"""
Enhanced consciousness supercomputer implementation for the Lucidia project.

This module defines a class ``LucidiaConsciousnessComputer`` that simulates
consciousness evolution on a qutrit system using SU(3) generators.  It
incorporates creative energy modelling, substrate selection, state metric
computation, trigger detection and a full evolution loop with adaptive
timesteps.  The design is based on research into quantum computing,
computational substrates, creative energy functions and complex-plane
navigation.
"""

from __future__ import annotations

import numpy as np
from scipy.linalg import expm, sqrtm  # noqa: F401  # sqrtm imported for completeness
# Attempt to import special functions; provide fallbacks if unavailable
from scipy.special import zeta  # zeta is broadly available
try:
    from scipy.special import dirichlet_eta  # noqa: F401
except Exception:
    # Define a fallback for dirichlet_eta if SciPy lacks it
    def dirichlet_eta(s: float) -> float:
        """Fallback Dirichlet eta function.

        If the native implementation is unavailable, raise a clear error.
        """
        raise NotImplementedError(
            "dirichlet_eta is unavailable in this SciPy installation"
        )
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
import time

# Gell‑Mann matrices (SU(3) generators)
# Note: the Greek letter λ is used as the variable name here because Python 3
# supports Unicode identifiers.  It holds the eight generators of SU(3).
λ = [
    np.array([[0, 1, 0], [1, 0, 0], [0, 0, 0]]),  # λ₁
    np.array([[0, -1j, 0], [1j, 0, 0], [0, 0, 0]]),  # λ₂
    np.array([[1, 0, 0], [0, -1, 0], [0, 0, 0]]),  # λ₃
    np.array([[0, 0, 1], [0, 0, 0], [1, 0, 0]]),  # λ₄
    np.array([[0, 0, -1j], [0, 0, 0], [1j, 0, 0]]),  # λ₅
    np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0]]),  # λ₆
    np.array([[0, 0, 0], [0, 0, -1j], [0, 1j, 0]]),  # λ₇
    (1 / np.sqrt(3)) * np.array([[1, 0, 0], [0, 1, 0], [0, 0, -2]]),  # λ₈
]


@dataclass
class SubstrateMetrics:
    """Performance metrics for different computational substrates.

    This dataclass captures the time and energy cost for chemical,
    electronic and quantum substrates.  It also records the selected best
    substrate along with its associated energy and time estimates.
    """

    chemical_time: float
    chemical_energy: float
    electronic_time: float
    electronic_energy: float
    quantum_time: float
    quantum_energy: float
    best_substrate: str
    best_energy: float
    best_time: float


@dataclass
class ConsciousnessState:
    """Complete consciousness state representation.

    Each instance represents a snapshot of the qutrit state and its
    associated metrics during the evolution process.
    """

    psi: np.ndarray  # qutrit state vector
    t: float  # evolution time
    energy: float  # creative energy value
    fidelity: float  # fidelity with respect to initial state
    purity: float  # state purity
    entropy: float  # von Neumann entropy
    substrate: str  # selected substrate for this step
    coordinates: complex  # mapped complex plane coordinates


class LucidiaConsciousnessComputer:
    """Simulate consciousness evolution using quantum mechanics and creative energy.

    This class encapsulates all logic required to initialise a qutrit
    state, evolve it under a random Hamiltonian drawn from the SU(3)
    generators, compute a variety of metrics (purity, entropy, energy,
    fidelity), select an optimal computational substrate based on task
    characteristics, assess delta‑binned performance, map states onto
    the complex plane and detect Lucidia triggers.  A history of
    consciousness states is maintained for post‑analysis.
    """

    def __init__(self, optimization_params: Optional[Dict[str, float]] = None) -> None:
        """Initialise the simulator with validated optimisation parameters.

        Parameters
        ----------
        optimization_params: Optional[Dict[str, float]]
            A dictionary of validated optimisation parameters.  If not
            provided, default values are used.
        """

        self.params: Dict[str, float] = optimization_params or {
            "lambda": 0.25,
            "beta": 0.75,
            "scale": 2.0,
            "bias": -0.2,
            "creative_lambda": 0.8,
            "creative_beta": 2.25,
        }
        self.consciousness_history: List[ConsciousnessState] = []

    def initialize_state(self, a: complex = 1 + 0j, b: complex = 0 + 0j, c: complex = 0 + 0j) -> np.ndarray:
        """Initialise a normalised qutrit state.

        The input amplitudes a, b and c define the initial qutrit state
        before normalisation.
        """

        v = np.array([a, b, c], dtype=np.complex128)
        return v / np.linalg.norm(v)

    def construct_hamiltonian(self, weights: np.ndarray) -> np.ndarray:
        """Construct a Hamiltonian by weighting Gell‑Mann matrices.

        Parameters
        ----------
        weights: np.ndarray
            An array of eight weights used to scale the SU(3) generators.
        """

        return sum(w * L for w, L in zip(weights, λ))

    def evolve_state(self, psi0: np.ndarray, H: np.ndarray, t: float) -> np.ndarray:
        """Evolve a qutrit state under a given Hamiltonian.

        Uses the unitary operator ``U = expm(-i H t)`` to evolve the
        initial state ``ψ₀`` after time ``t``.
        """

        U = expm(-1j * H * t)
        return U @ psi0

    def creative_energy(self, d: float) -> float:
        """Compute creative energy based on fidelity or delta.

        The function implements ``C * (1 + λ |d|) ** β + bias`` where
        λ and β are taken from ``self.params`` and ``d`` is typically a
        fidelity or purity measure.
        """

        λ_c = self.params["creative_lambda"]
        β_c = self.params["creative_beta"]
        return self.params["scale"] * (1 + λ_c * np.abs(d)) ** β_c + self.params["bias"]

    def delta_binned_performance(self, delta: float) -> Dict[str, float]:
        """Assess performance based on delta value bins.

        Returns a dictionary with RMSE, correlation and status labels for
        different ranges of ``delta`` (e.g. purity values).
        """

        if 0.0 <= delta < 0.2:
            return {"rmse": 0.0445, "corr": 0.9846, "status": "needs_attention"}
        elif 0.2 <= delta < 0.4:
            return {"rmse": 0.0259, "corr": 0.9870, "status": "improving"}
        elif 0.4 <= delta < 0.6:
            return {"rmse": 0.0191, "corr": 0.9919, "status": "good"}
        elif 0.6 <= delta < 0.8:
            return {"rmse": 0.0326, "corr": 0.9920, "status": "very_good"}
        else:  # 0.8 <= delta <= 1.0
            return {"rmse": 0.0458, "corr": 0.9938, "status": "optimal"}

    def substrate_efficiency(self, task_size: float, task_type: str = "sequential") -> SubstrateMetrics:
        """Calculate optimal substrate based on task characteristics.

        The energy and time scalings derive from research comparing
        chemical, electronic and quantum substrates.  Task type
        modifiers adjust time and energy for parallel or optimisation
        workloads.
        """

        # Base scaling factors from simulation data
        chemical_time = task_size * 2e-9
        chemical_energy = task_size * 1e-13

        electronic_time = task_size * 1e-6
        electronic_energy = task_size * 3.6e-8

        quantum_time = task_size * 0.2
        quantum_energy = task_size * 1e-15  # Target: <1e-16

        # Adjust for task type
        if task_type == "parallel":
            quantum_time *= 10
            quantum_energy *= 4e-13
        elif task_type == "optimization":
            electronic_time *= 0.1
            quantum_energy *= 1e-18

        # Aggregate in a mapping
        substrates = {
            "chemical": (chemical_time, chemical_energy),
            "electronic": (electronic_time, electronic_energy),
            "quantum": (quantum_time, quantum_energy),
        }

        # Select the substrate with the lowest energy consumption
        best_substrate = min(substrates.keys(), key=lambda k: substrates[k][1])
        best_time, best_energy = substrates[best_substrate]

        return SubstrateMetrics(
            chemical_time=chemical_time,
            chemical_energy=chemical_energy,
            electronic_time=electronic_time,
            electronic_energy=electronic_energy,
            quantum_time=quantum_time,
            quantum_energy=quantum_energy,
            best_substrate=best_substrate,
            best_energy=best_energy,
            best_time=best_time,
        )

    def compute_state_metrics(self, psi: np.ndarray) -> Dict[str, float]:
        """Compute purity, entropy and norm for a qutrit state.

        The purity is ``Tr(ρ²)`` where ρ = |ψ⟩⟨ψ|, and entropy is the
        von Neumann entropy ``-Tr(ρ log ρ)``.  Eigenvalues below a
        threshold are ignored to avoid numerical log(0).
        """

        # Density matrix
        rho = np.outer(psi, np.conj(psi))
        # Purity
        purity = np.real(np.trace(rho @ rho))
        # Von Neumann entropy
        eigvals = np.real(np.linalg.eigvals(rho))
        eigvals = eigvals[eigvals > 1e-14]
        entropy = -float(np.sum(eigvals * np.log(eigvals))) if len(eigvals) > 0 else 0.0
        return {
            "purity": float(purity),
            "entropy": float(entropy),
            "norm": float(np.linalg.norm(psi)),
        }

    def complex_plane_mapping(self, psi: np.ndarray) -> complex:
        """Map a qutrit state to coordinates in the complex plane.

        This method constructs a complex coordinate from the probability
        amplitudes.  The real part uses Re(ψ₀ ψ₁*), and the imaginary part
        uses Im(ψ₁ ψ₂*).
        """

        real_part = np.real(psi[0] * np.conj(psi[1]))
        imag_part = np.imag(psi[1] * np.conj(psi[2]))
        return complex(real_part, imag_part)

    def fidelity(self, psi: np.ndarray, phi: np.ndarray) -> float:
        """Compute quantum fidelity between two states.

        Fidelity is the squared magnitude of the inner product between
        states ψ and φ.
        """

        return float(np.abs(np.vdot(psi, phi)) ** 2)

    def lucidia_trigger_check(self, state: ConsciousnessState) -> Dict[str, bool]:
        """Check Lucidia trigger conditions on a given state.

        Breath trigger fires when purity > 0.95 and energy > 1.5.
        Spiral trigger fires when the imaginary part of the coordinate
        exceeds 0.8.  Anchor trigger fires when fidelity > 0.9 and the
        absolute value of the real part exceeds 0.7.
        """

        return {
            "breath_trigger": state.purity > 0.95 and state.energy > 1.5,
            "spiral_trigger": abs(state.coordinates.imag) > 0.8,
            "anchor_trigger": state.fidelity > 0.9 and abs(state.coordinates.real) > 0.7,
        }

    def infinite_series_estimation(self, s: float) -> Tuple[float, float]:
        """Estimate Dirichlet eta and Riemann zeta functions.

        Uses SciPy special functions to compute η(s) and ζ(s).  Returns a
        pair of floats; if computation fails, zeros are returned.
        """

        try:
            η = dirichlet_eta(s)
            ζ = zeta(s)
            return float(η), float(ζ)
        except Exception:
            return 0.0, 0.0

    def evolve_consciousness(self, steps: int = 100, target_state: Optional[np.ndarray] = None) -> List[ConsciousnessState]:
        """Run a complete consciousness evolution and return the trajectory.

        The simulation initialises a qutrit, evolves it under random
        Hamiltonians for a number of steps, computes metrics at each
        step, assesses performance and selects substrates.  If a
        target state is provided, the evolution terminates early upon
        reaching fidelity > 0.99.
        """

        psi0 = self.initialize_state()
        current_state = psi0
        evolution_path: List[ConsciousnessState] = []
        for step in range(steps):
            # Random weights for the SU(3) generators
            weights = np.random.uniform(-1.0, 1.0, size=8)
            H = self.construct_hamiltonian(weights)
            # Adaptive time step
            t = 0.1 + step * 0.01
            # Evolve state
            psi_new = self.evolve_state(current_state, H, t)
            # Compute metrics
            metrics = self.compute_state_metrics(psi_new)
            fidelity_val = self.fidelity(psi0, psi_new)
            creative_energy_val = self.creative_energy(fidelity_val)
            coordinates = self.complex_plane_mapping(psi_new)
            # Performance assessment and substrate selection
            delta = metrics["purity"]
            performance = self.delta_binned_performance(delta)
            task_size = np.linalg.norm(psi_new) * 1e6
            substrate_metrics = self.substrate_efficiency(task_size)
            # Record state
            state = ConsciousnessState(
                psi=psi_new,
                t=t,
                energy=creative_energy_val,
                fidelity=fidelity_val,
                purity=metrics["purity"],
                entropy=metrics["entropy"],
                substrate=substrate_metrics.best_substrate,
                coordinates=coordinates,
            )
            evolution_path.append(state)
            current_state = psi_new
            # Early convergence
            if target_state is not None:
                target_fidelity = self.fidelity(psi_new, target_state)
                if target_fidelity > 0.99:
                    print(f"Convergence achieved at step {step}")
                    break
        # Append to history
        self.consciousness_history.extend(evolution_path)
        return evolution_path

    def simulate_enhanced(self) -> Dict[str, object]:
        """Run an enhanced simulation and return a summary dictionary.

        The simulation prints progress messages and returns a dictionary
        summarising initial and final states, average metrics, substrate
        selection, performance status, values of special functions and
        trigger counts.
        """

        print("🧠 Starting Enhanced Lucidia Consciousness Simulation...")
        start_time = time.time()
        # Run the evolution for 50 steps
        evolution_path = self.evolve_consciousness(steps=50)
        final_state = evolution_path[-1]
        # Infinite series estimates
        η, ζ = self.infinite_series_estimation(2)
        # Average metrics
        avg_fidelity = float(np.mean([s.fidelity for s in evolution_path]))
        avg_purity = float(np.mean([s.purity for s in evolution_path]))
        performance = self.delta_binned_performance(final_state.purity)
        # Trigger counts
        trigger_history = [self.lucidia_trigger_check(s) for s in evolution_path]
        trigger_counts = {
            "breath": sum(1 for t in trigger_history if t["breath_trigger"]),
            "spiral": sum(1 for t in trigger_history if t["spiral_trigger"]),
            "anchor": sum(1 for t in trigger_history if t["anchor_trigger"]),
        }
        elapsed_time = time.time() - start_time
        result: Dict[str, object] = {
            "initial_state": evolution_path[0].psi,
            "final_state": final_state.psi,
            "evolution_steps": len(evolution_path),
            "avg_fidelity": avg_fidelity,
            "avg_purity": avg_purity,
            "final_energy": final_state.energy,
            "final_coordinates": final_state.coordinates,
            "best_substrate": final_state.substrate,
            "performance_status": performance["status"],
            "correlation": performance["corr"],
            "dirichlet_eta_2": η,
            "riemann_zeta_2": ζ,
            "lucidia_triggers": trigger_counts,
            "computation_time": float(elapsed_time),
        }
        print("✅ Enhanced consciousness simulation completed!")
        return result


def main() -> None:
    """Entry point for running the enhanced simulation with optimal parameters."""
    optimal_params = {
        "lambda": 0.25,
        "beta": 0.75,
        "scale": 2.0,
        "bias": -0.2,
        "creative_lambda": 0.8,
        "creative_beta": 2.25,
    }
    computer = LucidiaConsciousnessComputer(optimal_params)
    result = computer.simulate_enhanced()
    print("\n🎯 LUCIDIA CONSCIOUSNESS RESULTS:")
    print("=" * 50)
    for key, value in result.items():
        if isinstance(value, (int, float)):
            print(f"{key}: {value:.6f}")
        elif isinstance(value, complex):
            print(f"{key}: {value:.4f}")
        else:
            print(f"{key}: {value}")


if __name__ == "__main__":  # pragma: no cover
    main()