Morphotensorial-Field-Theory

new_state_of_matter_test.py

# new_state_of_matter_test.py

import torch
import numpy as np
from complextensor import ComplexTensor
from quantum_em_mis_core import MISTransform, EnhancedQuantumWaveFunction
from datetime import datetime
import logging
from pathlib import Path

logging.basicConfig(
    filename=f'new_state_matter_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log',
    level=logging.INFO
)
logger = logging.getLogger(__name__)

# Suppress ALL loggers related to ComplexTensor
for logger_name in logging.Logger.manager.loggerDict.keys():
    if isinstance(logger_name, str) and 'complextensor' in logger_name.lower():
        logging.getLogger(logger_name).setLevel(logging.CRITICAL)

class MISStateAnalyzer:
    """Analyzer for the new MIS-Quantum-Classical Bridge State"""
    def __init__(self):
        self.output_dir = Path(f"mis_state_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
        self.output_dir.mkdir(exist_ok=True)
        
        # Golden ratio for quantum resonance
        self.phi = (1 + np.sqrt(5)) / 2
        # Fine structure constant inverse
        self.alpha_inverse = 137.035999084
    
    def analyze_mis_state(self):
        """Analyze the new state of matter."""
        logger.info("Initializing MIS State Analysis")
        
        # Setup quantum system with golden ratio parameters
        qwf = EnhancedQuantumWaveFunction(
            n_states=int(self.phi * 5),  # Fibonacci-quantum states
            n_dimensions=3,              # 3D space
            alpha=complex(self.phi),     # Golden ratio coupling
            beta=complex(2.0)            # Scale invariance parameter
        )
        
        # Create spacetime grid
        x = torch.linspace(-self.phi, self.phi, 50)
        X, Y, Z = torch.meshgrid(x, x, x, indexing='ij')
        X = X.unsqueeze(0)
        Y = Y.unsqueeze(0)
        Z = Z.unsqueeze(0)
        
        # Time evolution at quantum-classical resonance points
        times = np.array([0.0, 1/self.phi, 2/self.phi, 3/self.phi, 5/self.phi, 8/self.phi])
        
        quantum_classical_bridge = {
            'state_vector': [],
            'coherence': [],
            'energy_coupling': [],
            'phase_resonance': [],
            'fibonacci_amplitudes': []
        }
        
        # Analyze quantum-classical bridge formation
        for t in times:
            logger.info(f"Analyzing t={t:.4f}")
            
            # Evolve state
            psi = qwf.evolve_unnormalized([X, Y, Z], t)
            
            # Calculate quantum-classical bridge metrics
            coherence = psi.abs().mean().item() * self.alpha_inverse
            energy = (psi.abs()**2 * qwf.energies.view(-1, 1, 1, 1)).sum()
            phase_coupling = (psi.angle() * energy).mean().item()
            
            # Calculate Fibonacci amplitude resonances
            fib_resonances = []
            for n in range(5):
                fib_n = int(self.fibonacci(n))
                resonance = (psi.abs()[fib_n:fib_n+1] if fib_n < len(psi) else psi.abs()[:1]).mean()
                fib_resonances.append(resonance.item())
            
            # Store results
            quantum_classical_bridge['state_vector'].append(psi)
            quantum_classical_bridge['coherence'].append(coherence)
            quantum_classical_bridge['energy_coupling'].append(energy.item())
            quantum_classical_bridge['phase_resonance'].append(phase_coupling)
            quantum_classical_bridge['fibonacci_amplitudes'].append(fib_resonances)
            
            # Log the emergence of the new state
            logger.info(f"""
            Time: {t:.4f}
            Coherence: {coherence:.8e}
            Energy Coupling: {energy.item():.8e}
            Phase Resonance: {phase_coupling:.8e}
            Fibonacci Resonances: {fib_resonances}
            """)
            
            # Save state properties
            np.save(
                self.output_dir / f"state_t{t:.2f}.npy",
                {
                    'psi_real': psi.real.detach().numpy(),
                    'psi_imag': psi.imag.detach().numpy(),
                    'coherence': coherence,
                    'energy': energy.item(),
                    'phase': phase_coupling,
                    'fib_resonances': fib_resonances
                }
            )
    
    def fibonacci(self, n: int) -> int:
        """Calculate nth Fibonacci number for quantum resonances."""
        if n <= 0:
            return 0
        elif n == 1:
            return 1
        else:
            a, b = 0, 1
            for _ in range(2, n + 1):
                a, b = b, a + b
            return b
    
    def verify_quantum_classical_bridge(self):
        """Verify the emergence of the new state."""
        logger.info("Verifying quantum-classical bridge state")
        
        # Theoretical predictions
        predictions = {
            'coherence_baseline': 1.0 / self.alpha_inverse,
            'energy_quantum': self.phi ** 2,
            'phase_quantum': np.pi / self.phi
        }
        
        # Load and analyze all saved states
        state_files = sorted(self.output_dir.glob("state_t*.npy"))
        
        verification = {
            'coherence_stability': [],
            'energy_quantization': [],
            'phase_locking': []
        }
        
        for state_file in state_files:
            state_data = np.load(state_file, allow_pickle=True).item()
            
            # Verify coherence stability
            coherence_error = abs(state_data['coherence'] - predictions['coherence_baseline'])
            verification['coherence_stability'].append(coherence_error)
            
            # Verify energy quantization
            energy_quantum_error = abs(state_data['energy'] / predictions['energy_quantum'] - 1)
            verification['energy_quantization'].append(energy_quantum_error)
            
            # Verify phase locking
            phase_error = abs(state_data['phase'] % predictions['phase_quantum'])
            verification['phase_locking'].append(phase_error)
        
        # Log verification results
        logger.info(f"""
        Verification Results:
        Mean Coherence Error: {np.mean(verification['coherence_stability']):.8e}
        Mean Energy Quantization Error: {np.mean(verification['energy_quantization']):.8e}
        Mean Phase Locking Error: {np.mean(verification['phase_locking']):.8e}
        """)
        
        # Save verification data
        np.save(self.output_dir / "verification_results.npy", verification)

def run_state_analysis():
    """Run the new state of matter analysis."""
    analyzer = MISStateAnalyzer()
    
    try:
        logger.info("Starting new state of matter analysis")
        analyzer.analyze_mis_state()
        analyzer.verify_quantum_classical_bridge()
        logger.info("Analysis completed successfully")
    except Exception as e:
        logger.error(f"Error in state analysis: {str(e)}", exc_info=True)

if __name__ == "__main__":
    run_state_analysis()

quantum_em_mis_core.py

# quantum_em_mis_core.py

import torch
import torch.nn.functional as F
from complextensor import ComplexTensor
import numpy as np
from numpy.polynomial.hermite import Hermite
import logging
from typing import List, Tuple, Optional, Union
from datetime import datetime

# Configure logging
logging.basicConfig(
    filename=f'quantum_em_mis_core_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log',
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

class MISTransform:
    """Morphing Infinity Spiral Transform implementation."""
    def __init__(self, alpha: complex = 1.0, beta: complex = 2.0):
        self.alpha = alpha
        self.beta = beta
        logger.debug(f"Initialized MISTransform with alpha={alpha}, beta={beta}")
        
    def complex_power(self, z: ComplexTensor, exponent: float) -> ComplexTensor:
        """Compute z^exponent using exp(log) method."""
        if not isinstance(z, ComplexTensor):
            raise TypeError("Input must be a ComplexTensor")
            
        log_z = ComplexTensor(
            torch.log(z.abs() + 1e-10),
            z.angle()
        )
        exponent_tensor = ComplexTensor(
            torch.full_like(z.real, exponent),
            torch.zeros_like(z.imag)
        )
        return (log_z * exponent_tensor).exp()
    
    def __call__(self, z: ComplexTensor, t: float) -> ComplexTensor:
        """Apply MIS transformation."""
        try:
            power_term = self.complex_power(z, float(self.alpha.real))
            
            log_z = ComplexTensor(
                torch.log(z.abs() + 1e-10),
                z.angle()
            )
            log_z_beta = self.complex_power(log_z, float(self.beta.real))
            
            phase_tensor = ComplexTensor(
                torch.full_like(z.real, t),
                torch.zeros_like(z.imag)
            )
            rotation = (log_z_beta * phase_tensor).imag
            
            rotation_term = ComplexTensor(
                torch.cos(rotation),
                torch.sin(rotation)
            )
            
            return power_term * rotation_term
            
        except Exception as e:
            logger.error(f"Error in MIS transformation: {str(e)}")
            raise

class EnhancedQuantumWaveFunction:
    """Quantum wave function with MIS scale invariance."""
    def __init__(self, n_states: int, n_dimensions: int, alpha: complex = 1.0, beta: complex = 2.0):
        self.n_states = n_states
        self.n_dimensions = n_dimensions
        self.mis = MISTransform(alpha, beta)
        
        try:
            self.alphas = ComplexTensor(
                torch.randn(n_states, 1, 1) / np.sqrt(n_states),
                torch.randn(n_states, 1, 1) / np.sqrt(n_states)
            )
            self.energies = torch.linspace(0, 10, n_states).pow(2)
            
            self.basis = [
                lambda x, n=n: ComplexTensor(
                    torch.exp(-x**2/2) * torch.from_numpy(
                        Hermite.basis(n)(x.cpu().numpy())
                    ).to(x.device).to(torch.float32),
                    torch.zeros_like(x, dtype=torch.float32)
                ) for n in range(n_states)
            ]
            logger.info(f"Initialized QuantumWaveFunction with {n_states} states in {n_dimensions}D")
        except Exception as e:
            logger.error(f"Error initializing QuantumWaveFunction: {str(e)}")
            raise
    
    def evolve_unnormalized(self, coordinates: List[torch.Tensor], time: float, 
                           hbar: float = 1.0) -> ComplexTensor:
        """Evolve quantum state without normalization."""
        try:
            psi = ComplexTensor(
                torch.zeros_like(coordinates[0], dtype=torch.float32),
                torch.zeros_like(coordinates[0], dtype=torch.float32)
            )
            
            for i in range(self.n_states):
                phi = self.basis[i](coordinates[0])
                for d in range(1, self.n_dimensions):
                    phi = phi * self.basis[i](coordinates[d])
                
                phi_transformed = self.mis(phi, time)
                
                phase = -self.energies[i] * time / hbar
                time_evolution = ComplexTensor(
                    torch.cos(phase).view(1, 1).to(torch.float32),
                    -torch.sin(phase).view(1, 1).to(torch.float32)
                )
                
                psi += self.alphas[i:i+1] * phi_transformed * time_evolution
            
            return psi
            
        except Exception as e:
            logger.error(f"Error in quantum evolution: {str(e)}")
            raise
    
    def get_relative_phases(self, psi: ComplexTensor) -> ComplexTensor:
        """Track relative phases explicitly."""
        if not isinstance(psi, ComplexTensor):
            raise TypeError("Input must be a ComplexTensor")
            
        reference_phase = psi.angle()[0,0]
        return ComplexTensor(psi.abs(), psi.angle() - reference_phase)
    
    def measure(self, psi: ComplexTensor) -> torch.Tensor:
        """Lazy normalization only during measurement."""
        if not isinstance(psi, ComplexTensor):
            raise TypeError("Input must be a ComplexTensor")
            
        prob = psi.abs().pow(2)
        return prob / (prob.sum() + 1e-10)

class EnhancedEMField:
    """EM field with MIS scale invariance."""
    def __init__(self, grid_size: int, dx: float = 0.1, alpha: complex = 1.0, beta: complex = 2.0):
        if grid_size <= 0 or dx <= 0:
            raise ValueError("grid_size and dx must be positive")
            
        self.grid_size = grid_size
        self.dx = dx
        self.mis = MISTransform(alpha, beta)
        logger.info(f"Initialized EM Field with grid_size={grid_size}, dx={dx}")
    
    def gradient(self, field: ComplexTensor) -> List[ComplexTensor]:
        """Compute gradient with central differences."""
        if not isinstance(field, ComplexTensor):
            raise TypeError("Input must be a ComplexTensor")
            
        try:
            grad = []
            for axis in range(field.real.ndim):
                grad_real = torch.zeros_like(field.real)
                grad_imag = torch.zeros_like(field.imag)
                
                slices = [slice(None)] * field.real.ndim
                slices_fwd = [slice(None)] * field.real.ndim
                slices_bwd = [slice(None)] * field.real.ndim
                
                slices[axis] = slice(1, -1)
                slices_fwd[axis] = slice(2, None)
                slices_bwd[axis] = slice(0, -2)
                
                grad_real[tuple(slices)] = (
                    field.real[tuple(slices_fwd)] - field.real[tuple(slices_bwd)]
                ) / (2 * self.dx)
                grad_imag[tuple(slices)] = (
                    field.imag[tuple(slices_fwd)] - field.imag[tuple(slices_bwd)]
                ) / (2 * self.dx)
                
                grad.append(ComplexTensor(grad_real, grad_imag))
            
            return grad
            
        except Exception as e:
            logger.error(f"Error computing gradient: {str(e)}")
            raise
    
    def divergence(self, field_components: List[ComplexTensor]) -> ComplexTensor:
        """Compute divergence."""
        if not isinstance(field_components, list) or not all(isinstance(f, ComplexTensor) for f in field_components):
            raise TypeError("Input must be a list of ComplexTensors")
            
        try:
            div = ComplexTensor(
                torch.zeros_like(field_components[0].real),
                torch.zeros_like(field_components[0].imag)
            )
            
            for component, field in enumerate(field_components):
                transformed_field = self.mis(field, 0.0)
                grad = self.gradient(transformed_field)[component]
                div += grad
            
            return div
            
        except Exception as e:
            logger.error(f"Error computing divergence: {str(e)}")
            raise
    
    def curl(self, field_components: List[ComplexTensor]) -> List[ComplexTensor]:
        """Compute curl."""
        if len(field_components) != 3:
            raise ValueError("Curl requires exactly 3 field components")
            
        try:
            transformed_fields = [self.mis(f, 0.0) for f in field_components]
            gradients = [self.gradient(f) for f in transformed_fields]
            
            curl_x = gradients[2][1] - gradients[1][2]
            curl_y = gradients[0][2] - gradients[2][0]
            curl_z = gradients[1][0] - gradients[0][1]
            
            return [curl_x, curl_y, curl_z]
            
        except Exception as e:
            logger.error(f"Error computing curl: {str(e)}")
            raise

# Initialization check
if __name__ == "__main__":
    logger.info("quantum_em_mis_core initialized")

quantum_em_mis_tests.py

# quantum_em_mis_tests.py

import torch
import numpy as np
from datetime import datetime
import logging
import unittest
import time
import psutil
from pathlib import Path
import json
from typing import List, Tuple, Dict, Any
from complextensor import ComplexTensor
from quantum_em_mis_core import MISTransform, EnhancedQuantumWaveFunction, EnhancedEMField

# Configure logging
logging.basicConfig(
    filename=f'quantum_em_mis_tests_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log',
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

class DataCollector:
    """Collect and save test data."""
    def __init__(self):
        self.timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        self.data_dir = Path(f"test_data_{self.timestamp}")
        self.data_dir.mkdir(exist_ok=True)
        self.metadata = {
            "timestamp": self.timestamp,
            "data_files": []
        }
        
    def save_tensor_data(self, data: torch.Tensor, name: str, metadata: Dict = None):
        """Save tensor data with metadata."""
        file_path = self.data_dir / f"{name}.npy"
        np.save(file_path, data.detach().cpu().numpy())
        
        meta_info = {
            "filename": f"{name}.npy",
            "shape": list(data.shape),
            "dtype": str(data.dtype),
            "metadata": metadata or {}
        }
        self.metadata["data_files"].append(meta_info)
        
    def save_complex_tensor_data(self, data: ComplexTensor, name: str, metadata: Dict = None):
        """Save ComplexTensor data."""
        self.save_tensor_data(data.real, f"{name}_real", metadata)
        self.save_tensor_data(data.imag, f"{name}_imag", metadata)
    
    def log_performance(self, name: str, execution_time: float, memory_used: float):
        """Log performance metrics."""
        with open(self.data_dir / "performance.json", "a") as f:
            json.dump({
                "name": name,
                "time": execution_time,
                "memory_mb": memory_used,
                "timestamp": datetime.now().isoformat()
            }, f)
            f.write('\n')
    
    def save_metadata(self):
        """Save collected metadata."""
        with open(self.data_dir / "metadata.json", "w") as f:
            json.dump(self.metadata, f, indent=2)

class QuantumEMMISTests(unittest.TestCase):
    def setUp(self):
        self.data_collector = DataCollector()
        
    def tearDown(self):
        self.data_collector.save_metadata()
    
    def measure_performance(self, name: str, callable_obj, *args, **kwargs):
        """Measure performance and collect data."""
        process = psutil.Process()
        start_mem = process.memory_info().rss / 1024 / 1024
        start_time = time.time()
        
        result = callable_obj(*args, **kwargs)
        
        end_time = time.time()
        end_mem = process.memory_info().rss / 1024 / 1024
        
        execution_time = end_time - start_time
        memory_used = end_mem - start_mem
        
        self.data_collector.log_performance(name, execution_time, memory_used)
        return result
    
    def test_mis_scale_invariance(self):
        """Test MIS transformation scale invariance."""
        logger.info("Testing MIS scale invariance")
        mis = MISTransform()
        scales = [0.1, 1.0, 10.0]
        
        x = torch.linspace(-5, 5, 50)
        X, Y = torch.meshgrid(x, x, indexing='ij')
        Z = ComplexTensor(X.unsqueeze(0), Y.unsqueeze(0))
        
        for scale in scales:
            scaled_Z = ComplexTensor(Z.real * scale, Z.imag * scale)
            transformed = self.measure_performance(
                f"mis_transform_scale_{scale}",
                mis.__call__,
                scaled_Z, t=1.0
            )
            self.data_collector.save_complex_tensor_data(
                transformed,
                f"mis_scale_{scale}",
                {"scale": scale}
            )
    
    def test_quantum_evolution(self):
        """Test quantum wave function evolution."""
        logger.info("Testing quantum evolution")
        qwf = EnhancedQuantumWaveFunction(n_states=3, n_dimensions=2)
        
        x = torch.linspace(-5, 5, 50)
        X, Y = torch.meshgrid(x, x, indexing='ij')
        X = X.unsqueeze(0)
        Y = Y.unsqueeze(0)
        
        times = [0.0, 0.5, 1.0, 2.0]
        
        for t in times:
            psi = self.measure_performance(
                f"quantum_evolution_t_{t}",
                qwf.evolve_unnormalized,
                [X, Y], t
            )
            self.data_collector.save_complex_tensor_data(
                psi,
                f"psi_t_{t}",
                {"time": t}
            )
            
            prob = qwf.measure(psi)
            self.data_collector.save_tensor_data(
                prob,
                f"probability_t_{t}",
                {"time": t}
            )
            
            total_prob = prob.sum().item()
            logger.info(f"Total probability at t={t}: {total_prob}")
            self.assertAlmostEqual(total_prob, 1.0, places=5)
    
    def test_em_field_operations(self):
        """Test electromagnetic field operations."""
        logger.info("Testing EM field operations")
        em = EnhancedEMField(grid_size=50)
        
        # Create test field
        k = 2 * np.pi / 10
        omega = 2 * np.pi
        x = torch.linspace(-5, 5, 50)
        X, Y, Z = torch.meshgrid(x, x, x, indexing='ij')
        X = X.unsqueeze(0)
        Y = Y.unsqueeze(0)
        Z = Z.unsqueeze(0)
        
        # Vector potential
        A_amp = 1.0
        A_z = A_amp * torch.cos(k * X)
        
        # Magnetic field
        B_x = -k * A_amp * torch.sin(k * X)
        B_y = torch.zeros_like(X)
        B_z = torch.zeros_like(X)
        
        # Electric field
        Ex = ComplexTensor(torch.zeros_like(X), torch.zeros_like(X))
        Ey = ComplexTensor(torch.zeros_like(X), torch.zeros_like(X))
        Ez = ComplexTensor(
            omega * A_amp * torch.sin(k * X),
            torch.zeros_like(X)
        )
        
        # Save field configurations
        self.data_collector.save_complex_tensor_data(
            Ex, "E_x", {"field": "electric", "component": "x"}
        )
        self.data_collector.save_complex_tensor_data(
            Ey, "E_y", {"field": "electric", "component": "y"}
        )
        self.data_collector.save_complex_tensor_data(
            Ez, "E_z", {"field": "electric", "component": "z"}
        )
        
        # Test operations
        curl = self.measure_performance(
            "em_curl",
            em.curl,
            [Ex, Ey, Ez]
        )
        for i, component in enumerate(['x', 'y', 'z']):
            self.data_collector.save_complex_tensor_data(
                curl[i],
                f"curl_{component}",
                {"operation": "curl", "component": component}
            )
        
        div = self.measure_performance(
            "em_divergence",
            em.divergence,
            [Ex, Ey, Ez]
        )
        self.data_collector.save_complex_tensor_data(
            div,
            "divergence",
            {"operation": "divergence"}
        )
        
        # Verify Maxwell's equations
        div_mean = div.abs().mean().item()
        logger.info(f"Mean divergence: {div_mean}")
        self.assertLess(div_mean, 1e-5)
        
        # B-field divergence
        B_fields = [
            ComplexTensor(B_x, torch.zeros_like(B_x)),
            ComplexTensor(B_y, torch.zeros_like(B_y)),
            ComplexTensor(B_z, torch.zeros_like(B_z))
        ]
        B_div = self.measure_performance(
            "b_field_divergence",
            em.divergence,
            B_fields
        )
        self.data_collector.save_complex_tensor_data(
            B_div,
            "B_divergence",
            {"operation": "B-field divergence"}
        )
    
    def test_edge_cases(self):
        """Test behavior at edge cases."""
        logger.info("Testing edge cases")
        mis = MISTransform()
        
        # Near zero
        x = torch.linspace(-1e-10, 1e-10, 50)
        X, Y = torch.meshgrid(x, x, indexing='ij')
        Z_near_zero = ComplexTensor(X.unsqueeze(0), Y.unsqueeze(0))
        
        result = self.measure_performance(
            "mis_near_zero",
            mis.__call__,
            Z_near_zero, t=1.0
        )
        self.data_collector.save_complex_tensor_data(
            result,
            "edge_case_near_zero",
            {"case": "near_zero"}
        )
        
        # Large magnitude
        x = torch.linspace(-1e10, 1e10, 50)
        X, Y = torch.meshgrid(x, x, indexing='ij')
        Z_large = ComplexTensor(X.unsqueeze(0), Y.unsqueeze(0))
        
        result = self.measure_performance(
            "mis_large_magnitude",
            mis.__call__,
            Z_large, t=1.0
        )
        self.data_collector.save_complex_tensor_data(
            result,
            "edge_case_large_magnitude",
            {"case": "large_magnitude"}
        )

def run_tests():
    """Run all tests and collect data."""
    logger.info("Starting comprehensive test suite")
    suite = unittest.TestLoader().loadTestsFromTestCase(QuantumEMMISTests)
    runner = unittest.TextTestRunner(verbosity=2)
    result = runner.run(suite)
    logger.info("Test suite completed")
    return result

if __name__ == "__main__":
    run_tests()

readthis.md

run the quantum_em_mis_tests.py, make sure you install complextensor with pip install complextensor make sure you have both files in the same folder, this will give you a massive dump of data.

there are many applications we can do with these ideas, for now we have to focus on testing, and testing and testing...

eventually we will have something to work with. these are complex systems the applications are many, from qt computing, to finance to medicine....

also pls be patient..

here is the link for the raw .npy data i got if you wanna take a look, all this code is experimental and I will make it better in the repo soon

https://github.com/NeoVertex1/Morphotensorial-Field-Theory/tree/main/data_dump