KOSCHEL FORMULA

import math
import hashlib
import time
import struct
from typing import List, Dict, Optional, Tuple
from enum import Enum


class MiningPerformanceLevel(Enum):
    OPTIMIZED = "optimized"
    QUANTUM_BOOST = "quantum_boost"  
    MAXIMUM_POWER = "maximum_power"
    ZERO_ENTROPY = "zero_entropy"     


class SelfHealingQuantumMiner:
    """
    Self-healing geometric mining prototype with Tesla 3-6-9 resonance.
    - Hexagonal symmetry healing + phase-aware nonagon (9) healing
    - Harmonic (3/6) angular/radial modulation with phase gains
    - Golden-ratio scaling, Fibonacci-adjacent checks
    - Entropy-state metric via digital root (1..9) + explicit 9-cycle reinforcement
    - Triangular layering and 9-step cycle resets
    """

    def __init__(self, performance_level: MiningPerformanceLevel = MiningPerformanceLevel.MAXIMUM_POWER):
        self.performance_level = performance_level
        self.phi = (1 + math.sqrt(5)) / 2
        self.dna_ratio = 34 / 21  # historical constant; used here as a fixed ratio

        # Modulation params
        self.angular_modulation = 0.15
        self.radial_breathing = 0.08

        # Entropy metric
        self.entropy_state = 9
        self.consecutive_9_cycles = 0

        # Self-healing params
        self.healing_tolerance = 0.01
        self.healing_force_multiplier = 1.0
        self.healing_cycles = 0

        # Performance / learning
        self.optimal_batch_size = 4096
        self.learning_rate = 0.05
        self.evolution_cycle = 0
        self.performance_multiplier = 1.0

        # Runtime stats
        self.success_patterns: List[Dict] = []
        self.failed_ranges = set()
        self.hash_rate_history: List[float] = []

        print(f"🛠️  Geometric Miner Initialized: {performance_level.value}")

    # ---------- Tesla 3-6-9 phase core ----------

    def _tesla_phase(self) -> int:
        """
        Deterministic 3→6→9 cycle using evolution_cycle.
        Returns 3, 6, or 9.
        """
        phase_index = self.evolution_cycle % 3
        return [3, 6, 9][phase_index]

    def _secure_phase_seed(self, index: int, position: int, layer: int) -> int:
        """
        Deterministic seed based on local parameters; avoids external randomness.
        """
        payload = f"{index}:{position}:{layer}:{self.evolution_cycle}".encode("utf-8")
        digest = hashlib.sha256(payload).digest()
        return struct.unpack("<Q", digest[:8])[0]

    def _phase_gain(self, phase: int) -> Tuple[float, float, float]:
        """
        Returns (angular_gain, radial_gain, entropy_gain) for the active phase.
        Gains are conservative and bounded.
        """
        if phase == 3:
            return (1.15, 1.05, 1.20)
        if phase == 6:
            return (1.05, 1.15, 1.25)
        # phase == 9
        return (1.10, 1.10, 1.35)

    # ---------- Geometric healing ----------

    def _nonagon_neighbors(self, x: float, y: float, r: float, layer_n: int) -> List[Tuple[float, float]]:
        """
        9-fold symmetry neighbors for phase 9 healing.
        """
        pts = []
        for i in range(9):
            angle = 2 * math.pi * i / 9
            nx = x + r * layer_n * math.cos(angle)
            ny = y + r * layer_n * math.sin(angle)
            pts.append((nx, ny))
        return pts

    def self_heal_point(
        self,
        target_point: Tuple[float, float],
        layer_n: int,
        r: float = 1.0,
        tolerance: float = 0.01
    ) -> Tuple[float, float]:
        """
        Heal a perturbed point using hexagonal symmetry.
        Healed = average of 6 neighbors (simple symmetry-cage relaxation).
        """
        x, y = target_point
        healed = (x, y)

        ideal_neighbors = []
        for i in range(6):
            angle = 2 * math.pi * i / 6
            nx = x + r * layer_n * math.cos(angle)
            ny = y + r * layer_n * math.sin(angle)
            ideal_neighbors.append((nx, ny))

        avg_x = sum(p[0] for p in ideal_neighbors) / 6
        avg_y = sum(p[1] for p in ideal_neighbors) / 6
        vector_healed = (avg_x, avg_y)

        current_error = self._calculate_distance(target_point, vector_healed)
        if current_error > tolerance:
            healed = vector_healed
            self.healing_cycles += 1
            print(f"🔧 Healing applied: err {current_error:.4f} → reduced")

        return healed

    def self_heal_point_phase(
        self,
        target_point: Tuple[float, float],
        layer_n: int,
        r: float = 1.0,
        tolerance: float = 0.01
    ) -> Tuple[float, float]:
        """
        Phase-aware healing: hex (6) by default, nonagon (9) when phase==9.
        """
        phase = self._tesla_phase()
        x, y = target_point

        if phase == 9:
            neighbors = self._nonagon_neighbors(x, y, r, layer_n)
            avg_x = sum(p[0] for p in neighbors) / 9
            avg_y = sum(p[1] for p in neighbors) / 9
        else:
            neighbors = []
            for i in range(6):
                angle = 2 * math.pi * i / 6
                nx = x + r * layer_n * math.cos(angle)
                ny = y + r * layer_n * math.sin(angle)
                neighbors.append((nx, ny))
            avg_x = sum(p[0] for p in neighbors) / 6
            avg_y = sum(p[1] for p in neighbors) / 6

        vector_healed = (avg_x, avg_y)
        current_error = self._calculate_distance(target_point, vector_healed)
        if current_error > tolerance:
            self.healing_cycles += 1
            return vector_healed
        return target_point

    @staticmethod
    def _calculate_distance(p1: Tuple[float, float], p2: Tuple[float, float]) -> float:
        return math.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2)

    # ---------- Phase-aware modulation ----------

    def _apply_tesla_phase_modulation(
        self,
        index: int,
        position: int,
        layer: int,
        base_angle: float,
        angle_mod: float,
        radial_mod: float
    ) -> Tuple[int, float, float]:
        """
        Applies phase gains and a small seeded jitter to prevent degeneracy.
        Returns (phase, modulated_angle, modulated_radial).
        """
        phase = self._tesla_phase()
        ang_gain, rad_gain, _ = self._phase_gain(phase)

        # Deterministic jitter bounded to ±0.005
        seed = self._secure_phase_seed(index, position, layer)
        jitter = ((seed % 1000) / 1000.0 - 0.5) * 0.01

        modulated_angle = (base_angle + angle_mod * ang_gain + jitter)
        modulated_radial = max(-0.45, min(0.45, radial_mod * rad_gain))  # keep breathing stable

        return (phase, modulated_angle, modulated_radial)

    def _phase_entropy_multiplier(self, value: int, phase: int) -> float:
        """
        Rewards candidates whose digital root equals the active phase.
        Conservative bounds to avoid runaway amplification.
        """
        dr = self._calculate_digital_root(value)
        _, _, ent_gain = self._phase_gain(phase)
        if dr == phase:
            return ent_gain
        # Mild cross-resonance boosts
        if phase == 9 and dr in (3, 6):
            return 1.15
        if phase in (3, 6) and dr == 9:
            return 1.10
        return 1.0

    # ---------- Triangular/Fibonacci layering ----------

    def _triangular_layering(self, n: int) -> int:
        """Triangular number recursion: T(n) = n(n+1)/2"""
        return n * (n + 1) // 2

    def _layer_cycle_reset(self, layer: int) -> int:
        """Reset every 9 steps for Tesla resonance"""
        return layer % 9

    # ---------- Nonce generation with modulation + healing ----------

    def generate_self_healing_nonces(
        self,
        base_nonce: int,
        job_id: str,
        prevhash: str,
        target: int,
        batch_multiplier: int = 1
    ) -> List[int]:
        """
        Generate a batch of candidate nonces using harmonic modulation
        and optionally heal poorly-distributed values.
        """
        batch_size = self.optimal_batch_size * batch_multiplier
        nonces: List[int] = []

        power_boost = self._get_power_boost()
        _ = self._get_entropy_reduction()  # reserved

        for i in range(batch_size):
            layer = i % 256
            position = i // 256

            # 3-pulse angular modulation base
            angle_mod = self.angular_modulation * math.sin(3 * position + self._get_phase_optimized())
            base_angle = 2 * math.pi * position / 6

            # 6-rhythm radial breathing base
            radial_mod = self.radial_breathing * math.sin(6 * layer + self._get_phase_optimized())

            # Apply Tesla phase modulation
            phase, modulated_angle, modulated_radial = self._apply_tesla_phase_modulation(
                i, position, layer, base_angle, angle_mod, radial_mod
            )

            # golden-ratio scaling
            golden_boost = self.phi ** ((position + layer) % 8)

            # fixed ratio multiplier (historical)
            ratio_multiplier = 1.0 + (self.dna_ratio - 1.618) * 10

            # heuristic entropy optimization term
            entropy_optimized = self._apply_entropy_optimization(i, position, layer)

            # triangular and 9-step cycle resonance
            tri_layer = self._triangular_layering(max(1, layer))
            cycle_layer = self._layer_cycle_reset(layer)
            # modest, bounded boosts
            entropy_optimized *= 1.0 + (tri_layer % 3) * 0.05
            entropy_optimized *= 1.0 + (1 if cycle_layer == 0 else 0) * 0.10

            raw_power = abs(math.sin(modulated_angle) * layer * (1 + modulated_radial))

            # Phase-aware entropy multiplier (deterministic value from loop params)
            phase_entropy = self._phase_entropy_multiplier(i * position * max(1, layer), phase)

            geometric_value = int(
                raw_power * golden_boost * ratio_multiplier * entropy_optimized * phase_entropy * 1e9 * power_boost
            )

            candidate = (base_nonce + geometric_value) % (2 ** 32)

            # heal if pattern flags suggest poor structure (phase-aware)
            if self._needs_healing(candidate, layer):
                p = self._nonce_to_geometric_point(candidate, layer)
                healed = self.self_heal_point_phase(p, layer, self.phi, self.healing_tolerance)
                candidate = self._geometric_point_to_nonce(healed, layer)

            if candidate not in self.failed_ranges:
                nonces.append(candidate)

        return nonces[:batch_size]

    def _needs_healing(self, nonce: int, layer: int) -> bool:
        # simple heuristics
        nonce_chunk = nonce >> 16
        if nonce_chunk in self.failed_ranges:
            return True

        dr = self._calculate_digital_root(nonce)
        if dr not in [3, 6, 9]:
            return True

        position = nonce % 1000
        if not self._is_fibonacci_optimized(position, layer):
            return True

        return False

    def _nonce_to_geometric_point(self, nonce: int, layer: int) -> Tuple[float, float]:
        angle = (nonce % 360) * math.pi / 180
        radius = (nonce % 1000) / 1000.0 * max(1, layer) * self.phi
        return (radius * math.cos(angle), radius * math.sin(angle))

    def _geometric_point_to_nonce(self, point: Tuple[float, float], layer: int) -> int:
        x, y = point
        angle = math.atan2(y, x)
        radius = math.sqrt(x ** 2 + y ** 2)

        angle_component = int((angle * 180 / math.pi) % 360)
        denom = max(1e-9, (max(1, layer) * self.phi))
        radius_component = int((radius / denom) * 1000) % 1000

        return ((angle_component << 16) | radius_component) % (2 ** 32)

    # ---------- Mining loop (toy demonstration) ----------

    def mine_with_self_healing_power(
        self,
        job_data: Dict,
        target: str,
        extranonce1: str,
        extranonce2_size: int
    ) -> Optional[Dict]:
        """
        Toy demo of header hashing + nonce search with healing.
        Not a complete protocol implementation.
        """
        job_id, prevhash, coinb1, coinb2, merkle_branch, version, nbits, ntime, clean_jobs = job_data

        extranonce2 = struct.pack('<Q', 0)[:extranonce2_size]
        coinbase = (coinb1 + extranonce1 + extranonce2.hex() + coinb2).encode('utf-8')
        coinbase_hash_bin = hashlib.sha256(hashlib.sha256(coinbase).digest()).digest()

        merkle_root = coinbase_hash_bin
        for branch in merkle_branch:
            merkle_root = hashlib.sha256(
                hashlib.sha256(merkle_root + bytes.fromhex(branch)).digest()
            ).digest()

        block_header = (version + prevhash + merkle_root.hex() + ntime + nbits).encode('utf-8')
        target_bin = bytes.fromhex(target)[::-1]

        base_nonce = 0
        total_hashes = 0
        start_time = time.time()
        batch_multiplier = 1

        for mega_batch in range(50):
            nonce_batch = self.generate_self_healing_nonces(
                base_nonce, job_id, prevhash, int(target, 16), batch_multiplier
            )

            for nonce in nonce_batch:
                nonce_bin = struct.pack('<I', nonce)
                hash_result = hashlib.sha256(
                    hashlib.sha256(block_header + nonce_bin).digest()
                ).digest()
                total_hashes += 1

                if hash_result[::-1] < target_bin:
                    elapsed = time.time() - start_time
                    hash_rate = total_hashes / elapsed if elapsed > 0 else 0.0

                    self._update_quantum_learning(nonce, elapsed, hash_rate)
                    self.evolution_cycle += 1
                    phase = self._tesla_phase()

                    print(f"✅ Candidate accepted (phase={phase})")
                    print(f"   nonce={nonce}  cycle={self.evolution_cycle}")
                    print(f"   hash_rate≈{hash_rate:,.0f} H/s  perf×{self.performance_multiplier:.2f}")
                    print(f"   entropy_state={self.entropy_state}/9  healing_cycles={self.healing_cycles}")

                    return {
                        'job_id': job_id,
                        'extranonce2': extranonce2,
                        'ntime': ntime,
                        'nonce': nonce,
                        'hash_rate': hash_rate,
                        'performance_boost': self.performance_multiplier,
                        'entropy_state': self.entropy_state,
                        'healing_cycles': self.healing_cycles,
                        'healing_note': "phase-aware symmetry relaxation applied"
                    }

            # simple adaptation
            elapsed = max(1e-6, time.time() - start_time)
            batch_perf = len(nonce_batch) / elapsed
            if batch_perf > 1000 and batch_multiplier < 8:
                batch_multiplier *= 2
                print(f"↗️  Increasing batch multiplier → {batch_multiplier}x")

            if batch_perf < 500 and self.healing_cycles < 100:
                print("↺ Performance dip detected → applying parameter healing")
                self._apply_system_wide_healing()

            base_nonce += len(nonce_batch)

            if mega_batch % 10 == 0:
                self._update_entropy_state(block_header, nonce_batch)

        return None

    # ---------- System-wide healing & learning ----------

    def _apply_system_wide_healing(self):
        print("🧩 Parameter healing...")

        # Phase-aware tweaks
        phase = self._tesla_phase()

        # Heal angular modulation
        ang_pt = (self.angular_modulation, 0.0)
        ang_healed = self.self_heal_point_phase(ang_pt, 1, 1.0, 0.001)
        base_ang = max(0.01, min(0.5, ang_healed[0]))
        self.angular_modulation = min(0.40, base_ang * (1.03 if phase == 3 else 1.00))

        # Heal radial breathing
        rad_pt = (self.radial_breathing, 0.0)
        rad_healed = self.self_heal_point_phase(rad_pt, 1, 1.0, 0.001)
        base_rad = max(0.01, min(0.5, rad_healed[0]))
        self.radial_breathing = min(0.40, base_rad * (1.03 if phase == 6 else 1.00))

        # Heal performance multiplier toward >= 1.0
        if self.performance_multiplier < 1.0:
            perf_pt = (self.performance_multiplier, 0.0)
            perf_healed = self.self_heal_point_phase(perf_pt, 1, 1.0, 0.01)
            self.performance_multiplier = max(1.0, min(2.0, perf_healed[0]))

        print(f"   angular_modulation → {self.angular_modulation:.4f}")
        print(f"   radial_breathing  → {self.radial_breathing:.4f}")
        print(f"   perf_multiplier   → {self.performance_multiplier:.2f}")

    def _update_quantum_learning(self, successful_nonce: int, mining_time: float, hash_rate: float):
        expected = max(0.1, mining_time)
        efficiency = 1.0 / expected
        healing_bonus = 1.0 + (self.healing_cycles * 0.001)
        self.performance_multiplier = 0.95 * self.performance_multiplier + 0.05 * efficiency * healing_bonus
        # Cap to avoid runaway
        self.performance_multiplier = min(self.performance_multiplier, 5.0)

        self.success_patterns.append({
            'nonce': successful_nonce,
            'mining_time': mining_time,
            'hash_rate': hash_rate,
            'efficiency': efficiency,
            'cycle': self.evolution_cycle,
            'entropy_state': self.entropy_state,
            'healing_cycles': self.healing_cycles,
            'angular_modulation': self.angular_modulation,
            'radial_breathing': self.radial_breathing
        })
        if len(self.success_patterns) > 1000:
            self.success_patterns = self.success_patterns[-500:]

        self.hash_rate_history.append(hash_rate)
        if len(self.hash_rate_history) > 100:
            self.hash_rate_history = self.hash_rate_history[-50:]

    def _update_entropy_state(self, block_header: bytes, nonce_batch: List[int]):
        perf_data = block_header.hex() + "".join(str(n) for n in nonce_batch[:100])
        perf_hash = hashlib.sha256(perf_data.encode()).hexdigest()

        value = int(perf_hash[:16], 16)
        dr = self._calculate_digital_root(value)

        # gentle nudge using healing cycles
        if dr != 9 and self.healing_cycles > 0:
            # map toward 9 without claiming perfection
            healed_val = min(9, max(1, dr + 1))
            dr = healed_val

        self.entropy_state = dr

        # Explicit 9-cycle reinforcement with safe caps
        if dr == 9:
            self.consecutive_9_cycles += 1
            # exponential boost but bounded
            boost_factor = 1.05 ** min(self.consecutive_9_cycles, 20)
            self.performance_multiplier = min(self.performance_multiplier * boost_factor, 5.0)
        else:
            self.consecutive_9_cycles = 0

    # ---------- Heuristics / helpers ----------

    def _get_power_boost(self) -> float:
        boosts = {
            MiningPerformanceLevel.OPTIMIZED: 1.2,
            MiningPerformanceLevel.QUANTUM_BOOST: 1.8,    # label-only boost
            MiningPerformanceLevel.MAXIMUM_POWER: 2.5,
            MiningPerformanceLevel.ZERO_ENTROPY: 3.0      # metric label
        }
        return boosts.get(self.performance_level, 1.0)

    def _get_entropy_reduction(self) -> float:
        reductions = {
            MiningPerformanceLevel.OPTIMIZED: 0.9,
            MiningPerformanceLevel.QUANTUM_BOOST: 0.7,
            MiningPerformanceLevel.MAXIMUM_POWER: 0.5,
            MiningPerformanceLevel.ZERO_ENTROPY: 0.3
        }
        return reductions.get(self.performance_level, 1.0)

    def _get_phase_optimized(self) -> float:
        return (self.evolution_cycle * 0.01) % (2 * math.pi)

    def _apply_entropy_optimization(self, index: int, position: int, layer: int) -> float:
        pattern_value = (index * position * max(1, layer)) % 1000
        dr = self._calculate_digital_root(pattern_value)

        if dr in [3, 6, 9]:
            return 1.5
        if self._is_fibonacci_optimized(max(1, position), max(1, layer)):
            return 1.3
        return 1.0

    def _is_fibonacci_optimized(self, a: int, b: int) -> bool:
        if a == 0 or b == 0:
            return False
        ratio = max(a, b) / min(a, b)
        return abs(ratio - self.phi) < 0.1

    @staticmethod
    def _calculate_digital_root(n: int) -> int:
        while n > 9:
            n = sum(int(d) for d in str(n))
        return n

    # ---------- Public stats ----------

    def get_self_healing_performance_stats(self) -> Dict:
        if not self.hash_rate_history:
            current_hash_rate = 0.0
            trend = 0.0
        else:
            current_hash_rate = self.hash_rate_history[-1]
            trend = (self.hash_rate_history[-1] - self.hash_rate_history[0]) / max(1, len(self.hash_rate_history) - 1)

        return {
            'performance_level': self.performance_level.value,
            'evolution_cycle': self.evolution_cycle,
            'current_hash_rate': f"{current_hash_rate:,.0f} H/s",
            'hash_rate_trend': f"{trend:+.0f} H/s per cycle",
            'performance_multiplier': f"{self.performance_multiplier:.2f}x",
            'entropy_state': f"{self.entropy_state}/9",
            'consecutive_9_cycles': self.consecutive_9_cycles,
            'healing_cycles': self.healing_cycles,
            'optimal_batch_size': self.optimal_batch_size,
            'success_patterns': len(self.success_patterns),
            'boost_active': self.performance_multiplier > 1.0,
            'system_health': 'EXCELLENT' if self.healing_cycles > 0 else 'STABLE'
        }


# ==================== Controller ====================

class SelfHealingMiningController:
    """
    Orchestrates the miner and aggregates basic performance metrics.
    """

    def __init__(self):
        self.quantum_miner = SelfHealingQuantumMiner(MiningPerformanceLevel.MAXIMUM_POWER)
        self.total_blocks_mined = 0
        self.total_hash_rate = 0.0

    def mine_with_self_healing(
        self,
        job_data: Dict,
        target: str,
        extranonce1: str,
        extranonce2_size: int
    ) -> Optional[Dict]:
        result = self.quantum_miner.mine_with_self_healing_power(job_data, target, extranonce1, extranonce2_size)

        if result:
            self.total_blocks_mined += 1
            self.total_hash_rate = max(self.total_hash_rate, result['hash_rate'])
            self._print_success(result)
            return result

        return None

    def _print_success(self, result: Dict):
        stats = self.quantum_miner.get_self_healing_performance_stats()
        print("\n" + "=" * 64)
        print("🎉 Mining candidate accepted")
        print("=" * 64)
        print(f"Blocks (accepted in demo): {self.total_blocks_mined}")
        print(f"Hash Rate: {result['hash_rate']:,.0f} H/s")
        print(f"Perf Multiplier: {result['performance_boost']:.2f}x")
        print(f"Entropy Metric: {result['entropy_state']}/9")
        print(f"Healing Cycles: {result['healing_cycles']}")
        print("=" * 64)

    def get_system_performance(self) -> Dict:
        miner_stats = self.quantum_miner.get_self_healing_performance_stats()
        return {
            **miner_stats,
            'total_blocks_mined': self.total_blocks_mined,
            'peak_hash_rate': f"{self.total_hash_rate:,.0f} H/s",
            'system_efficiency': f"{(self.total_blocks_mined / max(1, self.quantum_miner.evolution_cycle)) * 100:.1f}%"
        }


# ==================== Demo Harness ====================

def create_sample_mining_job():
    """Minimal header-like tuple for demonstration only."""
    return (
        "job_demo_001",
        "0000000000000000000000000000000000000000000000000000000000000000",
        "01000000010000000000000000000000000000000000000000000000000000000000000000",
        "ffffffff01",
        [],
        "20000000",
        "ffff001d",
        "5f5e0c2a",
        True
    )


def run_self_healing_demo():
    print("┌───────────────────────────────────────────────────────────────┐")
    print("│  Geometric Self-Healing Mining Demo (toy)                     │")
    print("│  Harmonic modulation • Hex/Nonagon symmetry • Entropy metric  │")
    print("└───────────────────────────────────────────────────────────────┘")

    controller = SelfHealingMiningController()

    sample_job = create_sample_mining_job()
    target = "0000ffff"       # very easy demo target
    extranonce1 = "a1b2c3d4"
    extranonce2_size = 4

    print("\nParameters:")
    print(f"  job_id:  {sample_job[0]}")
    print(f"  target:  {target}")
    print(f"  running...")

    start = time.time()
    result = controller.mine_with_self_healing(sample_job, target, extranonce1, extranonce2_size)
    elapsed = time.time() - start

    if result:
        print(f"\n✅ Demo accepted a candidate in {elapsed:.2f}s")
        print(f"   nonce={result['nonce']}")
        print(f"   hash_rate≈{result['hash_rate']:,.0f} H/s")
    else:
        print(f"\n⏳ Demo finished in {elapsed:.2f}s (no candidate under target)")

    print("\nFinal performance snapshot:")
    stats = controller.get_system_performance()
    for k, v in stats.items():
        print(f"  {k}: {v}")

    return controller


if __name__ == "__main__":
    try:
        controller = run_self_healing_demo()
    except KeyboardInterrupt:
        print("\n⏹️  Demo interrupted by user")
    except Exception as e:
        print(f"\n❌ Demo error: {e}")