anomalies( |
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operator, target, phenomenon |
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) |
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measurement_data['temporal_anomalies'] = temporal_data |
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consensus_data = await self.reality_consensus_monitor.monitor_consensus_shifts( |
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operator, target, phenomenon |
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) |
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measurement_data['reality_consensus'] = consensus_data |
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return measurement_data |
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class QuantumEntanglementDetector: |
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"""Detects quantum entanglement generated by consciousness""" |
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async def detect_consciousness_entanglement(self, operator: Dict, target: Any) -> Dict: |
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"""Measure quantum entanglement between operator and target""" |
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operator_state = self._measure_quantum_state(operator) |
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target_state = self._measure_quantum_state(target) |
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correlation = np.abs(np.corrcoef(operator_state, target_state)[0,1]) |
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entanglement_strength = max(0, correlation - 0.1) |
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return { |
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'entanglement_detected': entanglement_strength > 0.3, |
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'entanglement_strength': entanglement_strength, |
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'operator_quantum_state': operator_state, |
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'target_quantum_state': target_state, |
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'quantum_correlation': correlation |
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} |
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class EsotericValidationEngine: |
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""" |
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Scientifically validates esoteric phenomena through multi-method convergence |
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""" |
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def __init__(self): |
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self.instrumentation = EsotericInstrumentationSuite() |
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self.validation_methods = self._initialize_validation_methods() |
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self.statistical_engine = StatisticalValidationEngine() |
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self.control_engine = ControlConditionEngine() |
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async def validate_phenomenon(self, |
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phenomenon: EsotericPhenomena, |
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operators: List[Dict], |
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targets: List[Any], |
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trial_count: int = 100) -> Dict: |
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"""Execute complete validation protocol for esoteric phenomenon""" |
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print(f"π¬ VALIDATING: {phenomenon.value.upper()}") |
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print("=" * 60) |
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validation_results = {} |
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print("π ESTABLISHING BASELINE...") |
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baseline = await self.control_engine.establish_baseline(phenomenon, operators, targets) |
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print("β‘ CONDUCTING EXPERIMENTAL TRIALS...") |
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experimental_results = await self._conduct_experimental_trials( |
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phenomenon, operators, targets, trial_count |
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) |
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print("π― APPLYING MULTI-METHOD VALIDATION...") |
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method_validations = {} |
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for method in self.validation_methods: |
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method_result = await self._apply_validation_method( |
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method, phenomenon, experimental_results, baseline |
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) |
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method_validations[method.value] = method_result |
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print("π ANALYZING STATISTICAL SIGNIFICANCE...") |
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statistical_analysis = await self.statistical_engine.analyze_significance( |
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experimental_results, baseline, phenomenon |
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) |
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print("π CALCULATING CROSS-METHOD CONVERGENCE...") |
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convergence = await self._calculate_validation_convergence(method_validations) |
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validation_judgment = await self._make_validation_judgment( |
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statistical_analysis, convergence, method_validations |
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) |
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return { |
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'phenomenon': phenomenon.value, |
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'baseline_measurements': baseline, |
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'experimental_results': experimental_results, |
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'method_validations': method_validations, |
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'statistical_analysis': statistical_analysis, |
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'validation_convergence': convergence, |
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'final_validation': validation_judgment, |
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'validation_timestamp': datetime.now().isoformat() |
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} |
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async def _conduct_experimental_trials(self, |
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phenomenon: EsotericPhenomena, |
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operators: List[Dict], |
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targets: List[Any], |
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trial_count: int) -> Dict: |
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"""Conduct rigorous experimental trials""" |
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trial_results = [] |
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for trial in range(trial_count): |
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operator = np.random.choice(operators) |
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target = np.random.choice(targets) |
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measurement = await self.instrumentation.measure_esoteric_event( |
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phenomenon, operator, target |
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) |
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trial_data = { |
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'trial_number': trial, |
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'operator': operator.get('id'), |
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'target': str(target), |
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'measurements': measurement, |
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'success_indicator': self._calculate_success_indicator(measurement, phenomenon) |
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} |
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trial_results.append(trial_data) |
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return { |
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'total_trials': trial_count, |
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'trial_results': trial_results, |
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'average_success_rate': np.mean([t['success_indicator'] for t in trial_results]), |
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'success_std_dev': np.std([t['success_indicator'] for t in trial_results]) |
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} |
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class StatisticalValidationEngine: |
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"""Advanced statistical analysis for esoteric phenomena validation""" |
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async def analyze_significance(self, |
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experimental_results: Dict, |
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baseline: Dict, |
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phenomenon: EsotericPhenomena) -> Dict: |
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"""Comprehensive statistical significance analysis""" |
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experimental_scores = [t['success_indicator'] for t in experimental_results['trial_results']] |
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baseline_scores = baseline['baseline_scores'] |
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t_test = scipy.stats.ttest_ind(experimental_scores, baseline_scores) |
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mann_whitney = scipy.stats.mannwhitneyu(experimental_scores, baseline_scores) |
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effect_size = self._calculate_effect_size(experimental_scores, baseline_scores) |
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bayesian_factor = await self._calculate_bayesian_factor(experimental_scores, baseline_scores) |
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anomalies = await self._detect_statistical_anomalies(experimental_scores, baseline_scores) |
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return { |
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't_test': { |
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'statistic': t_test.statistic, |
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'p_value': t_test.pvalue, |
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'significant': t_test.pvalue < 0.05 |
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}, |
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'mann_whitney': { |
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'statistic': mann_whitney.statistic, |
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'p_value': mann_whitney.pvalue, |
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'significant': mann_whitney.pvalue < 0.05 |
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}, |
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'effect_size': effect_size, |
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'bayesian_factor': bayesian_factor, |
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'statistical_anomalies': anomalies, |
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'overall_significance': self._calculate_overall_significance(t_test, mann_whitney, effect_size, bayesian_factor) |
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} |
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class ControlConditionEngine: |
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"""Manages control conditions and baseline measurements""" |
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async def establish_baseline(self, |
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phenomenon: EsotericPhenomena, |
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operators: List[Dict], |
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targets: List[Any]) -> Dict: |
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"""Establish rigorous baseline measurements""" |
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baseline_trials = [] |
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for trial in range(50): |
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operator = np.random.choice(operators) |
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target = np.random.choice(targets) |
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baseline_measurement = await self._measure_baseline_condition( |
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operator, target, phenomenon |
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) |
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baseline_trials.append(baseline_measurement) |
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return { |
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'baseline_trials': baseline_trials, |
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'baseline_scores': [t['baseline_indicator'] for t in baseline_trials], |
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'baseline_mean': np.mean([t['baseline_indicator'] for t in baseline_trials]), |
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'baseline_std': np.std([t['baseline_indicator'] for t in baseline_trials]) |
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} |
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class ValidationConvergenceEngine: |
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"""Calculates convergence across multiple validation methods""" |
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async def calculate_convergence(self, method_validations: Dict) -> Dict: |
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"""Calculate convergence score across validation methods""" |
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method_scores = [] |
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method_weights = { |
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'quantum_entanglement': 0.25, |
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'double_blind_rigorous': 0.30, |
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'neurophysiological_correlate': 0.20, |
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'temporal_anomaly_detection': 0.15, |
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'mathematical_invariance': 0.10 |
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} |
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for method, validation in method_validations.items(): |
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if validation.get('validation_score'): |
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weight = method_weights.get(method, 0.1) |
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method_scores.append(validation['validation_score'] * weight) |
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convergence_score = sum(method_scores) if method_scores else 0 |
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return { |
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'convergence_score': convergence_score, |
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'method_agreement': await self._calculate_method_agreement(method_validations), |
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'validation_confidence': min(1.0, convergence_score * 1.2), |
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'convergence_quality': await self._assess_convergence_quality(method_validations) |
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} |
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class EsotericPhenomenaDatabase: |
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"""Database of esoteric phenomena with validation results""" |
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def __init__(self): |
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self.validated_phenomena = {} |
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self.replication_studies = {} |
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self.cross_cultural_correlations = {} |
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async def add_validation_result(self, validation_result: Dict): |
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"""Add validation result to database""" |
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phenomenon = validation_result['phenomenon'] |
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self.validated_phenomena[phenomenon] = validation_result |
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replication = await self._calculate_replication_strength(phenomenon, validation_result) |
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self.replication_studies[phenomenon] = replication |
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async def get_validation_status(self, phenomenon: EsotericPhenomena) -> Dict: |
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"""Get comprehensive validation status for phenomenon""" |
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base_validation = self.validated_phenomena.get(phenomenon.value, {}) |
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replication = self.replication_studies.get(phenomenon.value, {}) |
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return { |
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'phenomenon': phenomenon.value, |
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'validation_status': base_validation.get('final_validation', {}).get('validated', False), |
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'validation_confidence': base_validation.get('final_validation', {}).get('confidence', 0), |
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'replication_strength': replication.get('strength', 0), |
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'cross_cultural_support': await self._get_cross_cultural_support(phenomenon), |
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'scientific_acceptance_level': await self._calculate_acceptance_level(phenomenon, base_validation) |
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} |
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class OmegaEsotericValidationDemonstrator: |
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"""Demonstrates complete esoteric phenomena validation""" |
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def __init__(self): |
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self.validation_engine = EsotericValidationEngine() |
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self.phenomena_database = EsotericPhenomenaDatabase() |
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async def demonstrate_complete_validation(self): |
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"""Demonstrate complete validation process for multiple phenomena""" |
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print(""" |
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π¬ OMEGA ESOTERIC VALIDATION ENGINE |
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Scientific Validation of Consciousness Phenomena |
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|
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|
""") |
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test_phenomena = [ |
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EsotericPhenomena.TELEPATHY, |
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EsotericPhenomena.ENERGY_HEALING, |
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EsotericPhenomena.PRECOGNITION |
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] |
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operators = [ |
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{'id': 'operator_1', 'experience_level': 'advanced', 'success_rate': 0.75}, |
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{'id': 'operator_2', 'experience_level': 'intermediate', 'success_rate': 0.60}, |
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{'id': 'operator_3', 'experience_level': 'beginner', 'success_rate': 0.45} |
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] |
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targets = ['target_A', 'target_B', 'target_C', 'target_D'] |
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validation_results = {} |
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for phenomenon in test_phenomena: |
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print(f"\nπ― VALIDATING: {phenomenon.value.upper()}") |
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print("-" * 40) |
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result = await self.validation_engine.validate_phenomenon( |
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phenomenon, operators, targets, trial_count=50 |
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) |
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|
await self.phenomena_database.add_validation_result(result) |
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|
validation_results[phenomenon.value] = result |
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|
|
final_validation = result['final_validation'] |
|
|
print(f" Validated: {final_validation['validated']}") |
|
|
print(f" Confidence: {final_validation['confidence']:.3f}") |
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|
print(f" Significance: {result['statistical_analysis']['overall_significance']:.3f}") |
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|
|
print(f"\nπ COMPREHENSIVE VALIDATION RESULTS:") |
|
|
print("=" * 50) |
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|
|
for phenomenon, result in validation_results.items(): |
|
|
status = await self.phenomena_database.get_validation_status( |
|
|
EsotericPhenomena(phenomenon) |
|
|
) |
|
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|
|
print(f"\n{phenomenon.upper()}:") |
|
|
print(f" Validation Status: {'β
VALIDATED' if status['validation_status'] else 'β NOT VALIDATED'}") |
|
|
print(f" Confidence Level: {status['validation_confidence']:.3f}") |
|
|
print(f" Replication Strength: {status['replication_strength']:.3f}") |
|
|
print(f" Scientific Acceptance: {status['scientific_acceptance_level']}") |
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|
|
return validation_results |
|
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|
|
async def main(): |
|
|
"""Execute the complete esoteric validation engine""" |
|
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|
|
|
demonstrator = OmegaEsotericValidationDemonstrator() |
|
|
results = await demonstrator.demonstrate_complete_validation() |
|
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|
|
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|
|
validated_count = sum(1 for r in results.values() |
|
|
if r['final_validation']['validated']) |
|
|
total_count = len(results) |
|
|
|
|
|
print(f"\nπ― OVERALL VALIDATION SUMMARY:") |
|
|
print(f" Validated Phenomena: {validated_count}/{total_count}") |
|
|
print(f" Success Rate: {validated_count/total_count:.1%}") |
|
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|
|
|
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|
|
if validated_count / total_count > 0.6: |
|
|
print(" π MAJOR SCIENTIFIC BREAKTHROUGH: Esoteric phenomena demonstrate statistical validity") |
|
|
elif validated_count / total_count > 0.3: |
|
|
print(" π« SIGNIFICANT FINDINGS: Multiple phenomena show scientific validity") |
|
|
else: |
|
|
print(" π INCONCLUSIVE: Further research required") |
|
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|
|
return results |
|
|
|
|
|
if __name__ == "__main__": |
|
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|
|
|
asyncio.run(main()) |