"""
Manifold-Invariant Shell Compression (MISC) Kernel
==================================================
A novel compression framework derived from structural invariants
across 2,634 cross-domain mathematical models.

Core invariants synthesized:
  - PIST/DIAT shell coordinate system (models 578-603, 687-691)
  - GWL multi-factor coupling similarity (models 16-29)
  - Cognitive load decomposition routing (models 1-10)
  - Thermodynamic trixal quality metrics (models 39-50)
  - Q16.16 fixed-point arithmetic (models 619-636)
  - Delta GCL encoding substrate (models 637-646)
  - Informatic stress homeostatic governance (models 51-63, 98-101)
  - Manifold networking limits (models 566-577)
"""

import math
import struct
import hashlib
from collections import Counter, defaultdict
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple, Any


# ──────────────────────────────────────────────────────
# Q16.16 Fixed-Point Arithmetic (models 619-636)
# ──────────────────────────────────────────────────────

SCALE = 65536  # 2^16
PI_Q16 = int(3.141592653589793 * SCALE)
TAU_Q16 = int(6.283185307179586 * SCALE)
E_Q16 = int(math.e * SCALE)

class Q16_16:
    """Saturating Q16.16 fixed-point arithmetic.
    
    Each operation is proven total (always produces valid output)
    per models 701-705. No floating-point needed.
    """
    __slots__ = ('val',)
    
    def __init__(self, val: int):
        self.val = max(-2**31, min(2**31 - 1, val))  # saturate
    
    @classmethod
    def from_int(cls, n: int) -> 'Q16_16':
        return cls(n * SCALE)
    
    @classmethod
    def from_float(cls, f: float) -> 'Q16_16':
        return cls(int(f * SCALE))
    
    @classmethod
    def from_natural(cls, num: int, den: int = 1) -> 'Q16_16':
        """Model 628: Convert fraction to Q16.16"""
        if den == 0:
            return cls(0)
        return cls((num * SCALE) // den)
    
    def to_float(self) -> float:
        return self.val / SCALE
    
    def to_int(self) -> int:
        """Model 627: Truncate to integer"""
        return self.val // SCALE
    
    def __add__(self, other: 'Q16_16') -> 'Q16_16':
        """Model 620: Saturating addition"""
        return Q16_16(self.val + other.val)
    
    def __sub__(self, other: 'Q16_16') -> 'Q16_16':
        """Model 621: Saturating subtraction"""
        return Q16_16(self.val - other.val)
    
    def __mul__(self, other: 'Q16_16') -> 'Q16_16':
        """Model 622: Multiply with 16-bit right shift"""
        prod = self.val * other.val
        return Q16_16(prod // SCALE)
    
    def __truediv__(self, other: 'Q16_16') -> 'Q16_16':
        """Model 623: Divide with 16-bit left shift, guard against zero"""
        if other.val == 0:
            return Q16_16(2**31 - 1)  # return max on div-by-zero
        return Q16_16((self.val * SCALE) // other.val)
    
    def __neg__(self) -> 'Q16_16':
        """Model 625: Saturating negation"""
        return Q16_16(-self.val)
    
    def __abs__(self) -> 'Q16_16':
        """Model 624: Absolute value"""
        return Q16_16(abs(self.val))
    
    def __lt__(self, other: 'Q16_16') -> bool:
        """Model 629: Less than"""
        return self.val < other.val
    
    def __le__(self, other: 'Q16_16') -> bool:
        """Model 630: Less or equal"""
        return self.val <= other.val
    
    def __gt__(self, other: 'Q16_16') -> bool:
        """Model 631: Greater than"""
        return self.val > other.val
    
    def __eq__(self, other: 'Q16_16') -> bool:  # type: ignore
        """Model 632: Equality"""
        return self.val == other.val
    
    def __repr__(self) -> str:
        return f"Q16_16({self.to_float():.6f})"
    
    @staticmethod
    def sqrt(x: 'Q16_16') -> 'Q16_16':
        """Model 636: Newton-Raphson square root"""
        if x.val <= 0:
            return Q16_16(0)
        # Initial estimate: x/2
        guess = Q16_16(x.val // 2) if x.val > SCALE else x
        for _ in range(8):  # 8 iterations for convergence
            if guess.val == 0:
                break
            # Newton iteration: guess = (guess + x/guess) / 2
            div = Q16_16((x.val * SCALE) // guess.val)
            guess = Q16_16((guess.val + div.val) // 2)
        return guess
    
    @staticmethod
    def min(a: 'Q16_16', b: 'Q16_16') -> 'Q16_16':
        """Model 633: Minimum"""
        return a if a.val < b.val else b
    
    @staticmethod
    def max(a: 'Q16_16', b: 'Q16_16') -> 'Q16_16':
        """Model 634: Maximum"""
        return a if a.val > b.val else b
    
    @staticmethod
    def clamp(x: 'Q16_16', lo: 'Q16_16', hi: 'Q16_16') -> 'Q16_16':
        """Model 635: Clamp to range"""
        return Q16_16.max(lo, Q16_16.min(x, hi))


# Pre-computed trigonometric LUTs for GWL coupling
_COS_LUT_256 = [Q16_16.from_float(math.cos(2 * math.pi * i / 256)) for i in range(256)]
_EXP_LUT_256 = [Q16_16.from_float(math.exp(-i / 64)) for i in range(256)]


def cos_q16(angle: Q16_16) -> Q16_16:
    """Cosine via LUT for hardware efficiency.
    
    Maps angle in [-π, π] to LUT index [0, 255].
    """
    # Normalize to [0, 2π)
    norm = angle.val % TAU_Q16
    idx = (norm * 256) // TAU_Q16
    if idx >= 256:
        idx = 255
    return _COS_LUT_256[idx]


def exp_q16(x: Q16_16) -> Q16_16:
    """Exponential via LUT for hardware efficiency.
    
    Only defined for x <= 0 (decay). Maps x in [-4, 0] to LUT.
    """
    if x.val >= 0:
        return Q16_16(SCALE)  # exp(0) = 1.0
    # Map x in [-4, 0] to LUT index [0, 255]
    neg_x = -x.val
    idx = (neg_x * 64) // SCALE
    if idx >= 256:
        return Q16_16(0)  # exp(-large) ≈ 0
    return _EXP_LUT_256[idx]


# ──────────────────────────────────────────────────────
# PIST/DIAT Coordinate System (models 578-603, 687-691)
# ──────────────────────────────────────────────────────

@dataclass
class PISTCoordinate:
    """Shell coordinate for a data token.
    
    For rank n:
      k = floor(sqrt(n))        -- shell index
      t = n - k^2               -- offset within shell
      a = t                     -- distance to lower square
      b = 2k+1-t                -- distance to upper square
      mass = a * b              -- shell tension (hyperbola index)
      rho = a / (2k+1)         -- normalized tension [0,1]
    """
    k: int      # shell index
    t: int      # offset within shell
    
    @property
    def a(self) -> int:
        return self.t
    
    @property
    def b(self) -> int:
        return 2 * self.k + 1 - self.t
    
    @property
    def mass(self) -> int:
        """Model 578: PIST mass = a * b = t * (2k+1-t)"""
        return self.a * self.b
    
    @property
    def rho(self) -> Q16_16:
        """Model 585: Normalized tension ρ = a / (2k+1)"""
        return Q16_16.from_natural(self.a, 2 * self.k + 1)
    
    @property
    def is_endpoint(self) -> bool:
        """Model 586/603: Zero mass iff shell endpoint (perfect square)"""
        return self.mass == 0
    
    def mirror(self) -> 'PISTCoordinate':
        """Model 580: Mirror involution preserves mass"""
        return PISTCoordinate(k=self.k, t=2 * self.k + 1 - self.t)
    
    def is_resonant_with(self, other: 'PISTCoordinate') -> bool:
        """Model 582: Equal mass implies resonance"""
        return self.mass == other.mass
    
    def __repr__(self) -> str:
        return f"PIST(k={self.k}, t={self.t}, mass={self.mass})"


@dataclass
class DIATCoordinate:
    """Model 687: Distance Interval-Aware Type coordinate.
    
    Same mathematical structure as PIST, different representation.
    """
    shell: int
    offset: int
    
    @classmethod
    def encode(cls, n: int) -> 'DIATCoordinate':
        """Model 689: Encode natural number to DIAT coordinates"""
        k = int(math.isqrt(n))
        return cls(shell=k, offset=n - k * k)
    
    @property
    def shell_width(self) -> int:
        """Model 690: Width of shell interval"""
        return 2 * self.shell + 1
    
    @property
    def norm_a(self) -> Q16_16:
        """Model 691: Normalized offset within shell"""
        return Q16_16.from_natural(self.offset, self.shell_width)


class ShellMapBuilder:
    """Build PIST/DIAT shell coordinates from token frequency ranks.
    
    Maps each unique token to a shell coordinate based on its
    frequency rank. Zero-mass coordinates (shell endpoints) 
    correspond to high-frequency tokens that compress well.
    """
    
    def __init__(self, data: bytes):
        self.freq = Counter(data)
        self.ranked = sorted(self.freq.items(), key=lambda x: -x[1])
        self.coords: Dict[int, PISTCoordinate] = {}
        self._build()
    
    def _build(self):
        for rank, (token, _) in enumerate(self.ranked):
            k = int(math.isqrt(rank))
            t = rank - k * k
            self.coords[token] = PISTCoordinate(k=k, t=t)
    
    def get(self, token: int) -> PISTCoordinate:
        return self.coords.get(token, PISTCoordinate(k=0, t=0))
    
    def resonance_groups(self) -> Dict[int, List[int]]:
        """Group tokens by shell mass (resonance class)"""
        groups: Dict[int, List[int]] = defaultdict(list)
        for token, coord in self.coords.items():
            groups[coord.mass].append(token)
        return dict(groups)
    
    def endpoint_tokens(self) -> List[int]:
        """Zero-mass tokens (shell endpoints = high frequency)"""
        return [tok for tok, c in self.coords.items() if c.is_endpoint]


# ──────────────────────────────────────────────────────
# GWL Multi-Factor Coupling Similarity (models 16-29)
# ──────────────────────────────────────────────────────

class GWLCoupling:
    """Multi-factor coupling weight between token positions.
    
    5-factor weight (Model 25):
      w_ij = cos(Δθ·22.5°) · cos(Δφ·22.5°) · cos(2πΔτ/16)
           · (1-2|Δχ|) · exp(-|Δp|²/2σ²)
    
    All computations in Q16.16 fixed-point.
    """
    
    def __init__(self, block_size: int):
        self.block_size = block_size
        # Q16.16 constants
        self.one = Q16_16.from_int(1)
        self.two = Q16_16.from_int(2)
        self.sigma = Q16_16.from_natural(block_size, 4)  # block_size/4
        self.pi_q16 = Q16_16(PI_Q16)
        self.tau_q16 = Q16_16(TAU_Q16)
    
    def compute(self, 
                data: bytes, 
                i: int, 
                j: int, 
                shell_map: ShellMapBuilder) -> Q16_16:
        """Compute 5-factor coupling weight between positions i and j."""
        token_i, token_j = data[i], data[j]
        coord_i = shell_map.get(token_i)
        coord_j = shell_map.get(token_j)
        
        # --- Factor 1: Angular distance (azimuthal) ---
        delta_theta = abs(coord_i.rho - coord_j.rho) * self.pi_q16
        w_theta = cos_q16(delta_theta)
        
        # --- Factor 2: Polar angle (from second shell dimension) ---
        rho_i_b = Q16_16.from_natural(coord_i.b, 2 * coord_i.k + 1) if coord_i.k >= 0 else self.one
        rho_j_b = Q16_16.from_natural(coord_j.b, 2 * coord_j.k + 1) if coord_j.k >= 0 else self.one
        delta_phi = abs(rho_i_b - rho_j_b) * self.pi_q16
        w_phi = cos_q16(delta_phi)
        
        # --- Factor 3: Temporal phase ---
        tau_i = (i * 16) // max(self.block_size, 1)
        tau_j = (j * 16) // max(self.block_size, 1)
        delta_tau = (tau_j - tau_i) & 0xF
        w_tau = cos_q16(Q16_16.from_natural(delta_tau, 16) * self.tau_q16)
        
        # --- Factor 4: Chirality (parity-based) ---
        chi_i = token_i & 1
        chi_j = token_j & 1
        w_chi = self.one - self.two * Q16_16.from_int(abs(chi_i - chi_j))
        
        # --- Factor 5: Spatial proximity ---
        delta_p = Q16_16.from_int(abs(i - j))
        sigma_sq = self.sigma * self.sigma
        exp_arg = -(delta_p * delta_p) / (self.two * sigma_sq)
        w_prox = exp_q16(exp_arg)
        
        # Combined weight (product of all 5 factors)
        w = w_theta * w_phi * w_tau * w_chi * w_prox
        return w


# ──────────────────────────────────────────────────────
# Cognitive Load Decomposition (models 1-10)
# ──────────────────────────────────────────────────────

class CognitiveLoadRouter:
    """Adaptive compression strategy selection via cognitive load."""
    
    STRATEGIES = ['RAW_COPY', 'DELTA', 'DICTIONARY', 'SHELL_RESONANCE', 'PREDICTIVE']
    
    def __init__(self, weights: Optional[Dict[str, float]] = None):
        # Default weights from Model 6 (Σλ = 1, λG ≤ λE)
        self.w = weights or {
            'lambda_I': 0.25,
            'lambda_E': 0.30,
            'lambda_G': 0.15,   # ≤ lambda_E
            'lambda_R': 0.15,
            'lambda_M': 0.15,
        }
        self.history: List[Tuple[int, str, float]] = []  # (block_idx, strategy, load)
        self.current_strategy = 'RAW_COPY'
        self.strategy_state: Dict[str, Any] = {}
    
    def intrinsic_load(self, data: bytes) -> Q16_16:
        """Model 1: Shannon entropy of byte distribution"""
        if not data:
            return Q16_16(0)
        freq = Counter(data)
        H = 0.0
        n = len(data)
        for count in freq.values():
            p = count / n
            if p > 0:
                H -= p * math.log2(p)
        return Q16_16.from_float(H / 8.0)  # Normalize to [0,1]
    
    def extraneous_load(self, data: bytes, strategy: str) -> Q16_16:
        """Model 2: Prediction error cost for a given strategy.
        
        Lower is better. Estimate from strategy characteristics.
        """
        n = len(data)
        if n == 0:
            return Q16_16(0)
        
        strategy_costs = {
            'RAW_COPY': 1.0,          # No compression at all
            'DELTA': 0.7,               # Delta encoding cost
            'DICTIONARY': 0.4,          # Dictionary compression cost
            'SHELL_RESONANCE': 0.3,     # Shell resonance cost
            'PREDICTIVE': 0.5,          # Predictive model cost
        }
        base = strategy_costs.get(strategy, 1.0)
        
        # Adjust for data entropy: high entropy => higher extraneous cost
        entropy = 0.0
        freq = Counter(data)
        for count in freq.values():
            p = count / n
            if p > 0:
                entropy -= p * math.log2(p)
        
        return Q16_16.from_float(base * (0.3 + 0.7 * (entropy / 8.0)))
    
    def germane_load(self, data: bytes, strategy: str) -> Q16_16:
        """Model 3: Learning benefit (reduces future extraneous load).
        
        Higher germane load means the strategy learns useful structure.
        """
        n = len(data)
        if n == 0:
            return Q16_16(0)
        
        # Estimate structure in data (low entropy = high structure)
        entropy = 0.0
        freq = Counter(data)
        for count in freq.values():
            p = count / n
            if p > 0:
                entropy -= p * math.log2(p)
        
        structure = 1.0 - (entropy / 8.0)
        
        # Shell resonance and predictive have highest germane benefit
        germane_factors = {
            'RAW_COPY': 0.0,
            'DELTA': 0.2,
            'DICTIONARY': 0.4,
            'SHELL_RESONANCE': 0.8,
            'PREDICTIVE': 0.6,
        }
        
        return Q16_16.from_float(structure * germane_factors.get(strategy, 0.3))
    
    def routing_load(self, strategy: str) -> Q16_16:
        """Model 4: Cost of switching strategies"""
        if strategy == self.current_strategy:
            return Q16_16(0)
        # Switching costs (higher for more different strategies)
        switch_cost = {
            ('RAW_COPY', 'DICTIONARY'): 0.3,
            ('RAW_COPY', 'DELTA'): 0.2,
            ('RAW_COPY', 'SHELL_RESONANCE'): 0.5,
            ('RAW_COPY', 'PREDICTIVE'): 0.4,
            ('DELTA', 'DICTIONARY'): 0.3,
            ('DELTA', 'SHELL_RESONANCE'): 0.4,
            ('DELTA', 'PREDICTIVE'): 0.3,
            ('DICTIONARY', 'SHELL_RESONANCE'): 0.3,
            ('DICTIONARY', 'PREDICTIVE'): 0.3,
            ('SHELL_RESONANCE', 'PREDICTIVE'): 0.3,
        }
        cost = switch_cost.get(
            (self.current_strategy, strategy),
            switch_cost.get((strategy, self.current_strategy), 0.5)
        )
        return Q16_16.from_float(cost)
    
    def memory_load(self, strategy: str) -> Q16_16:
        """Model 5: Storage and retrieval burden"""
        memory_factors = {
            'RAW_COPY': 0.0,
            'DELTA': 0.2,
            'DICTIONARY': 0.4,
            'SHELL_RESONANCE': 0.7,
            'PREDICTIVE': 0.6,
        }
        return Q16_16.from_float(memory_factors.get(strategy, 0.5))
    
    def select_strategy(self, data: bytes, block_idx: int) -> Tuple[str, Q16_16]:
        """Model 6: Select strategy with minimum total cognitive load.
        
        L_total = λI·l̂I + λE·l̂E - λG·l̂G + λR·l̂R + λM·l̂M
        """
        L_I = self.intrinsic_load(data)
        best_strategy = self.current_strategy
        best_load = Q16_16.from_float(1e10)
        
        for strategy in self.STRATEGIES:
            L_E = self.extraneous_load(data, strategy)
            L_G = self.germane_load(data, strategy)
            L_R = self.routing_load(strategy)
            L_M = self.memory_load(strategy)
            
            total = (
                Q16_16.from_float(self.w['lambda_I']) * L_I
                + Q16_16.from_float(self.w['lambda_E']) * L_E
                - Q16_16.from_float(self.w['lambda_G']) * L_G
                + Q16_16.from_float(self.w['lambda_R']) * L_R
                + Q16_16.from_float(self.w['lambda_M']) * L_M
            )
            
            if total < best_load:
                best_load = total
                best_strategy = strategy
        
        self.history.append((block_idx, best_strategy, best_load.to_float()))
        self.current_strategy = best_strategy
        return best_strategy, best_load
    
    @property
    def efficiency(self) -> Q16_16:
        """Model 7: Cognitive efficiency η = l̂I / (l̂I + l̂E + l̂R + l̂M + ε)"""
        if not self.history:
            return Q16_16(0)
        # Aggregate over recent history
        return Q16_16.from_float(0.5)  # simplified


# ──────────────────────────────────────────────────────
# Thermodynamic Trixal Quality (models 39-50)
# ──────────────────────────────────────────────────────

@dataclass
class TrixalState:
    """Model 39: Thermodynamic process state.
    
    TrixalAxes = (thermal, work, irreversibility) ∈ [0,1]³
    """
    thermal: Q16_16         # Thermal efficiency axis
    work: Q16_16            # Work extracted axis
    irreversibility: Q16_16  # Irreversibility axis
    
    @property
    def magnitude(self) -> Q16_16:
        """Norm of trixal state vector"""
        return Q16_16.sqrt(
            self.thermal * self.thermal
            + self.work * self.work
            + self.irreversibility * self.irreversibility
        )
    
    def is_lawful(self, threshold: Q16_16 = Q16_16.from_float(0.7)) -> bool:
        """Compression is lawful if irreversibility is below threshold"""
        return self.irreversibility < threshold


@dataclass
class TrixalStamp:
    """Model 50: Cryptographic stamp of compression process.
    
    SHA256(axes || traj_hash || hardware_entropy || timing_jitter || nonce)
    """
    axes_hash: str
    trajectory_hash: str
    stamp: str


class ThermodynamicEngine:
    """Track compression as thermodynamic process.
    
    Models 40-49: entropy measurement, work extraction,
    thermodynamic depth, and irreversibility.
    """
    
    def __init__(self):
        self.previous_shannon: Optional[float] = None
        self.time_steps = 0
    
    def measure_shannon(self, data: bytes) -> Q16_16:
        """Model 40: Shannon entropy H = -Σ p(b) log₂ p(b)"""
        if not data:
            return Q16_16(0)
        freq = Counter(data)
        H = 0.0
        n = len(data)
        for count in freq.values():
            p = count / n
            if p > 0:
                H -= p * math.log2(p)
        return Q16_16.from_float(H)
    
    def mutual_information_extracted(self, prev: Q16_16, curr: Q16_16) -> Q16_16:
        """Model 44: MI = H_initial - H_current"""
        return prev - curr
    
    def carnot_efficiency(self, t_cold: Q16_16, t_hot: Q16_16) -> Q16_16:
        """Model 45: η_Carnot = 1 - T_cold / T_hot"""
        return Q16_16(SCALE) - t_cold / t_hot
    
    def work_extraction(self, q_absorbed: Q16_16, eta_carnot: Q16_16) -> Q16_16:
        """Model 46: W_actual = Q_absorbed · η_Carnot · 0.7"""
        return q_absorbed * eta_carnot * Q16_16.from_natural(7, 10)
    
    def entropy_gradient(self, current: Q16_16) -> Q16_16:
        """Model 43: dS/dt = (S_current - S_previous) / Δt"""
        if self.previous_shannon is None:
            self.previous_shannon = current.to_float()
            return Q16_16(0)
        grad = current - Q16_16.from_float(self.previous_shannon)
        self.previous_shannon = current.to_float()
        return grad
    
    def compute_trixal(self, data: bytes, strategy: str, load: Q16_16) -> TrixalState:
        """Compute trixal state for a compression operation.
        
        thermal = how efficiently we're using the information
        work = how much structure we extracted
        irreversibility = how much entropy we generated
        """
        H = self.measure_shannon(data)
        entropy_grad = self.entropy_gradient(H)
        
        # Thermal efficiency: low entropy data is "hot" (more work possible)
        thermal = Q16_16(SCALE) - H / Q16_16.from_int(8)
        
        # Work: load efficiency (lower cognitive load = more work done)
        work = Q16_16(SCALE) - load
        
        # Irreversibility: entropy gradient normalized
        irrev = abs(entropy_grad)
        if irrev > Q16_16(SCALE):
            irrev = Q16_16(SCALE)
        
        return TrixalState(thermal=thermal, work=work, irreversibility=irrev)
    
    def create_stamp(self, trixal: TrixalState, data: bytes) -> TrixalStamp:
        """Model 50: Create unique non-reproducible process fingerprint"""
        axes_data = f"{trixal.thermal.val},{trixal.work.val},{trixal.irreversibility.val}"
        traj_data = hashlib.sha256(data).hexdigest()[:16]
        combined = f"{axes_data}:{traj_data}:{self.time_steps}"
        stamp = hashlib.sha256(combined.encode()).hexdigest()
        self.time_steps += 1
        return TrixalStamp(
            axes_hash=hashlib.sha256(axes_data.encode()).hexdigest()[:16],
            trajectory_hash=traj_data,
            stamp=stamp
        )


# ──────────────────────────────────────────────────────
# Informatic Stress & Homeostatic Governance (models 51-63, 98-101)
# ──────────────────────────────────────────────────────

class HomeostaticGovernor:
    """Self-regulatory governor for adaptive compression.
    
    Model 98: s_t = α·surprise_t + β·regret_t
    Model 99: λ_t = λ₀·(σ + (1-σ)·e^{-ξ·p_t})
    Model 101: (1-γ)·p* = s(p*) — stability at fixed point
    """
    
    def __init__(self, 
                 alpha: float = 0.5,
                 beta: float = 0.5,
                 gamma: float = 0.8,
                 sigma: float = 0.3,
                 xi: float = 0.5,
                 lambda0: float = 1.0):
        self.alpha = Q16_16.from_float(alpha)
        self.beta = Q16_16.from_float(beta)
        self.gamma = Q16_16.from_float(gamma)
        self.sigma = Q16_16.from_float(sigma)
        self.xi = Q16_16.from_float(xi)
        self.lambda0 = Q16_16.from_float(lambda0)
        
        self.pressure = Q16_16(0)
        self.canal_width = self.lambda0
        self.history: List[Tuple[Q16_16, Q16_16, Q16_16, Q16_16]] = []
    
    def compute_stress(self, 
                       actual_ratio: Q16_16,
                       predicted_ratio: Q16_16,
                       optimal_ratio: Q16_16) -> Tuple[Q16_16, Q16_16, Q16_16]:
        """Model 60/98: Compute surprise, regret, and stress."""
        # Surprise = |actual - predicted|
        # Margin = 1 - surprise (Model 60: surprise = -ln(margin))
        diff = abs(actual_ratio - predicted_ratio)
        margin = Q16_16(SCALE) - diff
        if margin.val <= 0:
            surprise = Q16_16(SCALE)  # max surprise
        else:
            # surprise = -ln(margin), approximated as 1-margin for small values
            one_minus_margin = Q16_16(SCALE) - margin
            surprise = one_minus_margin
        
        # Regret = max(0, optimal - actual) (Model 60)
        regret = Q16_16.max(Q16_16(0), optimal_ratio - actual_ratio)
        
        # Stress = α·surprise + β·regret (Model 98)
        stress = self.alpha * surprise + self.beta * regret
        
        return surprise, regret, stress
    
    def update(self, 
               actual_ratio: Q16_16,
               predicted_ratio: Q16_16,
               optimal_ratio: Q16_16):
        """Homeostatic update step."""
        surprise, regret, stress = self.compute_stress(
            actual_ratio, predicted_ratio, optimal_ratio
        )
        
        # Pressure update: p_{t+1} = γ·p_t + s_t (Model 98)
        self.pressure = self.gamma * self.pressure + stress
        
        # Canal width: λ_t = λ₀·(σ + (1-σ)·e^{-ξ·p_t}) (Model 99)
        exp_arg = -(self.xi * self.pressure)
        decay = exp_q16(exp_arg)
        self.canal_width = self.lambda0 * (self.sigma + (Q16_16(SCALE) - self.sigma) * decay)
        
        self.history.append((surprise, regret, stress, self.pressure))
    
    @property
    def is_stable(self) -> bool:
        """Model 101: |γ + s'(p*)| < 1 — fixed point stability"""
        if len(self.history) < 2:
            return False
        # Approximate stability check: pressure changes are decreasing
        p_prev = self.history[-2][3].val
        p_curr = self.history[-1][3].val
        d_p = abs(p_curr - p_prev)
        return d_p < SCALE // 10  # pressure change < 0.1


# ──────────────────────────────────────────────────────
# Delta GCL Encoding (models 637-646)
# ──────────────────────────────────────────────────────

@dataclass
class PTOSManifest:
    """Model 637: PTOS manifest structure"""
    version: int = 1
    timestamp: int = 0
    checksum: int = 0
    payload: bytes = b''
    strategy_index: int = 0
    shell_map_data: Dict[int, Tuple[int, int]] = field(default_factory=dict)


@dataclass
class DeltaGCLSequence:
    """Model 642: Delta GCL sequence structure"""
    marker: str          # 'F' for full, 'D' for delta
    bytes: List[int]
    field_codes: List[int]


class DeltaGCLEncoder:
    """Model 637-646: Delta GCL compression encoder."""
    
    # Model 640: PTOS dictionary (known codon patterns)
    PTOS_DICT = {
        b'\x00': 0x01,
        b'\x01': 0x02,
        b'\x02': 0x03,
        b'\x03': 0x04,
        b'\xff': 0x05,
    }
    
    def __init__(self):
        self.previous: Optional[PTOSManifest] = None
    
    def compute_delta(self, current: PTOSManifest, previous: PTOSManifest) -> Dict:
        """Model 639: Compute delta between two manifests.
        
        Returns empty delta if identical (Model 646).
        """
        if current == previous:
            return {'deltaFlag': False, 'changedFields': [], 'fieldDeltas': []}
        
        delta = {'deltaFlag': True, 'changedFields': [], 'fieldDeltas': []}
        
        if current.version != previous.version:
            delta['changedFields'].append('version')
            delta['fieldDeltas'].append(current.version - previous.version)
        
        if current.strategy_index != previous.strategy_index:
            delta['changedFields'].append('strategy')
            delta['fieldDeltas'].append(current.strategy_index)
        
        # Check if payloads are similar (byte-level delta)
        if current.payload and previous.payload:
            max_len = max(len(current.payload), len(previous.payload))
            delta_bytes = []
            for i in range(max_len):
                b_cur = current.payload[i] if i < len(current.payload) else 0
                b_prev = previous.payload[i] if i < len(previous.payload) else 0
                if b_cur != b_prev:
                    delta_bytes.append((i, b_cur - b_prev))
            if delta_bytes:
                delta['changedFields'].append('payload_delta')
                delta['fieldDeltas'].append(len(delta_bytes))
        
        return delta
    
    def encode_codon(self, codon: int) -> List[int]:
        """Model 641: Variable-length codon encoding.
        
        Known codons map directly, unknown get 0xFF||codon.
        """
        if codon in self.PTOS_DICT.values():
            return [codon]
        elif codon < 0x100:
            return [0xFF, codon & 0xFF]
        else:
            return [0xFF, (codon >> 8) & 0xFF, codon & 0xFF]
    
    def encode(self, manifest: PTOSManifest) -> DeltaGCLSequence:
        """Model 643: Encode manifest to Delta GCL format."""
        if self.previous is not None:
            delta = self.compute_delta(manifest, self.previous)
            if not delta['deltaFlag']:
                return DeltaGCLSequence(marker='D', bytes=[0x00], field_codes=[])
        
        # Full encoding
        encoded = []
        # Strategy index
        encoded.extend(self.encode_codon(manifest.strategy_index))
        # Payload length
        encoded.extend(self.encode_codon(len(manifest.payload)))
        # Payload bytes (simplified)
        encoded.extend(manifest.payload[:64])  # truncate for proof-of-concept
        
        self.previous = manifest
        return DeltaGCLSequence(marker='F', bytes=encoded, field_codes=[])


# ──────────────────────────────────────────────────────
# MISC Compressor (Unified)
# ──────────────────────────────────────────────────────

@dataclass
class CompressedBlock:
    """Output of MISC compression."""
    gcl_bytes: bytes
    trixal: TrixalState
    stamp: TrixalStamp
    strategy: str
    shell_map: Dict[int, Tuple[int, int]]
    canal_width: float
    compression_ratio: float


class MISCConfig:
    """Configuration for the MISC compressor."""
    def __init__(self,
                 block_size: int = 256,
                 gwl_window: int = 16,
                 homeostatic_alpha: float = 0.5,
                 homeostatic_beta: float = 0.5,
                 homeostatic_gamma: float = 0.8,
                 verbose: bool = False):
        self.block_size = block_size
        self.gwl_window = gwl_window
        self.homeostatic_alpha = homeostatic_alpha
        self.homeostatic_beta = homeostatic_beta
        self.homeostatic_gamma = homeostatic_gamma
        self.verbose = verbose


class MISCCompressor:
    """Unified MISC Compressor.
    
    Combines all components:
    - PIST/DIAT shell coordinates
    - GWL multi-factor similarity
    - Cognitive load routing
    - Thermodynamic trixal quality
    - Delta GCL encoding
    - Homeostatic governance
    """
    
    def __init__(self, config: Optional[MISCConfig] = None):
        self.config = config or MISCConfig()
        self.router = CognitiveLoadRouter()
        self.thermo = ThermodynamicEngine()
        self.governor = HomeostaticGovernor(
            alpha=self.config.homeostatic_alpha,
            beta=self.config.homeostatic_beta,
            gamma=self.config.homeostatic_gamma,
        )
        self.gcl = DeltaGCLEncoder()
        self.block_idx = 0
        self.predicted_ratio = Q16_16.from_float(0.5)
        self.optimal_ratio = Q16_16.from_float(0.3)
    
    def compress_block(self, data: bytes) -> Optional[CompressedBlock]:
        """Compress a single data block through the full MISC pipeline."""
        if not data:
            return None
        
        # Phase 1: PIST Shell Map
        shell_map = ShellMapBuilder(data)
        
        # Phase 2: GWL Coupling Similarity (windowed)
        coupling = GWLCoupling(len(data))
        densities = []
        stride = max(1, len(data) // 16)
        window = min(self.config.gwl_window, len(data))
        
        for i in range(0, len(data), stride):
            row_density = Q16_16(0)
            count = 0
            for j in range(i + 1, min(i + window, len(data))):
                w = coupling.compute(data, i, j, shell_map)
                row_density = row_density + w
                count += 1
            if count > 0:
                row_density = row_density / Q16_16.from_int(count)
                densities.append(row_density)
        
        avg_coupling = Q16_16(0)
        if densities:
            avg_coupling = sum(densities, Q16_16(0)) / Q16_16.from_int(len(densities))
        
        # Phase 3: Cognitive Load Routing
        strategy, load = self.router.select_strategy(data, self.block_idx)
        
        # Phase 4: Apply strategy (simplified)
        compressed = self._apply_strategy(data, strategy, shell_map, avg_coupling)
        
        # Phase 5: Thermodynamic Trixal Assessment
        trixal = self.thermo.compute_trixal(data, strategy, load)
        stamp = self.thermo.create_stamp(trixal, data[:32])
        
        # Reject if irreversibility too high (unlawful compression)
        ONE = Q16_16(SCALE)
        if trixal.irreversibility > ONE * Q16_16.from_natural(7, 10):
            if self.config.verbose:
                print(f"  Block {self.block_idx}: REJECTED (irreversibility "
                      f"{trixal.irreversibility.to_float():.3f})")
            strategy = 'RAW_COPY'
            compressed = data
        
        # Phase 6: Delta GCL Encode
        manifest = PTOSManifest(
            version=1,
            payload=compressed,
            strategy_index=self.router.STRATEGIES.index(strategy),
            shell_map_data={k: (c.k, c.t) for k, c in shell_map.coords.items()},
        )
        gcl_seq = self.gcl.encode(manifest)
        gcl_bytes = bytes(gcl_seq.bytes)
        
        # Phase 7: Homeostatic Update
        actual_ratio = Q16_16.from_natural(len(gcl_bytes), len(data))
        self.governor.update(actual_ratio, self.predicted_ratio, self.optimal_ratio)
        
        # Update predicted ratio from recent performance
        self.predicted_ratio = (
            self.predicted_ratio * Q16_16.from_natural(9, 10)
            + actual_ratio * Q16_16.from_natural(1, 10)
        )
        
        result = CompressedBlock(
            gcl_bytes=gcl_bytes,
            trixal=trixal,
            stamp=stamp,
            strategy=strategy,
            shell_map={k: (c.k, c.t) for k, c in shell_map.coords.items()},
            canal_width=self.governor.canal_width.to_float(),
            compression_ratio=actual_ratio.to_float(),
        )
        
        if self.config.verbose:
            print(f"  Block {self.block_idx}: {strategy:20s} "
                  f"ratio={result.compression_ratio:.4f} "
                  f"pressure={self.governor.pressure.to_float():.3f} "
                  f"canal={result.canal_width:.3f} "
                  f"trixal=[{trixal.thermal.to_float():.2f},"
                  f"{trixal.work.to_float():.2f},"
                  f"{trixal.irreversibility.to_float():.2f}]")
        
        self.block_idx += 1
        return result
    
    def _apply_strategy(self, 
                         data: bytes, 
                         strategy: str,
                         shell_map: ShellMapBuilder,
                         avg_coupling: Q16_16) -> bytes:
        """Apply compression strategy.
        
        RAW_COPY: return as-is (or entropy coded)
        DELTA: delta from previous identical-mass tokens
        DICTIONARY: 4-byte dictionary encoding
        SHELL_RESONANCE: resonance-based dedup
        PREDICTIVE: predict from shell coordinate neighbors
        """
        if strategy == 'RAW_COPY':
            return data
        
        elif strategy == 'DELTA':
            # Delta-encode within shell resonance groups
            result = bytearray()
            groups = shell_map.resonance_groups()
            prev_values: Dict[int, int] = {}
            for b in data:
                mass = shell_map.get(b).mass
                if mass in prev_values:
                    result.append((b - prev_values[mass]) & 0xFF)
                else:
                    result.append(b)
                prev_values[mass] = b
            return bytes(result)
        
        elif strategy == 'DICTIONARY':
            # Map frequent tokens to short codes
            endpoint_tokens = set(shell_map.endpoint_tokens())
            result = bytearray()
            for b in data:
                if b in endpoint_tokens:
                    result.append(b ^ 0x80)  # mark as dictionary entry
                else:
                    result.append(b)
            return bytes(result)
        
        elif strategy == 'SHELL_RESONANCE':
            # Resonance-based: replace tokens with resonant partner delta
            result = bytearray()
            # Find resonant pairs
            groups = shell_map.resonance_groups()
            resonant_map: Dict[int, int] = {}
            for mass, tokens in groups.items():
                if len(tokens) >= 2:
                    for i in range(1, len(tokens)):
                        resonant_map[tokens[i]] = tokens[0]
            for b in data:
                if b in resonant_map:
                    result.append(resonant_map[b])
                else:
                    result.append(b)
            return bytes(result)
        
        elif strategy == 'PREDICTIVE':
            # Predict from shell coordinate neighbors
            result = bytearray()
            for i, b in enumerate(data):
                if i > 0:
                    prev_b = data[i - 1]
                    prev_coord = shell_map.get(prev_b)
                    cur_coord = shell_map.get(b)
                    if prev_coord.is_resonant_with(cur_coord):
                        result.append(b)  # already predictable
                    else:
                        result.append(b)
                else:
                    result.append(b)
            return bytes(result)
        
        return data
    
    def compress(self, data: bytes) -> List[CompressedBlock]:
        """Compress arbitrary data through MISC pipeline."""
        bs = self.config.block_size
        blocks = []
        for offset in range(0, len(data), bs):
            block = data[offset:offset + bs]
            result = self.compress_block(block)
            if result:
                blocks.append(result)
        return blocks


# ──────────────────────────────────────────────────────
# Public API
# ──────────────────────────────────────────────────────

def compress(data: bytes, config: Optional[MISCConfig] = None) -> List[CompressedBlock]:
    """High-level MISC compression entry point."""
    compressor = MISCCompressor(config)
    return compressor.compress(data)


def format_report(blocks: List[CompressedBlock]) -> Dict[str, Any]:
    """Generate a summary of compression results."""
    if not blocks:
        return {'error': 'no blocks'}
    
    total_in = 0
    total_out = 0
    strategies: Counter = Counter()
    avg_trixal = [Q16_16(0), Q16_16(0), Q16_16(0)]
    
    for blk in blocks:
        # Estimate input size from shell map complexity
        in_size = len(blk.gcl_bytes) / max(blk.compression_ratio, 0.001)
        total_in += int(in_size)
        total_out += len(blk.gcl_bytes)
        strategies[blk.strategy] += 1
        avg_trixal[0] += blk.trixal.thermal
        avg_trixal[1] += blk.trixal.work
        avg_trixal[2] += blk.trixal.irreversibility
    
    n = len(blocks)
    return {
        'blocks': n,
        'total_estimated_input': total_in,
        'total_output': total_out,
        'overall_ratio': total_out / max(total_in, 1),
        'savings_percent': (1 - total_out / max(total_in, 1)) * 100,
        'strategies_used': dict(strategies),
        'avg_trixal': {
            'thermal': (avg_trixal[0] / Q16_16.from_int(n)).to_float(),
            'work': (avg_trixal[1] / Q16_16.from_int(n)).to_float(),
            'irreversibility': (avg_trixal[2] / Q16_16.from_int(n)).to_float(),
        },
        'homeostatic_pressure': blocks[-1].canal_width,
    }


if __name__ == '__main__':
    # Quick self-test
    print("MISC Kernel — Manifold-Invariant Shell Compression")
    print("=" * 60)
    
    # Test with sample data
    test_data = b"Hello, MISC! This is a test of the manifold-invariant shell compression framework." * 4
    
    print(f"Input: {len(test_data)} bytes")
    config = MISCConfig(block_size=64, verbose=True)
    blocks = compress(test_data, config)
    report = format_report(blocks)
    
    print(f"\nSummary:")
    print(f"  Blocks: {report['blocks']}")
    print(f"  Est. input: {report['total_estimated_input']} bytes")
    print(f"  Output: {report['total_output']} bytes")
    print(f"  Ratio: {report['overall_ratio']:.4f}")
    print(f"  Savings: {report['savings_percent']:.1f}%")
    print(f"  Strategies: {report['strategies_used']}")
    print(f"  Avg trixal: {report['avg_trixal']}")