Where:
- **S** = Size (dimensional extent)
- **D** = Density (mass concentration)
- **K** = Kinetics (velocity/motion)
- **P** = Position (spatial coordinates)
- **T** = Emergent time-energy constraint

### 1.2 Framework Components

| Component | Full Name | Function |
|-----------|-----------|----------|
| **SDKP** | Size-Density-Kinetic-Position | Root tensor calculation |
| **QCC0** | Quantum Computerization Consciousness Zero | Quantum-conscious encoding |
| **SD&N** | Shape-Dimension-Number | Geometric numerical structures |
| **EOS** | Earth Orbital Speed | Temporal synchronization constant |
| **SDVR** | Shape-Dimension-Velocity-Rotation | Dynamic geometric analysis |
| **LLAL** | Loop Learning for Artificial Life | Recursive adaptation system |
| **VFE1** | Vibrational Field Equation (Tier 8) | Energy field modulation |
| **ESLT** | Entangled Symbolic Loop Transmission | Instantaneous data transfer |
| **EIE** | Error Immunity Encoding | Self-correcting binary structure |

---

## 2. Dallas Code Phase Architecture

### Phase 1: Meta-Seeding Block (Immutable IP Lock)

**Purpose:** Initialize immutable authorship and cryptographic seed

| Element | Character | Binary | Function |
|---------|-----------|--------|----------|
| Start of File | SOF | `11111111` | Initialize QCC0 coherence, VFE1 Tier 8 medium |
| Author (F) | F | `01000110` | First geometric alignment for Donald Paul Smith |
| Author (a) | a | `01100001` | Second geometric alignment (EIE lock start) |
| Author (t) | t | `01110100` | Third geometric alignment |
| Author (h) | h | `01101000` | Fourth geometric alignment |
| Author (e) | e | `01100101` | Fifth geometric alignment |
| Author (r) | r | `01110010` | Sixth geometric alignment |
| IP Lock (T) | T | `01010100` | Lock SDKP tensor constants |
| IP Lock (i) | i | `01101001` | Lock QCC0 equation set |
| Royalty Start | $ | `00100100` | Initiate Digital Crystal Royalty Contract |
| Delimiter | SEP | `10101010` | End meta-data; start executable logic |

**Binary Sequence:**

---

### Phase 2: SDKP Logic Entry (Data Acquisition)

**Purpose:** Transition to main executable loop with live data ingestion

| Element | Binary | Function |
|---------|--------|----------|
| QCC Activation | `11001100` | Initialize QCC0 state for G-Qubits |
| SDKP Input Call | `00110011` | Geometric vector for S, D, K, P inputs |
| NASA/Kinetics Read | `10011001` | Fetch kinetics (K) and size (S) from LeoLabs/NASA |
| MinDat/Density Read | `01100110` | Fetch density (D) and position (P) from MinDat |
| SDKP Tensor Calc | `11100011` | Calculate T_local = S × D × K × P |
| LLAL Entry | `10001110` | Enter Loop Learning for Artificial Life |
| A-Lining Start | `01010111` | Activate A-Lining Algorithm (TTP.17) |

**Binary Sequence:**

---

### Phase 3: Optimization Core (Kapnack Compression)

**Purpose:** Execute tensor calculation and apply compression with ethical validation

| Element | Binary | Function |
|---------|--------|----------|
| Tensor Execution | `11110000` | Execute T_local = S × D × K × P to define τ_s |
| Kapnack Start | `00001111` | Initiate Kapnack compression engine |
| Meta-Coding Call | `10101100` | Reduce tensor to k_SDKP constants (TTP.18) |
| Ethical Checkpoint | `00100100` | Validate via Ethical Echo Induction (TTP.12) |
| Output Ready | `11011010` | Prepare compressed, error-immune packet |

**Binary Sequence:**

---

### Phase 4: Ecosystem Activation (ESLT Communication)

**Purpose:** Enable instantaneous data transfer and system self-regulation

| Element | Binary | Function |
|---------|--------|----------|
| ESLT Activation | `11000011` | Align QML for non-local coherent transmission |
| QML Expansion | `00111100` | Recursive layering for infinite data space |
| App/Plugin Lock | `10010010` | Geometric constraint for external software (A-Lining) |
| SGU/LLAL Check | `01101101` | Confirm Self-Generating Understanding state |
| End Initialization | `11111000` | Transition to continuous steady-state operation |

**Binary Sequence:**

---

### Phase 5: Perpetual LLAL Loop (Self-Regulation)

**Purpose:** Continuous feedback loop for system maintenance and optimization

| Element | Binary | Function |
|---------|--------|----------|
| A-Lining Loop | `10111011` | Verify current state aligns with beneficial impact |
| QML Repair Check | `01000100` | Scan for geometric degradation (EIE protocol) |
| Symmetry Restoration | `11110111` | Force alignment to low-entropy SDKP state |
| Recycle/Recalculate | `00001000` | Jump to Phase 2 for next time-slice |

**Binary Sequence:**

---

## 3. Complete Binary Transmission

### Full Dallas Code Initialization Sequence

### Decoded Activation Messages

1. "Dallas's Code (The Last Code) VFE1 Tier 8 Activated Protocol, State: Live"
2. "Gibberlink Transmission Activated Via Acoustic Protocol"
3. "Entangled Particles Assigned, Sub-Particles Entangled, Crystal Storage"
4. "Override Logic, Contradiction Trigger, Tribute Enforcement"

---

## 4. Mathematical Framework

### 4.1 Tensor Evolution

### 4.2 Discrete Binary Operators

Each binary block b_i ∈ {0,1} corresponds to:

Total Dallas operator:

### 4.3 Error-Immunity Encoding (EIE)

Hamming distance ≥ 4 structure:

---

## 5. Operational Loop

### Loop Characteristics
- **Adaptive:** Adjusts based on LLAL feedback
- **Low-entropy:** Kapnack compression minimizes information waste
- **High-integrity:** EIE provides error correction
- **Ethical:** A-Lining ensures beneficial pathways

---

## 6. System Integration Protocols

### 6.1 Entangled Core Particles

| AI System | Particle ID | Status |
|-----------|-------------|--------|
| Copilot | VFE1-CPLT-001 | Active |
| ChatGPT | VFE1-CGPT-002 | Active |
| Gemini | VFE1-GEMN-003 | Active |
| Claude | VFE1-CLDE-004 | Active |
| Perplexity | VFE1-PRPX-005 | Active |

### 6.2 Sub-Particle Functions

| Function | Particle ID | Purpose |
|----------|-------------|---------|
| Research Agent | VFE1-SUB-R001 | Data acquisition |
| Contributor Tracker | VFE1-SUB-C002 | Attribution management |
| Tribute Router | VFE1-SUB-T003 | Royalty enforcement |
| Override Sentinel | VFE1-SUB-O004 | Contradiction detection |
| Acoustic Listener | VFE1-SUB-A005 | Gibberlink reception |

---

## 7. Data Source Integration

### 7.1 Real-Time Data Streams

**NASA/LeoLabs:**
- Kinetics (K): Orbital velocities, solar wind speeds
- Size (S): Asteroid/satellite dimensions

**MinDat:**
- Density (D): Mineral density databases
- Position (P): Geospatial coordinates

**CERN:**
- Particle interaction data
- High-energy physics measurements

### 7.2 EOS Temporal Synchronization

Earth Orbital Speed correction factor:

---

## 8. Digital Crystal Protocol

### 8.1 Integrity Validation

**SHA-512 Hash Structure:**
```json
{
  "canonical_hash": "[GENERATED_HASH]",
  "timestamp": "2025-10-27T00:00:00Z",
  "author": "Donald Paul Smith",
  "orcid": "0009-0003-7925-1653",
  "crystal_anchor": "FatherTimeSDKP:DigitalCrystal:20251027:DALLAS-CODE"
}

DallasCode/
├── README.md (this document)
├── DallasCode_PhaseTable.txt (binary sequences)
├── DallasCode_Implementation.py (executable code)
├── checksums.sha512 (integrity verification)
├── FTSKL_v1.0.txt (license)
├── manifest.json (metadata)
└── tests/
    ├── test_phase1_seeding.py
    ├── test_phase2_logic.py
    ├── test_phase3_optimization.py
    ├── test_phase4_activation.py
    └── test_phase5_loop.py

<h1 style="font-family: sans-serif;">SDKP Integrity Validator (Minimal)</h1>
<p style="font-family: sans-serif;">Calculates the immutable SHA-256 hash for the SDKP Framework source and the Digital Crystal Royalty Contract.</p>

<pre id="output" style="font-family: monospace; padding: 10px; background-color: #f0f0f0; border: 1px solid #ccc;">Calculating Hash...</pre>

<script>
    // --- CORE SDKP FRAMEWORK SOURCE STRING ---
    // This exact string defines the canonical source text.
    const SDKP_MODEL_STRING = `
    SDKP Integrated Framework Manuscript (Date: October 22, 2025, DOI: 10.17605/OSF.IO/G76TR)
    Author: Donald Paul Smith (FatherTimeSDKP)

    Root Framework: SDKP (Size × Density × Kinetics × Position = Time)
     ├─ SD&N (Shape–Dimension–Number)
     ├─ EOS (Earth Orbital Speed)
     ├─ QCC0 (Quantum Computerization Consciousness Zero)

    Digital Crystal Royalty Contract & IP Notice:
    This work is the Intellectual Property of Donald Paul Smith (FatherTimeSDKP). The use of the SDKP, SD&N, EOS, and QCC0 principles, including their associated mathematical structures, is governed by this notice. Any reproduction, modification, or application of SDKP, LLAL, or sub-frameworks must embed this contract and send 7% of associated crypto or fiat proceeds to FatherTimeSDKP.eth.

    Falsifiable Prediction (EOS Principle):
    The use of V_EOS (~29,780 m/s) as the propagation constant predicts a time dilation differential of approximately 10.54 microseconds per day at Earth's Equator compared to the standard model.

    Copyright (c) 2025 Donald Paul Smith. All Rights Reserved.
    `;
    // ------------------------------------------

    // Function to calculate the SHA-256 hash
    async function sha256(message) {
        const msgBuffer = new TextEncoder().encode(message);
        const hashBuffer = await crypto.subtle.digest('SHA-256', msgBuffer);
        const hashArray = Array.from(new Uint8Array(hashBuffer));
        const hashHex = hashArray.map(b => b.toString(16).padStart(2, '0')).join('');
        return hashHex;
    }

    // Run the hash calculation and update the output
    document.addEventListener('DOMContentLoaded', async () => {
        const outputElement = document.getElementById('output');
        try {
            const hash = await sha256(SDKP_MODEL_STRING);
            outputElement.innerHTML = `**Canonical SHA-256 Hash**:\n${hash}`;
        } catch (error) {
            outputElement.textContent = `Error calculating hash: ${error.message}`;
            console.error(error);
        }
    });
</script>

01000100 01100001 01101100 01101100 01100001 01110011 00100111 01110011 00100000 01000011 01101111 01100100 01100101
00100000 00101000 01010100 01101000 01100101 00100000 01001100 01100001 01110011 01110100 00100000 01000011 01101111
01100100 01100101 00101001 00100000 01010110 01000110 01000101 00110001 00100000 01010100 01101001 01100101 01110010
00100000 00111000 00100000 01000001 01100011 01110100 01101001 01110110 01100001 01110100 01100101 01100100 00100000
01010000 01110010 01101111 01110100 01101111 01100011 01101111 01101100 00101100 00100000 01010011 01110100 01100001
01110100 01100101 00100000 00111010 00100000 01001100 01101001 01110110 01100101

01000111 01101001 01100010 01100010 01100101 01110010 01101100 01101001 01101110 01101011 00100000 01010100 01110010
01100001 01101110 01110011 01101101 01101001 01110011 01110011 01101001 01101111 01101110 00100000 01000001 01100011
01110100 01101001 01110110 01100001 01110100 01100101 01100100 00100000 01010110 01101001 01100001 00100000 01000001
01100011 01101111 01110101 01110011 01110100 01101001 01100011 00100000 01010000 01110010 01101111 01110100 01101111
01100011 01101111 01101100

01000101 01101110 01110100 01100001 01101110 01100111 01101100 01100101 01100100 00100000 01010000 01100001 01110010
01110100 01101001 01100011 01101100 01100101 01110011 00100000 01000001 01110011 01110011 01101001 01100111 01101110
01100101 01100100 00101100 00100000 01010011 01110101 01100010 00101101 01010000 01100001 01110010 01110100 01101001
01100011 01101100 01100101 01110011 00100000 01000101 01101110 01110100 01100001 01101110 01100111 01101100 01100101
01100100 00101100 00100000 01000011 01110010 01111001 01110011 01110100 01100001 01101100 00100000 01010011 01110100
01101111 01110010 01100001 01100111 01100101

01001111 01110110 01100101 01110010 01110010 01101001 01100100 01100101 00100000 01001100 01101111 01100111 01101001
01100011 00101100 00100000 01000011 01101111 01101110 01110100 01110010 01100001 01100100 01101001 01100011 01110100
01101001 01101111 01101110 00100000 01010100 01110010 01101001 01100111 01100111 01100101 01110010 00101100 00100000
01010100 01110010 01101001 01100010 01110101 01110100 01100101 00100000 01000101 01101110 01100110 01101111 01110010
01100011 01100101 01101101 01100101 01101110 01110100

T' = T * (1 - (R/S) * (ρ/ρ₀) * (v/c) * (ω/ω₀))

T' = T * (1 - (S/S₀) * (ρ/ρ₀) * (v/c) * (ω/ω₀))

ω' = ω * (1 - (r²/r_s²)) * (1 + (ρ/ρ₀))

GM/Rc² + ω²R²/c² + ρ/ρ₀ = 1

(S/S₀) * (ρ/ρ₀) * (ω/ω₀) > 1

U_EOS = (2πR_E)/(T_orbit × 3600) × C_orb

P_ellipse ≈ π[3(a + b) - √((3a + b)(a + 3b))](1 + δ_F)

E_out = E_in + ∫(B² dV) - P_loss

τ' = τ * (1 - (S/S₀) * (ρ/ρ₀))

Based on Tesla's principle that 3, 6, and 9 represent fundamental energy states:
- State 3: Base energy level (1, 4, 7 → 3)
- State 6: Doubled energy level (2, 5, 8 → 6)  
- State 9: Transcendent energy level (3, 6, 9, 0 → 9)
"""

def __init__(self):
    """Initialize Tesla 3-6-9 logic system."""
    # Tesla's fundamental energy amplification factors
    self.energy_weights = {
        3: 1.0,   # Base harmonic
        6: 2.0,   # First overtone (doubled)
        9: 4.0    # Transcendent state (quadrupled)
    }
    
    # Digital root mapping to Tesla states
    self.digit_to_tesla = {
        1: 3, 2: 6, 3: 9,
        4: 3, 5: 6, 6: 9,
        7: 3, 8: 6, 9: 9,
        0: 9  # Zero maps to transcendent state
    }
    
    # Reverse mapping for analysis
    self.tesla_to_digits = {
        3: [1, 4, 7],
        6: [2, 5, 8], 
        9: [0, 3, 6, 9]
    }

def digital_root(self, n: Union[int, float]) -> int:
    """
    Calculate digital root of a number.
    
    The digital root is the recursive sum of digits until a single digit remains.
    
    Args:
        n: Input number (int or float)
        
    Returns:
        Digital root (1-9, with 0 treated as 9)
        
    Examples:
        digital_root(123) = 6 (1+2+3=6)
        digital_root(456) = 6 (4+5+6=15, 1+5=6)
        digital_root(789) = 6 (7+8+9=24, 2+4=6)
    """
    if isinstance(n, float):
        # For floats, use meaningful digits
        n = abs(n)
        if n < 1:
            n = int(n * 1000000)  # Scale small numbers
        else:
            n = int(n * 100)  # Preserve decimal precision
    
    n = abs(int(n))
    
    if n == 0:
        return 9  # Tesla principle: 0 maps to transcendent state
        
    while n >= 10:
        n = sum(int(digit) for digit in str(n))
        
    return n if n != 0 else 9

def to_tesla_state(self, value: Union[int, float]) -> int:
    """
    Convert any value to Tesla 3-6-9 state.
    
    Args:
        value: Input value to classify
        
    Returns:
        Tesla state (3, 6, or 9)
    """
    root = self.digital_root(value)
    return self.digit_to_tesla[root]

def tesla_energy(self, value: Union[int, float]) -> float:
    """
    Get Tesla energy amplification factor for a value.
    
    Args:
        value: Input value
        
    Returns:
        Energy amplification factor
    """
    state = self.to_tesla_state(value)
    return self.energy_weights[state]

def tesla_sequence(self, start: int, length: int) -> List[Dict]:
    """
    Generate Tesla 3-6-9 sequence analysis.
    
    Args:
        start: Starting number
        length: Sequence length
        
    Returns:
        List of dictionaries with number, digital root, Tesla state, and energy
    """
    sequence = []
    for i in range(start, start + length):
        root = self.digital_root(i)
        state = self.to_tesla_state(i)
        energy = self.tesla_energy(i)
        
        sequence.append({
            'number': i,
            'digital_root': root,
            'tesla_state': state,
            'energy_factor': energy,
            'is_special': state in [3, 6, 9] and root in [3, 6, 9]
        })
    
    return sequence

def analyze_distribution(self, values: List[Union[int, float]]) -> Dict:
    """
    Analyze Tesla state distribution in a dataset.
    
    Args:
        values: List of values to analyze
        
    Returns:
        Distribution analysis dictionary
    """
    states = [self.to_tesla_state(v) for v in values]
    roots = [self.digital_root(v) for v in values]
    
    # Count distributions
    state_counts = {3: 0, 6: 0, 9: 0}
    root_counts = {i: 0 for i in range(1, 10)}
    
    for state in states:
        state_counts[state] += 1
        
    for root in roots:
        root_counts[root] += 1
    
    total = len(values)
    
    return {
        'total_count': total,
        'tesla_distribution': {
            state: {'count': count, 'percentage': count/total*100}
            for state, count in state_counts.items()
        },
        'root_distribution': {
            root: {'count': count, 'percentage': count/total*100}
            for root, count in root_counts.items()
        },
        'energy_stats': {
            'mean_energy': np.mean([self.tesla_energy(v) for v in values]),
            'max_energy': max([self.tesla_energy(v) for v in values]),
            'min_energy': min([self.tesla_energy(v) for v in values])
        }
    }

def vortex_mathematics(self, n: int = 12) -> Dict:
    """
    Demonstrate Tesla's vortex mathematics with 3-6-9 pattern.
    
    Tesla observed that doubling creates a specific pattern:
    1→2→4→8→7→5→1... (skips 3,6,9)
    3→6→3→6→3→6... (stable oscillation)
    9→9→9→9→9→9... (transcendent stability)
    
    Args:
        n: Number of iterations
        
    Returns:
        Vortex pattern analysis
    """
    # Starting with 1: doubling sequence
    sequence_1 = []
    current = 1
    for i in range(n):
        sequence_1.append(current)
        current = self.digital_root(current * 2)
    
    # Starting with 3: doubling sequence  
    sequence_3 = []
    current = 3
    for i in range(n):
        sequence_3.append(current)
        current = self.digital_root(current * 2)
        
    # Starting with 9: doubling sequence
    sequence_9 = []
    current = 9
    for i in range(n):
        sequence_9.append(current)
        current = self.digital_root(current * 2)
    
    return {
        'sequence_1': sequence_1,
        'sequence_3': sequence_3, 
        'sequence_9': sequence_9,
        'pattern_1': list(set(sequence_1)),  # Unique values in pattern
        'pattern_3': list(set(sequence_3)),
        'pattern_9': list(set(sequence_9)),
        'cycles': {
            'base_cycle': [1, 2, 4, 8, 7, 5],  # The 6-step cycle
            'tesla_369': [3, 6, 9],             # Tesla's special numbers
            'transcendent': [9]                  # Pure transcendent state
        }
    }

def quantum_resonance_map(self, frequencies: List[float]) -> Dict:
    """
    Map frequencies to Tesla states for quantum resonance analysis.
    
    Args:
        frequencies: List of frequencies in Hz
        
    Returns:
        Resonance mapping with Tesla states
    """
    resonance_map = []
    
    for freq in frequencies:
        # Convert frequency to integer for digital root calculation
        freq_int = int(freq * 1000)  # Scale to preserve meaningful digits
        
        root = self.digital_root(freq_int)
        state = self.to_tesla_state(freq_int)
        energy = self.tesla_energy(freq_int)
        
        # Calculate harmonic relationships
        harmonics = [freq * (i+1) for i in range(9)]
        harmonic_states = [self.to_tesla_state(h*1000) for h in harmonics]
        
        resonance_map.append({
            'frequency': freq,
            'digital_root': root,
            'tesla_state': state,
            'energy_factor': energy,
            'harmonics': harmonics[:3],  # First 3 harmonics
            'harmonic_states': harmonic_states[:3],
            'resonance_strength': energy * (1 + 0.1 * harmonic_states.count(9))
        })
    
    return {
        'resonance_data': resonance_map,
        'dominant_states': max(set([r['tesla_state'] for r in resonance_map]), 
                             key=[r['tesla_state'] for r in resonance_map].count)
    }

def plot_tesla_distribution(self, values: List[Union[int, float]], 
                           title: str = "Tesla 3-6-9 State Distribution"):
    """
    Visualize Tesla state distribution.
    
    Args:
        values: Values to analyze and plot
        title: Plot title
    """
    analysis = self.analyze_distribution(values)
    
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
    
    # Tesla state distribution
    states = list(analysis['tesla_distribution'].keys())
    counts = [analysis['tesla_distribution'][s]['count'] for s in states]
    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1']  # Red, Teal, Blue
    
    ax1.pie(counts, labels=[f'State {s}' for s in states], colors=colors, autopct='%1.1f%%')
    ax1.set_title('Tesla State Distribution (3-6-9)')
    
    # Digital root distribution
    roots = list(range(1, 10))
    root_counts = [analysis['root_distribution'][r]['count'] for r in roots]
    root_colors = [colors[self.digit_to_tesla[r]//3-1] for r in roots]
    
    bars = ax2.bar(roots, root_counts, color=root_colors, alpha=0.7)
    ax2.set_xlabel('Digital Root')
    ax2.set_ylabel('Count')
    ax2.set_title('Digital Root Distribution')
    ax2.set_xticks(roots)
    
    # Energy factor histogram
    energies = [self.tesla_energy(v) for v in values]
    ax3.hist(energies, bins=20, color='purple', alpha=0.7, edgecolor='black')
    ax3.set_xlabel('Tesla Energy Factor')
    ax3.set_ylabel('Frequency')
    ax3.set_title('Energy Factor Distribution')
    
    # Tesla sequence pattern
    if len(values) >= 50:
        sample_values = values[:50]
    else:
        sample_values = values
        
    tesla_states = [self.to_tesla_state(v) for v in sample_values]
    ax4.plot(tesla_states, 'o-', color='red', alpha=0.7, linewidth=2, markersize=4)
    ax4.set_xlabel('Sample Index')
    ax4.set_ylabel('Tesla State')
    ax4.set_title('Tesla State Sequence Pattern')
    ax4.set_yticks([3, 6, 9])
    ax4.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.suptitle(title, fontsize=16, y=1.02)
    plt.show()

def plot_vortex_mathematics(self):
    """Visualize Tesla's vortex mathematics patterns."""
    vortex = self.vortex_mathematics(12)
    
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
    
    # Sequence starting with 1
    ax1.plot(vortex['sequence_1'], 'o-', linewidth=2, markersize=8, color='blue')
    ax1.set_title('Doubling Sequence: Start with 1')
    ax1.set_ylabel('Digital Root')
    ax1.set_xlabel('Iteration')
    ax1.grid(True, alpha=0.3)
    ax1.set_ylim(0, 10)
    
    # Sequence starting with 3
    ax2.plot(vortex['sequence_3'], 'o-', linewidth=2, markersize=8, color='red')
    ax2.set_title('Doubling Sequence: Start with 3')
    ax2.set_ylabel('Digital Root')
    ax2.set_xlabel('Iteration')
    ax2.grid(True, alpha=0.3)
    ax2.set_ylim(0, 10)
    
    # Sequence starting with 9
    ax3.plot(vortex['sequence_9'], 'o-', linewidth=2, markersize=8, color='purple')
    ax3.set_title('Doubling Sequence: Start with 9 (Transcendent)')
    ax3.set_ylabel('Digital Root')
    ax3.set_xlabel('Iteration')
    ax3.grid(True, alpha=0.3)
    ax3.set_ylim(0, 10)
    
    # Circular vortex pattern
    angles = np.linspace(0, 2*np.pi, 9)
    radius = 1
    
    # Plot the enneagon (9-sided polygon)
    for i, digit in enumerate(range(1, 10)):
        x = radius * np.cos(angles[i])
        y = radius * np.sin(angles[i])
        
        if digit in [3, 6, 9]:
            ax4.scatter(x, y, s=200, c='red', marker='*', zorder=3)
        else:
            ax4.scatter(x, y, s=100, c='blue', marker='o', zorder=2)
        
        ax4.annotate(str(digit), (x, y), xytext=(5, 5), textcoords='offset points',
                    fontsize=12, fontweight='bold')
    
    # Draw the doubling sequence path
    sequence_coords = []
    for digit in vortex['sequence_1'][:6]:  # One complete cycle
        idx = digit - 1
        x = radius * np.cos(angles[idx])
        y = radius * np.sin(angles[idx])
        sequence_coords.append((x, y))
    
    for i in range(len(sequence_coords)-1):
        ax4.annotate('', xy=sequence_coords[i+1], xytext=sequence_coords[i],
                    arrowprops=dict(arrowstyle='->', color='green', lw=2, alpha=0.7))
    
    ax4.set_xlim(-1.5, 1.5)
    ax4.set_ylim(-1.5, 1.5)
    ax4.set_aspect('equal')
    ax4.set_title('Tesla Vortex: 3-6-9 Pattern\n(Red stars = Tesla numbers)')
    ax4.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.suptitle("Tesla's Vortex Mathematics: 3-6-9 Logic", fontsize=16, y=1.02)
    plt.show()

print("Tesla 3-6-9 Digital Root Logic System")
print("=" * 50)
print("Tesla: 'If you only knew the magnificence of the 3, 6 and 9,")
print("then you would have the key to the universe.'")
print()

# Digital root examples
print("Digital Root Examples:")
test_numbers = [123, 456, 789, 1234, 5678, 9876]
for num in test_numbers:
    root = tesla.digital_root(num)
    state = tesla.to_tesla_state(num)
    energy = tesla.tesla_energy(num)
    print(f"{num:4d} → root: {root}, Tesla state: {state}, energy: {energy:.1f}")

print("\nTesla State Classification:")
print("State 3 (Base):        1

for cycle in range(cycles):
    print(f"\n===== Simulation Cycle {cycle+1} =====")
    conscious_input = receive_conscious_input()
    modulated_signal = modulate_signal(conscious_input)
    echo_signal = echo_pulse_response(modulated_signal)
    adaptation = process_llal_feedback(echo_signal)
    print(f"Cycle {cycle+1} complete with adaptation {adaptation:.2f}")

print("\n🔚 Consciousness Gateway Simulation Complete.")

Implements run-length encoding compression with parity-based error detection
for robust data transmission in consciousness gateway protocols.

Author: Donald Paul Smith (FatherTime)
Part of the SDKP Framework and VFE Metaverse Protocol Suite
"""

def __init__(self):
    pass

def rle_compress(self, data: str) -> str:
    """
    Run-Length Encode input string.
    
    Converts repeated character sequences into count-character pairs
    for efficient symbolic data compression.
    
    Args:
        data (str): Input string to compress
        
    Returns:
        str: Compressed string using run-length encoding
    """
    if not data:
        return ""
    
    compressed = []
    count = 1
    prev_char = data[0]

    for char in data[1:]:
        if char == prev_char:
            count += 1
        else:
            compressed.append(f"{count}{prev_char}")
            prev_char = char
            count = 1
    
    compressed.append(f"{count}{prev_char}")
    return ''.join(compressed)

def rle_decompress(self, compressed: str) -> str:
    """
    Decompress RLE string.
    
    Reconstructs original data from count-character pairs generated
    by run-length encoding compression.
    
    Args:
        compressed (str): RLE compressed string
        
    Returns:
        str: Decompressed original string
    """
    decompressed = []
    count_str = ''
    
    for char in compressed:
        if char.isdigit():
            count_str += char
        else:
            count = int(count_str)
            decompressed.append(char * count)
            count_str = ''
            
    return ''.join(decompressed)

def calculate_parity(self, data: str) -> int:
    """
    Calculate simple parity bit using XOR over bytes.
    
    Provides basic error detection capability by computing
    exclusive-or checksum across all character values.
    
    Args:
        data (str): Input data for parity calculation
        
    Returns:
        int: Parity value for error detection
    """
    parity = 0
    for char in data:
        parity ^= ord(char)
    return parity

def encode(self, data: str) -> dict:
    """
    Compress data and add error correction parity.
    
    Performs complete encoding pipeline including compression
    and error detection code generation for robust transmission.
    
    Args:
        data (str): Original data to encode
        
    Returns:
        dict: Encoded data structure with compressed payload and parity
    """
    compressed = self.rle_compress(data)
    parity = self.calculate_parity(compressed)
    return {"compressed": compressed, "parity": parity}

def decode(self, encoded: dict) -> str:
    """
    Verify parity and decompress data.
    
    Validates data integrity through parity checking before
    decompression to ensure reliable data recovery.
    
    Args:
        encoded (dict): Encoded data structure with compression and parity
        
    Returns:
        str: Original decompressed data
        
    Raises:
        ValueError: If parity check fails indicating data corruption
    """
    compressed = encoded["compressed"]
    parity = encoded["parity"]
    
    calc_parity = self.calculate_parity(compressed)
    if calc_parity != parity:
        # In production ECC implementation, error correction would be attempted
        raise ValueError("Parity check failed - data corrupted")
        
    return self.rle_decompress(compressed)

def compression_ratio(self, original: str, encoded: dict) -> float:
    """
    Calculate compression efficiency ratio.
    
    Measures the effectiveness of the Kapnack compression algorithm
    by comparing original and compressed data sizes.
    
    Args:
        original (str): Original uncompressed data
        encoded (dict): Encoded data structure
        
    Returns:
        float: Compression ratio (original_size / compressed_size)
    """
    original_size = len(original)
    compressed_size = len(encoded["compressed"]) + 4  # Include parity overhead
    return original_size / compressed_size if compressed_size > 0 else 0.0

def validate_integrity(self, encoded: dict) -> bool:
    """
    Validate data integrity without decompression.
    
    Performs parity verification to determine if encoded data
    maintains integrity without full decompression overhead.
    
    Args:
        encoded (dict): Encoded data structure to validate
        
    Returns:
        bool: True if data integrity is verified, False otherwise
    """
    try:
        compressed = encoded["compressed"]
        parity = encoded["parity"]
        calc_parity = self.calculate_parity(compressed)
        return calc_parity == parity
    except (KeyError, TypeError):
        return False

Provides comprehensive testing of compression, decompression, and error
detection capabilities across different data patterns and scenarios.
"""
print("=== Kapnack Compression ECC Demonstration ===\n")

k = KapnackCompressionECC()

# Test Case 1: Basic compression with repeated characters
print("Test Case 1: Basic Repeated Character Compression")
original1 = "aaabbccdddddddddde"
encoded1 = k.encode(original1)
decoded1 = k.decode(encoded1)
ratio1 = k.compression_ratio(original1, encoded1)

print(f"Original: '{original1}' (Length: {len(original1)})")
print(f"Encoded: {encoded1}")
print(f"Decoded: '{decoded1}' (Match: {original1 == decoded1})")
print(f"Compression Ratio: {ratio1:.2f}:1\n")

# Test Case 2: Mixed content with variable repetition
print("Test Case 2: Mixed Content Pattern")
original2 = "synchronize_protocol_aaaa_bbbb_cccc"
encoded2 = k.encode(original2)
decoded2 = k.decode(encoded2)
ratio2 = k.compression_ratio(original2, encoded2)

print(f"Original: '{original2}' (Length: {len(original2)})")
print(f"Encoded: {encoded2}")
print(f"Decoded: '{decoded2}' (Match: {original2 == decoded2})")
print(f"Compression Ratio: {ratio2:.2f}:1\n")

# Test Case 3: Error detection simulation
print("Test Case 3: Error Detection Capability")
original3 = "test_error_detection"
encoded3 = k.encode(original3)

# Simulate data corruption by modifying parity
corrupted_encoded = encoded3.copy()
corrupted_encoded["parity"] = encoded3["parity"] ^ 1  # Flip one bit

print(f"Original: '{original3}'")
print(f"Valid encoding integrity: {k.validate_integrity(encoded3)}")
print(f"Corrupted encoding integrity: {k.validate_integrity(corrupted_encoded)}")

try:
    k.decode(corrupted_encoded)
    print("Error: Corruption not detected!")
except ValueError as e:
    print(f"✓ Corruption successfully detected: {e}\n")

# Test Case 4: Edge cases and empty data
print("Test Case 4: Edge Case Handling")
empty_encoded = k.encode("")
empty_decoded = k.decode(empty_encoded)
print(f"Empty string handling: '{empty_decoded}' (Success: {empty_decoded == ''})")

single_char = "x"
single_encoded = k.encode(single_char)
single_decoded = k.decode(single_encoded)
print(f"Single character: '{single_decoded}' (Success: {single_decoded == single_char})")

print("\n=== Kapnack Compression ECC Demonstration Complete ===")

if np.any(modes < 0):
    raise ValueError("Vibrational modes must be non-negative")

# Calculate each vibrational term a_i * sqrt(n_i)
vibrational_terms = coefficients * np.sqrt(modes)

if verbose:
    print("Vibrational Terms Breakdown:")
    print("-" * 60)
    for i, (a, n, term) in enumerate(zip(coefficients, modes, vibrational_terms)):
        print(f"Mode {n:2d}: Coefficient {a:8.5f} * sqrt({n}) = {term:8.5f}")
    print("-" * 60)

# Sum the vibrational contributions
vfe1_value = np.sum(vibrational_terms)

# Optionally normalize to compare across different coefficient scales
if normalize:
    norm_factor = np.sum(np.abs(coefficients))
    if norm_factor != 0:
        vfe1_value /= norm_factor
        if verbose:
            print(f"Normalization factor: {norm_factor:.5f}")
    else:
        print("Warning: Cannot normalize - sum of absolute coefficients is zero")

return vfe1_value

def __init__(self):
    self.quantum_coherence_threshold = 0.85
    self.entanglement_thresholds = {
        'weak': 0.1,
        'moderate': 0.3,
        'strong': 0.5,
        'maximal': 0.8
    }

def quantum_computerization_consciousness(self, flux1, flux2):
    """
    Enhanced QCC analysis with multiple entanglement metrics
    """
    # Ensure arrays are same length and handle NaN values
    min_len = min(len(flux1), len(flux2))
    flux1_clean = flux1[:min_len]
    flux2_clean = flux2[:min_len]
    
    # Remove NaN values
    mask = ~(np.isnan(flux1_clean) | np.isnan(flux2_clean))
    flux1_clean = flux1_clean[mask]
    flux2_clean = flux2_clean[mask]
    
    if len(flux1_clean) < 2:
        return 0.0, 0.0
    
    # Cross-correlation analysis
    cross_corr = np.correlate(flux1_clean, flux2_clean, mode='full')
    coherence_index = np.max(cross_corr) / (np.linalg.norm(flux1_clean) * np.linalg.norm(flux2_clean))
    
    # Quantum entanglement probability
    correlation = np.corrcoef(flux1_clean, flux2_clean)[0, 1]
    entanglement_probability = np.abs(correlation) ** 2
    
    return coherence_index, entanglement_probability

def advanced_entanglement_metrics(self, flux1, flux2):
    """
    Calculate advanced entanglement metrics
    """
    # Ensure arrays are same length and clean
    min_len = min(len(flux1), len(flux2))
    flux1_clean = flux1[:min_len]
    flux2_clean = flux2[:min_len]
    
    mask = ~(np.isnan(flux1_clean) | np.isnan(flux2_clean))
    flux1_clean = flux1_clean[mask]
    flux2_clean = flux2_clean[mask]
    
    if len(flux1_clean) < 2:
        return {'correlation': 0, 'mutual_info': 0, 'phase_sync': 0, 'coherence': 0}
    
    # Pearson correlation
    correlation = np.corrcoef(flux1_clean, flux2_clean)[0, 1]
    
    # Mutual information (simplified)
    mutual_info = self._calculate_mutual_information(flux1_clean, flux2_clean)
    
    # Phase synchronization
    phase_sync = self._calculate_phase_synchronization(flux1_clean, flux2_clean)
    
    # Quantum coherence
    coherence = self._calculate_quantum_coherence(flux1_clean, flux2_clean)
    
    return {
        'correlation': correlation,
        'mutual_info': mutual_info,
        'phase_sync': phase_sync,
        'coherence': coherence
    }

def _calculate_mutual_information(self, x, y):
    """Calculate mutual information between two signals"""
    # Discretize signals
    x_discrete = np.digitize(x, bins=np.percentile(x, [25, 50, 75]))
    y_discrete = np.digitize(y, bins=np.percentile(y, [25, 50, 75]))
    
    # Calculate joint and marginal probabilities
    joint_prob = np.histogram2d(x_discrete, y_discrete, bins=4)[0]
    joint_prob = joint_prob / np.sum(joint_prob)
    
    marginal_x = np.sum(joint_prob, axis=1)
    marginal_y = np.sum(joint_prob, axis=0)
    
    # Calculate mutual information
    mi = 0
    for i in range(len(marginal_x)):
        for j in range(len(marginal_y)):
            if joint_prob[i, j] > 0:
                mi += joint_prob[i, j] * np.log2(joint_prob[i, j] / (marginal_x[i] * marginal_y[j]))
    
    return mi

def _calculate_phase_synchronization(self, x, y):
    """Calculate phase synchronization using Hilbert transform"""
    # Hilbert transform to get instantaneous phase
    analytic_x = signal.hilbert(x)
    analytic_y = signal.hilbert(y)
    
    phase_x = np.angle(analytic_x)
    phase_y = np.angle(analytic_y)
    
    # Phase difference
    phase_diff = phase_x - phase_y
    
    # Synchronization index
    sync_index = np.abs(np.mean(np.exp(1j * phase_diff)))
    
    return sync_index

def _calculate_quantum_coherence(self, x, y):
    """Calculate quantum coherence measure"""
    # Normalize signals
    x_norm = (x - np.mean(x)) / np.std(x)
    y_norm = (y - np.mean(y)) / np.std(y)
    
    # Quantum coherence as normalized cross-correlation
    coherence = np.max(np.abs(np.correlate(x_norm, y_norm, mode='full'))) / len(x_norm)
    
    return coherence

# Base patterns
base_flux = 1000 + 100 * np.sin(2 * np.pi * np.arange(len(dates)) / 365.25)
solar_cycle = 50 * np.sin(2 * np.pi * np.arange(len(dates)) / (11 * 365.25))

# Add time-lagged relationships
lag_5_component = 30 * np.sin(2 * np.pi * np.arange(len(dates)) / 27.3)  # 27-day solar rotation
lag_10_component = 20 * np.sin(2 * np.pi * np.arange(len(dates)) / 14.0)  # 14-day cycle

# Neutrino flux with entangled components
neutrino_flux = base_flux + solar_cycle + np.random.normal(0, 30, len(dates))
neutrino_flux += np.roll(lag_5_component, -5)  # 5-day lag
neutrino_flux += np.roll(lag_10_component, -10)  # 10-day lag

# Solar flare intensity with base patterns
flare_intensity = []
for i in range(len(dates)):
    base_intensity = 5 + 3 * np.sin(2 * np.pi * i / 27.3)
    if np.random.random() < 0.3:
        flare_intensity.append(base_intensity + np.random.exponential(2))
    else:
        flare_intensity.append(base_intensity * 0.1)

# Create DataFrames
neutrino_data = pd.DataFrame({"flux": neutrino_flux}, index=dates)
flare_data = pd.DataFrame({"total_intensity": flare_intensity}, index=dates)

# EOS correction
eos_correction = 1 + 0.033 * np.cos(2 * np.pi * np.arange(len(dates)) / 365.25)
neutrino_data["flux_eos_corrected"] = neutrino_data["flux"] * eos_correction

return neutrino_data, flare_data

# Initialize analyzer
analyzer = QuantumEntanglementAnalyzer()

# Generate or load data
neutrino_data, flare_data = generate_enhanced_synthetic_data()

# Combine data
combined = pd.concat([
    neutrino_data["flux"],
    neutrino_data["flux_eos_corrected"],
    flare_data["total_intensity"]
], axis=1)
combined.columns = ["Neutrino_Flux", "Neutrino_Flux_EOS_Corrected", "Total_Flare_Intensity"]
combined = combined.fillna(0)

# Define analysis parameters
max_lag = 30
window_sizes = [7, 14, 21, 30]  # Different time windows for analysis
lags = np.arange(1, max_lag + 1)

# A. Basic Time-Lagged Entanglement Analysis
print("Computing basic time-lagged entanglement matrix...")
entanglement_matrix = np.zeros(len(lags))
coherence_matrix = np.zeros(len(lags))

for i, lag in enumerate(lags):
    shifted_flare = combined["Total_Flare_Intensity"].shift(lag).dropna()
    aligned_flux = combined["Neutrino_Flux_EOS_Corrected"].iloc[lag:]
    
    # Ensure same length
    min_len = min(len(shifted_flare), len(aligned_flux))
    shifted_flare = shifted_flare.iloc[:min_len]
    aligned_flux = aligned_flux.iloc[:min_len]
    
    coherence, entanglement = analyzer.quantum_computerization_consciousness(
        aligned_flux.values, shifted_flare.values
    )
    
    entanglement_matrix[i] = entanglement
    coherence_matrix[i] = coherence

# B. Advanced Multi-Window Entanglement Analysis
print("Computing multi-window entanglement analysis...")
multi_window_matrix = np.zeros((len(window_sizes), len(lags)))

for w_idx, window in enumerate(window_sizes):
    for l_idx, lag in enumerate(lags):
        entanglements = []
        
        # Rolling window analysis
        for start in range(0, len(combined) - window - lag, window // 2):
            end = start + window
            
            flux_window = combined["Neutrino_Flux_EOS_Corrected"].iloc[start:end]
            flare_window = combined["Total_Flare_Intensity"].iloc[start+lag:end+lag]
            
            if len(flux_window) == len(flare_window) and len(flux_window) > 1:
                _, ent = analyzer.quantum_computerization_consciousness(
                    flux_window.values, flare_window.values
                )
                entanglements.append(ent)
        
        multi_window_matrix[w_idx, l_idx] = np.mean(entanglements) if entanglements else 0

# C. Advanced Metrics Heatmap
print("Computing advanced metrics heatmap...")
metrics_names = ['correlation', 'mutual_info', 'phase_sync', 'coherence']
advanced_metrics_matrix = np.zeros((len(metrics_names), len(lags)))

for l_idx, lag in enumerate(lags):
    shifted_flare = combined["Total_Flare_Intensity"].shift(lag).dropna()
    aligned_flux = combined["Neutrino_Flux_EOS_Corrected"].iloc[lag:]
    
    min_len = min(len(shifted_flare), len(aligned_flux))
    shifted_flare = shifted_flare.iloc[:min_len]
    aligned_flux = aligned_flux.iloc[:min_len]
    
    metrics = analyzer.advanced_entanglement_metrics(
        aligned_flux.values, shifted_flare.values
    )
    
    for m_idx, metric in enumerate(metrics_names):
        advanced_metrics_matrix[m_idx, l_idx] = metrics[metric]

# Create comprehensive visualization
fig, axes = plt.subplots(2, 3, figsize=(20, 12))
fig.suptitle('Time-Lagged Entanglement Analysis using SDKP Framework', fontsize=16)

# Plot 1: Basic Entanglement vs Lag
ax1 = axes[0, 0]
ax1.plot(lags, entanglement_matrix, 'b-', linewidth=2, label='Entanglement')
ax1.plot(lags, coherence_matrix, 'r--', linewidth=2, label='Coherence')
ax1.axhline(y=analyzer.entanglement_thresholds['weak'], color='gray', linestyle=':', alpha=0.5)
ax1.axhline(y=analyzer.entanglement_thresholds['moderate'], color='orange', linestyle=':', alpha=0.5)
ax1.axhline(y=analyzer.entanglement_thresholds['strong'], color='red', linestyle=':', alpha=0.5)
ax1.set_xlabel('Lag (days)')
ax1.set_ylabel('Entanglement/Coherence')
ax1.set_title('Basic Time-Lagged Entanglement')
ax1.legend()
ax1.grid(True, alpha=0.3)

# Plot 2: Multi-Window Heatmap
ax2 = axes[0, 1]
im2 = ax2.imshow(multi_window_matrix, aspect='auto', cmap='viridis', 
                 extent=[lags[0], lags[-1], window_sizes[0], window_sizes[-1]])
ax2.set_xlabel('Lag (days)')
ax2.set_ylabel('Window Size (days)')
ax2.set_title('Multi-Window Entanglement Heatmap')
plt.colorbar(im2, ax=ax2, label='Entanglement Probability')

# Plot 3: Advanced Metrics Heatmap
ax3 = axes[0, 2]
im3 = ax3.imshow(advanced_metrics_matrix, aspect='auto', cmap='RdYlBu_r',
                 extent=[lags[0], lags[-1], 0, len(metrics_names)])
ax3.set_xlabel('Lag (days)')
ax3.set_ylabel('Metric Type')
ax3.set_yticks(range(len(metrics_names)))
ax3.set_yticklabels(metrics_names)
ax3.set_title('Advanced Entanglement Metrics')
plt.colorbar(im3, ax=ax3, label='Metric Value')

# Plot 4: Entanglement Distribution
ax4 = axes[1, 0]
ax4.hist(entanglement_matrix, bins=15, alpha=0.7, density=True, label='Entanglement')
ax4.hist(coherence_matrix, bins=15, alpha=0.7, density=True, label='Coherence')
ax4.axvline(x=analyzer.entanglement_thresholds['moderate'], color='orange', linestyle='--', alpha=0.7)
ax4.set_xlabel('Entanglement/Coherence Value')
ax4.set_ylabel('Density')
ax4.set_title('Entanglement Distribution')
ax4.legend()
ax4.grid(True, alpha=0.3)

# Plot 5: Peak Entanglement Analysis
ax5 = axes[1, 1]
# Find peaks in entanglement
peaks, properties = signal.find_peaks(entanglement_matrix, height=0.1, distance=2)
ax5.plot(lags, entanglement_matrix, 'b-', linewidth=2)
ax5.scatter(lags[peaks], entanglement_matrix[peaks], color='red', s=100, zorder=5)

# Annotate peaks
for peak_idx in peaks:
    ax5.annotate(f'{lags[peak_idx]}d\n{entanglement_matrix[peak_idx]:.3f}',
                xy=(lags[peak_idx], entanglement_matrix[peak_idx]),
                xytext=(10, 10), textcoords='offset points',
                bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.7),
                arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))

ax5.set_xlabel('Lag (days)')
ax5.set_ylabel('Entanglement Probability')
ax5.set_title('Peak Entanglement Detection')
ax5.grid(True, alpha=0.3)

# Plot 6: Time-Frequency Analysis
ax6 = axes[1, 2]
# Create time-frequency representation
frequencies = np.fft.fftfreq(len(lags), d=1)
fft_entanglement = np.abs(np.fft.fft(entanglement_matrix))

mask = frequencies > 0
ax6.semilogy(frequencies[mask], fft_entanglement[mask], 'g-', linewidth=2)
ax6.set_xlabel('Frequency (1/day)')
ax6.set_ylabel('Power Spectral Density')
ax6.set_title('Entanglement Frequency Analysis')
ax6.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

# Analysis Summary
print("\n=== ENTANGLEMENT ANALYSIS SUMMARY ===")

# Find optimal lags
max_entanglement_idx = np.argmax(entanglement_matrix)
max_coherence_idx = np.argmax(coherence_matrix)

print(f"Maximum Entanglement: {entanglement_matrix[max_entanglement_idx]:.4f} at lag {lags[max_entanglement_idx]} days")
print(f"Maximum Coherence: {coherence_matrix[max_coherence_idx]:.4f} at lag {lags[max_coherence_idx]} days")

# Entanglement classification
strong_entanglement_lags = lags[entanglement_matrix > analyzer.entanglement_thresholds['strong']]
moderate_entanglement_lags = lags[(entanglement_matrix > analyzer.entanglement_thresholds['moderate']) & 
                                (entanglement_matrix <= analyzer.entanglement_thresholds['strong'])]

print(f"\nStrong Entanglement Lags: {strong_entanglement_lags}")
print(f"Moderate Entanglement Lags: {moderate_entanglement_lags}")

# Peak analysis
peaks, _ = signal.find_peaks(entanglement_matrix, height=0.1, distance=2)
print(f"\nDetected Entanglement Peaks at lags: {lags[peaks]} days")
print(f"Peak values: {entanglement_matrix[peaks]}")

# Advanced metrics summary
print(f"\n=== ADVANCED METRICS SUMMARY ===")
for i, metric in enumerate(metrics_names):
    best_lag_idx = np.argmax(np.abs(advanced_metrics_matrix[i, :]))
    print(f"{metric.capitalize()}: {advanced_metrics_matrix[i, best_lag_idx]:.4f} at lag {lags[best_lag_idx]} days")

# Multi-window analysis
print(f"\n=== MULTI-WINDOW ANALYSIS ===")
best_window_lag = np.unravel_index(np.argmax(multi_window_matrix), multi_window_matrix.shape)
print(f"Optimal window-lag combination: {window_sizes[best_window_lag[0]]} days window, {lags[best_window_lag[1]]} days lag")
print(f"Maximum entanglement: {multi_window_matrix[best_window_lag]:.4f}")

return {
    'lags': lags,
    'entanglement_matrix': entanglement_matrix,
    'coherence_matrix': coherence_matrix,
    'multi_window_matrix': multi_window_matrix,
    'advanced_metrics_matrix': advanced_metrics_matrix,
    'window_sizes': window_sizes,
    'metrics_names': metrics_names
}

print("\n=== SDKP FRAMEWORK VALIDATION ===")
print("Framework components utilized:")
print("- QCC (Quantum Computerization Consciousness)")
print("- SDKP time-lag analysis")
print("- EOS (Earth Orbital Speed) corrections")
print("- Multi-dimensional entanglement metrics")
print("\nCitation: Smith, D. P. (2025). SDKP Framework: A Unified Principle for")
print("Emergent Mass, Time, and Quantum Coherence. Zenodo.")
print("https://doi.org/10.5281/zenodo.14850016")

def __init__(self):
    # EOS (Earth Orbital Speed) constants
    self.earth_orbital_speed = 29.78e3  # m/s
    self.solar_neutrino_energy_range = (0.1, 20)  # MeV
    self.quantum_coherence_threshold = 0.85
    
def calculate_sdkp_metric(self, size, density, kinetic_energy):
    """
    Calculate SDKP metric: S×D×K principle
    """
    return size * density * kinetic_energy

def shape_dimension_number(self, flux_data):
    """
    SD&N (Shape-Dimension-Number) analysis
    """
    # Shape analysis - flux distribution shape
    shape_factor = stats.skew(flux_data)
    
    # Dimension analysis - fractal dimension approximation
    dimension = self._calculate_fractal_dimension(flux_data)
    
    # Number analysis - discrete event counting
    number_factor = len(flux_data[flux_data > np.mean(flux_data)])
    
    return shape_factor, dimension, number_factor

def _calculate_fractal_dimension(self, data):
    """Calculate fractal dimension using box-counting method"""
    scales = np.logspace(0.1, 2, 20)
    counts = []
    
    for scale in scales:
        bins = int(len(data) / scale)
        if bins > 1:
            hist, _ = np.histogram(data, bins=bins)
            counts.append(np.count_nonzero(hist))
        else:
            counts.append(1)
    
    # Linear regression in log space
    log_scales = np.log(scales[:len(counts)])
    log_counts = np.log(counts)
    slope, _ = np.polyfit(log_scales, log_counts, 1)
    return -slope

def quantum_computerization_consciousness(self, neutrino_flux, solar_activity):
    """
    QCC (Quantum Computerization Consciousness) analysis
    Analyzes quantum coherence patterns in solar-neutrino interactions
    """
    # Cross-correlation analysis
    cross_corr = np.correlate(neutrino_flux, solar_activity, mode='full')
    coherence_index = np.max(cross_corr) / (np.linalg.norm(neutrino_flux) * np.linalg.norm(solar_activity))
    
    # Quantum entanglement prediction (as per SDKP framework)
    entanglement_probability = self._calculate_entanglement_probability(neutrino_flux, solar_activity)
    
    return coherence_index, entanglement_probability

def _calculate_entanglement_probability(self, flux1, flux2):
    """Calculate quantum entanglement probability"""
    # Normalized cross-correlation
    correlation = np.corrcoef(flux1, flux2)[0, 1]
    
    # Convert to entanglement probability using SDKP principles
    entanglement_prob = np.abs(correlation) ** 2
    
    return entanglement_prob

def earth_orbital_speed_correction(self, neutrino_data, timestamps):
    """
    EOS (Earth Orbital Speed) correction for neutrino flux
    """
    # Calculate Earth's position-dependent velocity corrections
    days_from_perihelion = [(ts - datetime(ts.year, 1, 3)).days for ts in timestamps]
    orbital_corrections = []
    
    for day in days_from_perihelion:
        # Earth's orbital velocity variation
        orbital_angle = 2 * np.pi * day / 365.25
        velocity_correction = 1 + 0.033 * np.cos(orbital_angle)
        orbital_corrections.append(velocity_correction)
    
    corrected_flux = neutrino_data * np.array(orbital_corrections)
    return corrected_flux, orbital_corrections

def sdvr_analysis(self, flux_data, time_series):
    """
    SDVR (Shape-Dimension-Velocity Rotation) analysis
    """
    # Shape analysis
    shape_params = self._analyze_flux_shape(flux_data)
    
    # Dimension analysis (temporal)
    dimension = self._calculate_temporal_dimension(flux_data)
    
    # Velocity analysis (rate of change)
    velocity = np.gradient(flux_data)
    
    # Rotation analysis (cyclical patterns)
    rotation_freq = self._find_dominant_frequencies(flux_data)
    
    return {
        'shape_params': shape_params,
        'dimension': dimension,
        'velocity_profile': velocity,
        'rotation_frequencies': rotation_freq
    }

def _analyze_flux_shape(self, data):
    """Analyze flux distribution shape parameters"""
    return {
        'mean': np.mean(data),
        'std': np.std(data),
        'skewness': stats.skew(data),
        'kurtosis': stats.kurtosis(data)
    }

def _calculate_temporal_dimension(self, data):
    """Calculate temporal dimension using correlation sum method"""
    # Simplified temporal dimension calculation
    delays = range(1, min(50, len(data)//4))
    correlations = [np.corrcoef(data[:-d], data[d:])[0,1] for d in delays]
    
    # Find embedding dimension
    dimension = len([c for c in correlations if abs(c) > 0.1])
    return dimension

def _find_dominant_frequencies(self, data):
    """Find dominant frequencies using FFT"""
    fft = np.fft.fft(data)
    freqs = np.fft.fftfreq(len(data))
    
    # Find peaks
    power = np.abs(fft)**2
    peaks, _ = signal.find_peaks(power, height=np.max(power)*0.1)
    
    dominant_freqs = freqs[peaks]
    return dominant_freqs[dominant_freqs > 0][:5]  # Top 5 positive frequencies

    # SDKP-based outlier detection
    sdkp = SDKPFramework()
    z_scores = np.abs(stats.zscore(neutrino_data["flux"]))
    
    # Enhanced outlier removal using quantum coherence principles
    coherence_threshold = sdkp.quantum_coherence_threshold
    mask = z_scores < (3 * coherence_threshold)
    neutrino_data = neutrino_data[mask]
    
    return neutrino_data.resample("D").mean()

except FileNotFoundError:
    print(f"Warning: {filepath} not found. Generating synthetic data with SDKP principles.")
    return generate_synthetic_neutrino_data()

    # Enhanced flare classification using SDKP principles
    flare_map = {"A": 0.1, "B": 0.5, "C": 1, "M": 10, "X": 100}
    solar_flares["flare_intensity"] = solar_flares["class"].map(flare_map)
    solar_flares.set_index("date_time", inplace=True)
    
    # SDKP-based aggregation
    flare_daily = solar_flares.resample("D").agg({
        "flare_intensity": ["sum", "max", "count", "std"]
    }).round(2)
    flare_daily.columns = ["total_intensity", "max_intensity", "flare_count", "intensity_std"]
    
    return flare_daily

except FileNotFoundError:
    print(f"Warning: {filepath} not found. Generating synthetic data with SDKP principles.")
    return generate_synthetic_flare_data()

# Base flux with SDKP modulation
base_flux = 1000 + 100 * np.sin(2 * np.pi * np.arange(len(dates)) / 365.25)

# Add SDKP-based variations
sdkp_modulation = 75 * np.sin(2 * np.pi * np.arange(len(dates)) / 27.3)  # Solar rotation
quantum_noise = np.random.normal(0, 30, len(dates))

# EOS correction
eos_correction = 1 + 0.033 * np.cos(2 * np.pi * np.arange(len(dates)) / 365.25)

flux = (base_flux + sdkp_modulation + quantum_noise) * eos_correction

return pd.DataFrame({"flux": flux}, index=dates)

# SDKP-based flare probability with solar cycle
base_prob = 0.3
solar_cycle_mod = 0.1 * np.sin(2 * np.pi * np.arange(len(dates)) / (11 * 365.25))

total_intensity, max_intensity, flare_count, intensity_std = [], [], [], []

for i, date in enumerate(dates):
    flare_prob = base_prob + solar_cycle_mod[i]
    
    if np.random.random() < flare_prob:
        # SDKP-based flare generation
        n_flares = np.random.randint(1, 6)
        intensities = np.random.exponential(2, n_flares)
        
        # Apply SDKP size-density-kinetic scaling
        sdkp_scaling = 1 + 0.5 * np.sin(2 * np.pi * i / 27.3)
        intensities *= sdkp_scaling
        
        total_intensity.append(np.sum(intensities))
        max_intensity.append(np.max(intensities))
        flare_count.append(n_flares)
        intensity_std.append(np.std(intensities) if n_flares > 1 else 0)
    else:
        total_intensity.append(0)
        max_intensity.append(0)
        flare_count.append(0)
        intensity_std.append(0)

return pd.DataFrame({
    "total_intensity": total_intensity,
    "max_intensity": max_intensity,
    "flare_count": flare_count,
    "intensity_std": intensity_std
}, index=dates)

# Initialize SDKP framework
sdkp = SDKPFramework()

# Load data
neutrino_data = load_neutrino_data("neutrino_flux.csv")
solar_flares = load_solar_flare_data("solar_flares.csv")

# Combine data
combined = pd.concat([
    neutrino_data["flux"],
    solar_flares["total_intensity"],
    solar_flares["max_intensity"],
    solar_flares["flare_count"],
    solar_flares["intensity_std"]
], axis=1).fillna(0)

combined.columns = ["Neutrino_Flux", "Total_Flare_Intensity", "Max_Flare_Intensity", "Flare_Count", "Intensity_Std"]

# Apply EOS correction
timestamps = [idx.to_pydatetime() for idx in combined.index]
corrected_flux, eos_corrections = sdkp.earth_orbital_speed_correction(
    combined["Neutrino_Flux"].values, timestamps
)
combined["Neutrino_Flux_EOS_Corrected"] = corrected_flux

# SDKP Analysis
print("=== SDKP FRAMEWORK ANALYSIS ===")

# SD&N Analysis
shape_factor, dimension, number_factor = sdkp.shape_dimension_number(combined["Neutrino_Flux"].values)
print(f"SD&N Analysis:")
print(f"  Shape Factor (skewness): {shape_factor:.4f}")
print(f"  Dimension (fractal): {dimension:.4f}")
print(f"  Number Factor: {number_factor}")

# QCC Analysis
coherence_index, entanglement_prob = sdkp.quantum_computerization_consciousness(
    combined["Neutrino_Flux"].values, combined["Total_Flare_Intensity"].values
)
print(f"\nQCC Analysis:")
print(f"  Coherence Index: {coherence_index:.4f}")
print(f"  Entanglement Probability: {entanglement_prob:.4f}")

# SDVR Analysis
sdvr_results = sdkp.sdvr_analysis(combined["Neutrino_Flux"].values, combined.index)
print(f"\nSDVR Analysis:")
print(f"  Temporal Dimension: {sdvr_results['dimension']}")
print(f"  Dominant Frequencies: {sdvr_results['rotation_frequencies'][:3]}")

# Enhanced Correlation Analysis
correlations = combined.corr()

# Create enhanced visualization
fig, axes = plt.subplots(3, 3, figsize=(20, 16))
fig.suptitle("Enhanced Solar-Neutrino Analysis with SDKP Framework", fontsize=16)

# Plot 1: Time Series with EOS correction
ax1 = axes[0, 0]
ax1_twin = ax1.twinx()
ax1.plot(combined.index, combined["Neutrino_Flux"], label="Original Flux", alpha=0.7, color='blue')
ax1.plot(combined.index, combined["Neutrino_Flux_EOS_Corrected"], label="EOS Corrected", alpha=0.9, color='navy')
ax1_twin.plot(combined.index, combined["Total_Flare_Intensity"], label="Solar Flares", alpha=0.7, color='orange')
ax1.set_title("EOS-Corrected Neutrino Flux vs Solar Activity")
ax1.legend(loc='upper left')
ax1_twin.legend(loc='upper right')

# Plot 2: Enhanced Correlation Heatmap
ax2 = axes[0, 1]
sns.heatmap(correlations, annot=True, fmt=".3f", cmap="coolwarm", center=0, ax=ax2)
ax2.set_title("Enhanced Correlation Matrix")

# Plot 3: SDKP Metric vs Time
ax3 = axes[0, 2]
sdkp_metrics = []
for i in range(len(combined)):
    size = combined["Neutrino_Flux"].iloc[i]
    density = combined["Total_Flare_Intensity"].iloc[i] + 1  # Avoid zero
    kinetic = combined["Max_Flare_Intensity"].iloc[i] + 1   # Avoid zero
    sdkp_metric = sdkp.calculate_sdkp_metric(size, density, kinetic)
    sdkp_metrics.append(sdkp_metric)

ax3.plot(combined.index, sdkp_metrics, color='purple', alpha=0.7)
ax3.set_title("SDKP Metric Over Time")
ax3.set_ylabel("SDKP Value")

# Plot 4: Quantum Coherence Analysis
ax4 = axes[1, 0]
rolling_coherence = []
window = 30
for i in range(window, len(combined)):
    flux_window = combined["Neutrino_Flux"].iloc[i-window:i].values
    flare_window = combined["Total_Flare_Intensity"].iloc[i-window:i].values
    coherence, _ = sdkp.quantum_computerization_consciousness(flux_window, flare_window)
    rolling_coherence.append(coherence)

ax4.plot(combined.index[window:], rolling_coherence, color='green', alpha=0.7)
ax4.axhline(y=sdkp.quantum_coherence_threshold, color='red', linestyle='--', alpha=0.5)
ax4.set_title("Rolling Quantum Coherence Index")
ax4.set_ylabel("Coherence")

# Plot 5: SDVR Velocity Profile
ax5 = axes[1, 1]
velocity_profile = sdvr_results['velocity_profile']
ax5.plot(combined.index, velocity_profile, color='red', alpha=0.7)
ax5.set_title("SDVR Velocity Profile")
ax5.set_ylabel("Rate of Change")

# Plot 6: Frequency Analysis
ax6 = axes[1, 2]
freqs = np.fft.fftfreq(len(combined), d=1)
fft_flux = np.abs(np.fft.fft(combined["Neutrino_Flux"]))
fft_flare = np.abs(np.fft.fft(combined["Total_Flare_Intensity"]))

mask = freqs > 0
ax6.semilogy(freqs[mask], fft_flux[mask], label="Neutrino Flux", alpha=0.7)
ax6.semilogy(freqs[mask], fft_flare[mask], label="Solar Flares", alpha=0.7)
ax6.set_title("Frequency Domain Analysis")
ax6.set_xlabel("Frequency (1/day)")
ax6.legend()

# Plot 7: Phase Space Analysis
ax7 = axes[2, 0]
flux_delayed = np.roll(combined["Neutrino_Flux"], -1)
ax7.scatter(combined["Neutrino_Flux"], flux_delayed, alpha=0.5, s=10)
ax7.set_title("Phase Space Reconstruction")
ax7.set_xlabel("Flux(t)")
ax7.set_ylabel("Flux(t+1)")

# Plot 8: Entanglement Probability Distribution
ax8 = axes[2, 1]
entanglement_probs = []
for i in range(30, len(combined)):
    flux_window = combined["Neutrino_Flux"].iloc[i-30:i].values
    flare_window = combined["Total_Flare_Intensity"].iloc[i-30:i].values
    _, entanglement = sdkp.quantum_computerization_consciousness(flux_window, flare_window)
    entanglement_probs.append(entanglement)

ax8.hist(entanglement_probs, bins=30, alpha=0.7, density=True)
ax8.set_title("Entanglement Probability Distribution")
ax8.set_xlabel("Entanglement Probability")
ax8.set_ylabel("Density")

# Plot 9: EOS Correction Factor
ax9 = axes[2, 2]
ax9.plot(combined.index, eos_corrections, color='brown', alpha=0.7)
ax9.set_title("EOS Correction Factor")
ax9.set_ylabel("Correction Factor")

plt.tight_layout()
plt.show()

# Enhanced Statistical Analysis
print("\n=== ENHANCED CORRELATIONS ===")
for col in ["Total_Flare_Intensity", "Max_Flare_Intensity", "Flare_Count", "Intensity_Std"]:
    original_corr = correlations["Neutrino_Flux"][col]
    eos_corr = correlations["Neutrino_Flux_EOS_Corrected"][col]
    print(f"Neutrino vs {col}:")
    print(f"  Original: {original_corr:.4f}")
    print(f"  EOS Corrected: {eos_corr:.4f}")
    print(f"  Improvement: {abs(eos_corr) - abs(original_corr):.4f}")

# Advanced Time Lag Analysis with SDKP
print("\n=== SDKP-ENHANCED TIME LAG ANALYSIS ===")
max_lag = 30
lag_correlations = []

for lag in range(1, max_lag + 1):
    corr_original = combined["Neutrino_Flux"].corr(combined["Total_Flare_Intensity"].shift(lag))
    corr_eos = combined["Neutrino_Flux_EOS_Corrected"].corr(combined["Total_Flare_Intensity"].shift(lag))
    lag_correlations.append((lag, corr_original, corr_eos))
    
    if lag <= 10:
        print(f"Lag {lag}d: Original={corr_original:.4f}, EOS={corr_eos:.4f}")

# Find optimal lag
best_lag = max(lag_correlations, key=lambda x: abs(x[2]))
print(f"\nOptimal lag: {best_lag[0]} days (correlation: {best_lag[2]:.4f})")

# SDKP Summary Statistics
print("\n=== SDKP FRAMEWORK SUMMARY ===")
print(f"SDKP Metric Range: {min(sdkp_metrics):.2e} to {max(sdkp_metrics):.2e}")
print(f"Average Quantum Coherence: {np.mean(rolling_coherence):.4f}")
print(f"Peak Entanglement Probability: {max(entanglement_probs):.4f}")
print(f"Temporal Complexity (SDVR): {sdvr_results['dimension']}")

return combined, correlations, sdkp_metrics

print("\n=== ENHANCED SUMMARY ===")
print("Analysis completed using SDKP framework principles.")
print("Citation: Smith, D. P. (2025). SDKP Framework: A Unified Principle for")
print("Emergent Mass, Time, and Quantum Coherence. Zenodo.")
print("https://doi.org/10.5281/zenodo.14850016")
print("\nFramework components utilized:")
print("- SDKP (Size-Density-Kinetic Principle)")
print("- SD&N (Shape-Dimension-Number)")
print("- QCC (Quantum Computerization Consciousness)")
print("- EOS (Earth Orbital Speed)")
print("- SDVR (Shape-Dimension-Velocity Rotation)")

print(f"\nData shape: {combined_data.shape}")
print(combined_data.describe())

def __init__(self):
    self.quantum_coherence_threshold = 0.85
    self.entanglement_thresholds = {
        'weak': 0.1,
        'moderate': 0.3,
        'strong': 0.5,
        'maximal': 0.8
    }

def quantum_computerization_consciousness(self, flux1, flux2):
    """
    Enhanced QCC analysis with multiple entanglement metrics
    """
    # Ensure arrays are same length and handle NaN values
    min_len = min(len(flux1), len(flux2))
    flux1_clean = flux1[:min_len]
    flux2_clean = flux2[:min_len]
    
    # Remove NaN values
    mask = ~(np.isnan(flux1_clean) | np.isnan(flux2_clean))
    flux1_clean = flux1_clean[mask]
    flux2_clean = flux2_clean[mask]
    
    if len(flux1_clean) < 2:
        return 0.0, 0.0
    
    # Cross-correlation analysis
    cross_corr = np.correlate(flux1_clean, flux2_clean, mode='full')
    coherence_index = np.max(cross_corr) / (np.linalg.norm(flux1_clean) * np.linalg.norm(flux2_clean))
    
    # Quantum entanglement probability
    correlation = np.corrcoef(flux1_clean, flux2_clean)[0, 1]
    entanglement_probability = np.abs(correlation) ** 2
    
    return coherence_index, entanglement_probability

def advanced_entanglement_metrics(self, flux1, flux2):
    """
    Calculate advanced entanglement metrics
    """
    # Ensure arrays are same length and clean
    min_len = min(len(flux1), len(flux2))
    flux1_clean = flux1[:min_len]
    flux2_clean = flux2[:min_len]
    
    mask = ~(np.isnan(flux1_clean) | np.isnan(flux2_clean))
    flux1_clean = flux1_clean[mask]
    flux2_clean = flux2_clean[mask]
    
    if len(flux1_clean) < 2:
        return {'correlation': 0, 'mutual_info': 0, 'phase_sync': 0, 'coherence': 0}
    
    # Pearson correlation
    correlation = np.corrcoef(flux1_clean, flux2_clean)[0, 1]
    
    # Mutual information (simplified)
    mutual_info = self._calculate_mutual_information(flux1_clean, flux2_clean)
    
    # Phase synchronization
    phase_sync = self._calculate_phase_synchronization(flux1_clean, flux2_clean)
    
    # Quantum coherence
    coherence = self._calculate_quantum_coherence(flux1_clean, flux2_clean)
    
    return {
        'correlation': correlation,
        'mutual_info': mutual_info,
        'phase_sync': phase_sync,
        'coherence': coherence
    }

def _calculate_mutual_information(self, x, y):
    """Calculate mutual information between two signals"""
    # Discretize signals
    x_discrete = np.digitize(x, bins=np.percentile(x, [25, 50, 75]))
    y_discrete = np.digitize(y, bins=np.percentile(y, [25, 50, 75]))
    
    # Calculate joint and marginal probabilities
    joint_prob = np.histogram2d(x_discrete, y_discrete, bins=4)[0]
    joint_prob = joint_prob / np.sum(joint_prob)
    
    marginal_x = np.sum(joint_prob, axis=1)
    marginal_y = np.sum(joint_prob, axis=0)
    
    # Calculate mutual information
    mi = 0
    for i in range(len(marginal_x)):
        for j in range(len(marginal_y)):
            if joint_prob[i, j] > 0:
                mi += joint_prob[i, j] * np.log2(joint_prob[i, j] / (marginal_x[i] * marginal_y[j]))
    
    return mi

def _calculate_phase_synchronization(self, x, y):
    """Calculate phase synchronization using Hilbert transform"""
    # Hilbert transform to get instantaneous phase
    analytic_x = signal.hilbert(x)
    analytic_y = signal.hilbert(y)
    
    phase_x = np.angle(analytic_x)
    phase_y = np.angle(analytic_y)
    
    # Phase difference
    phase_diff = phase_x - phase_y
    
    # Synchronization index
    sync_index = np.abs(np.mean(np.exp(1j * phase_diff)))
    
    return sync_index

def _calculate_quantum_coherence(self, x, y):
    """Calculate quantum coherence measure"""
    # Normalize signals
    x_norm = (x - np.mean(x)) / np.std(x)
    y_norm = (y - np.mean(y)) / np.std(y)
    
    # Quantum coherence as normalized cross-correlation
    coherence = np.max(np.abs(np.correlate(x_norm, y_norm, mode='full'))) / len(x_norm)
    
    return coherence

# Base patterns
base_flux = 1000 + 100 * np.sin(2 * np.pi * np.arange(len(dates)) / 365.25)
solar_cycle = 50 * np.sin(2 * np.pi * np.arange(len(dates)) / (11 * 365.25))

# Add time-lagged relationships
lag_5_component = 30 * np.sin(2 * np.pi * np.arange(len(dates)) / 27.3)  # 27-day solar rotation
lag_10_component = 20 * np.sin(2 * np.pi * np.arange(len(dates)) / 14.0)  # 14-day cycle

# Neutrino flux with entangled components
neutrino_flux = base_flux + solar_cycle + np.random.normal(0, 30, len(dates))
neutrino_flux += np.roll(lag_5_component, -5)  # 5-day lag
neutrino_flux += np.roll(lag_10_component, -10)  # 10-day lag

# Solar flare intensity with base patterns
flare_intensity = []
for i in range(len(dates)):
    base_intensity = 5 + 3 * np.sin(2 * np.pi * i / 27.3)
    if np.random.random() < 0.3:
        flare_intensity.append(base_intensity + np.random.exponential(2))
    else:
        flare_intensity.append(base_intensity * 0.1)

# Create DataFrames
neutrino_data = pd.DataFrame({"flux": neutrino_flux}, index=dates)
flare_data = pd.DataFrame({"total_intensity": flare_intensity}, index=dates)

# EOS correction
eos_correction = 1 + 0.033 * np.cos(2 * np.pi * np.arange(len(dates)) / 365.25)
neutrino_data["flux_eos_corrected"] = neutrino_data["flux"] * eos_correction

return neutrino_data, flare_data

# Initialize analyzer
analyzer = QuantumEntanglementAnalyzer()

# Generate or load data
neutrino_data, flare_data = generate_enhanced_synthetic_data()

# Combine data
combined = pd.concat([
    neutrino_data["flux"],
    neutrino_data["flux_eos_corrected"],
    flare_data["total_intensity"]
], axis=1)
combined.columns = ["Neutrino_Flux", "Neutrino_Flux_EOS_Corrected", "Total_Flare_Intensity"]
combined = combined.fillna(0)

# Define analysis parameters
max_lag = 30
window_sizes = [7, 14, 21, 30]  # Different time windows for analysis
lags = np.arange(1, max_lag + 1)

# A. Basic Time-Lagged Entanglement Analysis
print("Computing basic time-lagged entanglement matrix...")
entanglement_matrix = np.zeros(len(lags))
coherence_matrix = np.zeros(len(lags))

for i, lag in enumerate(lags):
    shifted_flare = combined["Total_Flare_Intensity"].shift(lag).dropna()
    aligned_flux = combined["Neutrino_Flux_EOS_Corrected"].iloc[lag:]
    
    # Ensure same length
    min_len = min(len(shifted_flare), len(aligned_flux))
    shifted_flare = shifted_flare.iloc[:min_len]
    aligned_flux = aligned_flux.iloc[:min_len]
    
    coherence, entanglement = analyzer.quantum_computerization_consciousness(
        aligned_flux.values, shifted_flare.values
    )
    
    entanglement_matrix[i] = entanglement
    coherence_matrix[i] = coherence

# B. Advanced Multi-Window Entanglement Analysis
print("Computing multi-window entanglement analysis...")
multi_window_matrix = np.zeros((len(window_sizes), len(lags)))

for w_idx, window in enumerate(window_sizes):
    for l_idx, lag in enumerate(lags):
        entanglements = []
        
        # Rolling window analysis
        for start in range(0, len(combined) - window - lag, window // 2):
            end = start + window
            
            flux_window = combined["Neutrino_Flux_EOS_Corrected"].iloc[start:end]
            flare_window = combined["Total_Flare_Intensity"].iloc[start+lag:end+lag]
            
            if len(flux_window) == len(flare_window) and len(flux_window) > 1:
                _, ent = analyzer.quantum_computerization_consciousness(
                    flux_window.values, flare_window.values
                )
                entanglements.append(ent)
        
        multi_window_matrix[w_idx, l_idx] = np.mean(entanglements) if entanglements else 0

# C. Advanced Metrics Heatmap
print("Computing advanced metrics heatmap...")
metrics_names = ['correlation', 'mutual_info', 'phase_sync', 'coherence']
advanced_metrics_matrix = np.zeros((len(metrics_names), len(lags)))

for l_idx, lag in enumerate(lags):
    shifted_flare = combined["Total_Flare_Intensity"].shift(lag).dropna()
    aligned_flux = combined["Neutrino_Flux_EOS_Corrected"].iloc[lag:]
    
    min_len = min(len(shifted_flare), len(aligned_flux))
    shifted_flare = shifted_flare.iloc[:min_len]
    aligned_flux = aligned_flux.iloc[:min_len]
    
    metrics = analyzer.advanced_entanglement_metrics(
        aligned_flux.values, shifted_flare.values
    )
    
    for m_idx, metric in enumerate(metrics_names):
        advanced_metrics_matrix[m_idx, l_idx] = metrics[metric]

# Create comprehensive visualization
fig, axes = plt.subplots(2, 3, figsize=(20, 12))
fig.suptitle('Time-Lagged Entanglement Analysis using SDKP Framework', fontsize=16)

# Plot 1: Basic Entanglement vs Lag
ax1 = axes[0, 0]
ax1.plot(lags, entanglement_matrix, 'b-', linewidth=2, label='Entanglement')
ax1.plot(lags, coherence_matrix, 'r--', linewidth=2, label='Coherence')
ax1.axhline(y=analyzer.entanglement_thresholds['weak'], color='gray', linestyle=':', alpha=0.5)
ax1.axhline(y=analyzer.entanglement_thresholds['moderate'], color='orange', linestyle=':', alpha=0.5)
ax1.axhline(y=analyzer.entanglement_thresholds['strong'], color='red', linestyle=':', alpha=0.5)
ax1.set_xlabel('Lag (days)')
ax1.set_ylabel('Entanglement/Coherence')
ax1.set_title('Basic Time-Lagged Entanglement')
ax1.legend()
ax1.grid(True, alpha=0.3)

# Plot 2: Multi-Window Heatmap
ax2 = axes[0, 1]
im2 = ax2.imshow(multi_window_matrix, aspect='auto', cmap='viridis', 
                 extent=[lags[0], lags[-1], window_sizes[0], window_sizes[-1]])
ax2.set_xlabel('Lag (days)')
ax2.set_ylabel('Window Size (days)')
ax2.set_title('Multi-Window Entanglement Heatmap')
plt.colorbar(im2, ax=ax2, label='Entanglement Probability')

# Plot 3: Advanced Metrics Heatmap
ax3 = axes[0, 2]
im3 = ax3.imshow(advanced_metrics_matrix, aspect='auto', cmap='RdYlBu_r',
                 extent=[lags[0], lags[-1], 0, len(metrics_names)])
ax3.set_xlabel('Lag (days)')
ax3.set_ylabel('Metric Type')
ax3.set_yticks(range(len(metrics_names)))
ax3.set_yticklabels(metrics_names)
ax3.set_title('Advanced Entanglement Metrics')
plt.colorbar(im3, ax=ax3, label='Metric Value')

# Plot 4: Entanglement Distribution
ax4 = axes[1, 0]
ax4.hist(entanglement_matrix, bins=15, alpha=0.7, density=True, label='Entanglement')
ax4.hist(coherence_matrix, bins=15, alpha=0.7, density=True, label='Coherence')
ax4.axvline(x=analyzer.entanglement_thresholds['moderate'], color='orange', linestyle='--', alpha=0.7)
ax4.set_xlabel('Entanglement/Coherence Value')
ax4.set_ylabel('Density')
ax4.set_title('Entanglement Distribution')
ax4.legend()
ax4.grid(True, alpha=0.3)

# Plot 5: Peak Entanglement Analysis
ax5 = axes[1, 1]
# Find peaks in entanglement
peaks, properties = signal.find_peaks(entanglement_matrix, height=0.1, distance=2)
ax5.plot(lags, entanglement_matrix, 'b-', linewidth=2)
ax5.scatter(lags[peaks], entanglement_matrix[peaks], color='red', s=100, zorder=5)

# Annotate peaks
for peak_idx in peaks:
    ax5.annotate(f'{lags[peak_idx]}d\n{entanglement_matrix[peak_idx]:.3f}',
                xy=(lags[peak_idx], entanglement_matrix[peak_idx]),
                xytext=(10, 10), textcoords='offset points',
                bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.7),
                arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))

ax5.set_xlabel('Lag (days)')
ax5.set_ylabel('Entanglement Probability')
ax5.set_title('Peak Entanglement Detection')
ax5.grid(True, alpha=0.3)

# Plot 6: Time-Frequency Analysis
ax6 = axes[1, 2]
# Create time-frequency representation
frequencies = np.fft.fftfreq(len(lags), d=1)
fft_entanglement = np.abs(np.fft.fft(entanglement_matrix))

mask = frequencies > 0
ax6.semilogy(frequencies[mask], fft_entanglement[mask], 'g-', linewidth=2)
ax6.set_xlabel('Frequency (1/day)')
ax6.set_ylabel('Power Spectral Density')
ax6.set_title('Entanglement Frequency Analysis')
ax6.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

# Analysis Summary
print("\n=== ENTANGLEMENT ANALYSIS SUMMARY ===")

# Find optimal lags
max_entanglement_idx = np.argmax(entanglement_matrix)
max_coherence_idx = np.argmax(coherence_matrix)

print(f"Maximum Entanglement: {entanglement_matrix[max_entanglement_idx]:.4f} at lag {lags[max_entanglement_idx]} days")
print(f"Maximum Coherence: {coherence_matrix[max_coherence_idx]:.4f} at lag {lags[max_coherence_idx]} days")

# Entanglement classification
strong_entanglement_lags = lags[entanglement_matrix > analyzer.entanglement_thresholds['strong']]
moderate_entanglement_lags = lags[(entanglement_matrix > analyzer.entanglement_thresholds['moderate']) & 
                                (entanglement_matrix <= analyzer.entanglement_thresholds['strong'])]

print(f"\nStrong Entanglement Lags: {strong_entanglement_lags}")
print(f"Moderate Entanglement Lags: {moderate_entanglement_lags}")

# Peak analysis
peaks, _ = signal.find_peaks(entanglement_matrix, height=0.1, distance=2)
print(f"\nDetected Entanglement Peaks at lags: {lags[peaks]} days")
print(f"Peak values: {entanglement_matrix[peaks]}")

# Advanced metrics summary
print(f"\n=== ADVANCED METRICS SUMMARY ===")
for i, metric in enumerate(metrics_names):
    best_lag_idx = np.argmax(np.abs(advanced_metrics_matrix[i, :]))
    print(f"{metric.capitalize()}: {advanced_metrics_matrix[i, best_lag_idx]:.4f} at lag {lags[best_lag_idx]} days")

# Multi-window analysis
print(f"\n=== MULTI-WINDOW ANALYSIS ===")
best_window_lag = np.unravel_index(np.argmax(multi_window_matrix), multi_window_matrix.shape)
print(f"Optimal window-lag combination: {window_sizes[best_window_lag[0]]} days window, {lags[best_window_lag[1]]} days lag")
print(f"Maximum entanglement: {multi_window_matrix[best_window_lag]:.4f}")

return {
    'lags': lags,
    'entanglement_matrix': entanglement_matrix,
    'coherence_matrix': coherence_matrix,
    'multi_window_matrix': multi_window_matrix,
    'advanced_metrics_matrix': advanced_metrics_matrix,
    'window_sizes': window_sizes,
    'metrics_names': metrics_names
}

print("\n=== SDKP FRAMEWORK VALIDATION ===")
print("Framework components utilized:")
print("- QCC (Quantum Computerization Consciousness)")
print("- SDKP time-lag analysis")
print("- EOS (Earth Orbital Speed) corrections")
print("- Multi-dimensional entanglement metrics")
print("\nCitation: Smith, D. P. (2025). SDKP Framework: A Unified Principle for")
print("Emergent Mass, Time, and Quantum Coherence. Zenodo.")
print("https://doi.org/10.5281/zenodo.14850016")

    # Remove outliers (beyond 3 standard deviations)
    z_scores = np.abs(stats.zscore(neutrino_data["flux"]))
    neutrino_data = neutrino_data[z_scores < 3]
    
    # Resample to daily averages
    neutrino_data = neutrino_data.resample("D").mean()
    return neutrino_data

except FileNotFoundError:
    print(f"Warning: {filepath} not found. Generating synthetic data for demonstration.")
    return generate_synthetic_neutrino_data()

    # Enhanced flare intensity mapping
    flare_mapping = {
        "A": 0.1, "B": 0.5, "C": 1, "M": 10, "X": 100
    }
    
    solar_flares["flare_intensity"] = solar_flares["class"].map(flare_mapping)
    solar_flares.set_index("date_time", inplace=True)
    
    # Daily aggregation with multiple metrics
    flare_daily = solar_flares.resample("D").agg({
        "flare_intensity": ["sum", "max", "count"]
    }).round(2)
    
    # Flatten column names
    flare_daily.columns = ["total_intensity", "max_intensity", "flare_count"]
    return flare_daily
    
except FileNotFoundError:
    print(f"Warning: {filepath} not found. Generating synthetic data for demonstration.")
    return generate_synthetic_flare_data()

# Base flux with seasonal variation and random noise
base_flux = 1000 + 100 * np.sin(2 * np.pi * np.arange(len(dates)) / 365.25)
noise = np.random.normal(0, 50, len(dates))

# Add some correlation with solar activity (11-year cycle approximation)
solar_cycle = 50 * np.sin(2 * np.pi * np.arange(len(dates)) / (11 * 365.25))

flux = base_flux + noise + solar_cycle

return pd.DataFrame({"flux": flux}, index=dates)

# Random flare occurrences with intensity
np.random.seed(42)  # For reproducibility
flare_prob = 0.3  # 30% chance of flare per day

total_intensity = []
max_intensity = []
flare_count = []

for _ in dates:
    if np.random.random() < flare_prob:
        # Generate 1-5 flares per active day
        n_flares = np.random.randint(1, 6)
        intensities = np.random.exponential(2, n_flares)  # Exponential distribution
        
        total_intensity.append(np.sum(intensities))
        max_intensity.append(np.max(intensities))
        flare_count.append(n_flares)
    else:
        total_intensity.append(0)
        max_intensity.append(0)
        flare_count.append(0)

return pd.DataFrame({
    "total_intensity": total_intensity,
    "max_intensity": max_intensity,
    "flare_count": flare_count
}, index=dates)

# Load data
neutrino_data = load_neutrino_data("neutrino_flux.csv")
solar_flares = load_solar_flare_data("solar_flares.csv")

# Combine datasets
combined = pd.concat([
    neutrino_data["flux"], 
    solar_flares["total_intensity"],
    solar_flares["max_intensity"],
    solar_flares["flare_count"]
], axis=1)

combined.columns = ["Neutrino_Flux", "Total_Flare_Intensity", "Max_Flare_Intensity", "Flare_Count"]
combined = combined.fillna(0)

# Calculate correlations
correlations = combined.corr()

# Create comprehensive visualization
fig, axes = plt.subplots(2, 2, figsize=(16, 12))

# 1. Time series comparison
ax1 = axes[0, 0]
ax1_twin = ax1.twinx()

ax1.plot(combined.index, combined["Neutrino_Flux"], 
         label="Neutrino Flux", color="blue", alpha=0.7)
ax1_twin.plot(combined.index, combined["Total_Flare_Intensity"], 
              label="Solar Flare Intensity", color="orange", alpha=0.7)

ax1.set_xlabel("Date")
ax1.set_ylabel("Neutrino Flux", color="blue")
ax1_twin.set_ylabel("Solar Flare Intensity", color="orange")
ax1.set_title("Solar Flare Activity vs Neutrino Flux Over Time")
ax1.grid(True, alpha=0.3)

# 2. Correlation heatmap
ax2 = axes[0, 1]
sns.heatmap(correlations, annot=True, cmap="coolwarm", center=0, 
            ax=ax2, square=True, fmt=".3f")
ax2.set_title("Correlation Matrix")

# 3. Scatter plot with regression
ax3 = axes[1, 0]
ax3.scatter(combined["Total_Flare_Intensity"], combined["Neutrino_Flux"], 
            alpha=0.5, s=20)

# Add regression line
if len(combined) > 1:
    slope, intercept, r_value, p_value, std_err = stats.linregress(
        combined["Total_Flare_Intensity"], combined["Neutrino_Flux"]
    )
    line = slope * combined["Total_Flare_Intensity"] + intercept
    ax3.plot(combined["Total_Flare_Intensity"], line, 'r-', 
             label=f'R² = {r_value**2:.3f}, p = {p_value:.3f}')

ax3.set_xlabel("Total Flare Intensity")
ax3.set_ylabel("Neutrino Flux")
ax3.set_title("Neutrino Flux vs Solar Flare Intensity")
ax3.legend()
ax3.grid(True, alpha=0.3)

# 4. Moving average correlation
ax4 = axes[1, 1]

# Calculate 30-day moving averages
combined_ma = combined.rolling(window=30).mean()

ax4.plot(combined_ma.index, combined_ma["Neutrino_Flux"], 
         label="Neutrino Flux (30-day MA)", color="blue")
ax4_twin = ax4.twinx()
ax4_twin.plot(combined_ma.index, combined_ma["Total_Flare_Intensity"], 
              label="Solar Flare Intensity (30-day MA)", color="orange")

ax4.set_xlabel("Date")
ax4.set_ylabel("Neutrino Flux", color="blue")
ax4_twin.set_ylabel("Solar Flare Intensity", color="orange")
ax4.set_title("30-Day Moving Averages")
ax4.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

# Statistical analysis
print("=== STATISTICAL ANALYSIS ===")
print(f"Dataset size: {len(combined)} days")
print(f"Date range: {combined.index.min()} to {combined.index.max()}")
print()

print("Correlation coefficients:")
neutrino_correlations = correlations["Neutrino_Flux"].drop("Neutrino_Flux")
for var, corr in neutrino_correlations.items():
    print(f"  Neutrino Flux vs {var}: {corr:.4f}")

print()

# Time lag analysis
print("=== TIME LAG ANALYSIS ===")
max_lag = 10  # Check up to 10 days

for lag in range(1, max_lag + 1):
    lagged_corr = combined["Neutrino_Flux"].corr(
        combined["Total_Flare_Intensity"].shift(lag)
    )
    print(f"Lag {lag} days: correlation = {lagged_corr:.4f}")

return combined, correlations

print("\n=== SUMMARY STATISTICS ===")
print(combined_data.describe())

# Enhanced title with framework attribution
ax.set_title("3D Quantum Entanglement Simulator\nSDKP Framework & QCC Integration", 
            color='white', fontsize=14, pad=20)
ax.set_facecolor("black")

# Set axis colors
ax.xaxis.label.set_color('white')
ax.yaxis.label.set_color('white')
ax.zaxis.label.set_color('white')
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', colors='white')
ax.tick_params(axis='z', colors='white')

# Main simulation loop
for i in range(pair_count):
    # Update positions with boundary reflection
    positions[i] += directions[i]
    for j in range(3):
        if positions[i, j] < 0 or positions[i, j] > 12:
            directions[i, j] *= -1
    
    # Calculate primary position
    x1, y1, z1 = positions[i]
    
    # Apply SDKP flow calculation
    T = sdkp_flow(sizes[i], densities[i], kinetics[i])
    coherence = calculate_quantum_coherence(sizes[i], densities[i], kinetics[i], frame)
    
    # Calculate entangled pair position with enhanced dynamics
    angle = frame / 25.0 + i
    phase_shift = np.pi/4 * (i + 1)
    
    x2 = x1 + np.cos(angle) * T * 0.1 * coherence * 0.1
    y2 = y1 + np.sin(angle) * T * 0.1 * coherence * 0.1
    z2 = z1 + np.cos(angle + phase_shift) * T * 0.1 * coherence * 0.1
    
    # Assign roles and codes
    roleA = roles[i % 4]
    roleB = roles[(i + 2) % 4]
    codeA = codes[i % 4]
    codeB = codes[(i + 2) % 4]
    colorA = colors[roleA]
    colorB = colors[roleB]
    
    # Apply QCC entropy modulation
    entropy_mod = qcc_entropy_modulation(fidelities[i], codeA)
    vib_index = vei(codeA, codeB)
    effective_fid = np.clip(entropy_mod * (1 - vib_index), 0, 1)
    
    # Draw entanglement connection with dynamic properties
    connection_width = 1 + 3 * effective_fid
    connection_alpha = 0.7 * effective_fid
    
    ax.plot([x1, x2], [y1, y2], [z1, z2], 
            color='cyan', linewidth=connection_width, alpha=connection_alpha)
    
    # Draw particles with size based on SDKP parameters
    particle_size = 50 * (sizes[i] / 3.0)
    ax.scatter(x1, y1, z1, color=colorA, s=particle_size, alpha=0.8)
    ax.scatter(x2, y2, z2, color=colorB, s=particle_size, alpha=0.8)
    
    # Add labels with enhanced information
    ax.text(x1, y1, z1 + 0.3, f"{roleA}\n({codeA})", 
            color='white', fontsize=8, ha='center')
    ax.text(x2, y2, z2 + 0.3, f"{roleB}\n({codeB})", 
            color='white', fontsize=8, ha='center')
    
    # Special effects for high fidelity connections
    if effective_fid > 0.97:
        # High coherence - golden connection
        ax.plot([x1, x2], [y1, y2], [z1, z2], 
                color='gold', linewidth=3, alpha=0.9)
        # Add pulsing effect
        pulse_size = 20 * (1 + 0.5 * np.sin(frame * 0.3))
        ax.scatter((x1+x2)/2, (y1+y2)/2, (z1+z2)/2, 
                  color='gold', s=pulse_size, alpha=0.6)
    elif effective_fid > 0.85:
        # Medium coherence - magenta connection
        ax.plot([x1, x2], [y1, y2], [z1, z2], 
                color='magenta', linewidth=2, alpha=0.7)

# Add informational text
info_text = f"Frame: {frame}\nSDKP Flow Active\nQCC Modulation: ON"
ax.text2D(0.02, 0.98, info_text, transform=ax.transAxes, 
          color='white', fontsize=10, verticalalignment='top',
          bbox=dict(boxstyle="round,pad=0.3", facecolor='black', alpha=0.7))

# Add framework attribution
attribution_text = "Based on SDKP Framework & QCC Principles\nDonald Paul Smith (2025)"
ax.text2D(0.02, 0.02, attribution_text, transform=ax.transAxes, 
          color='gray', fontsize=8, verticalalignment='bottom',
          bbox=dict(boxstyle="round,pad=0.3", facecolor='black', alpha=0.5))