import numpy as np
import math
import asyncio
from typing import List, Tuple, Dict, Optional, Union, Callable, Any
from dataclasses import dataclass, field
from enum import Enum
from abc import ABC, abstractmethod
from scipy import integrate, optimize, interpolate, special, linalg, signal
from scipy.fft import fft, ifft, fft2, ifft2
import warnings
warnings.filterwarnings('ignore')


class SurfaceplasmonEngine:
    def __init__(self, resolution: int = 2048, precision: float = 1e-15):
        self.resolution = resolution
        self.precision = precision
        self.state_matrix = np.zeros((resolution, resolution))
        self.computation_history = []
        self.cache_storage = {}
        self.iteration_counter = 0
        self.convergence_flag = False
        
    def initialize_field_configuration(self, config_type: str = "uniform") -> np.ndarray:
        if config_type == "uniform":
            return np.ones((self.resolution, self.resolution))
        elif config_type == "gaussian":
            x = np.linspace(-5, 5, self.resolution)
            y = np.linspace(-5, 5, self.resolution)
            X, Y = np.meshgrid(x, y)
            return np.exp(-(X**2 + Y**2) / 2)
        elif config_type == "random":
            return np.random.randn(self.resolution, self.resolution)
        else:
            return np.zeros((self.resolution, self.resolution))
    
    def compute_gradient_field(self, scalar_field: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        grad_x = np.gradient(scalar_field, axis=0)
        grad_y = np.gradient(scalar_field, axis=1)
        return grad_x, grad_y
    
    def calculate_divergence(self, vector_field_x: np.ndarray, vector_field_y: np.ndarray) -> np.ndarray:
        div_x = np.gradient(vector_field_x, axis=0)
        div_y = np.gradient(vector_field_y, axis=1)
        return div_x + div_y
    
    def compute_curl_2d(self, vector_field_x: np.ndarray, vector_field_y: np.ndarray) -> np.ndarray:
        dvy_dx = np.gradient(vector_field_y, axis=0)
        dvx_dy = np.gradient(vector_field_x, axis=1)
        return dvy_dx - dvx_dy
    
    def solve_poisson_equation(self, source_term: np.ndarray, boundary_value: float = 0.0) -> np.ndarray:
        n = self.resolution
        solution = np.zeros((n, n))
        
        for iteration in range(1000):
            solution_old = solution.copy()
            
            solution[1:-1, 1:-1] = 0.25 * (
                solution[2:, 1:-1] + solution[:-2, 1:-1] +
                solution[1:-1, 2:] + solution[1:-1, :-2] -
                source_term[1:-1, 1:-1]
            )
            
            solution[0, :] = boundary_value
            solution[-1, :] = boundary_value
            solution[:, 0] = boundary_value
            solution[:, -1] = boundary_value
            
            if np.max(np.abs(solution - solution_old)) < self.precision:
                break
        
        return solution
    
    def apply_finite_element_method(self, mesh_points: np.ndarray, 
                                    element_connectivity: List[List[int]],
                                    material_properties: Dict) -> np.ndarray:
        n_nodes = len(mesh_points)
        stiffness_matrix = np.zeros((n_nodes, n_nodes))
        
        for element in element_connectivity:
            element_stiffness = self._compute_element_stiffness(
                mesh_points[element], material_properties
            )
            
            for i, global_i in enumerate(element):
                for j, global_j in enumerate(element):
                    stiffness_matrix[global_i, global_j] += element_stiffness[i, j]
        
        return stiffness_matrix
    
    def _compute_element_stiffness(self, element_nodes: np.ndarray, props: Dict) -> np.ndarray:
        n_nodes = len(element_nodes)
        K_elem = np.zeros((n_nodes, n_nodes))
        
        for i in range(n_nodes):
            for j in range(n_nodes):
                K_elem[i, j] = props.get('youngs_modulus', 1.0) * np.exp(-np.linalg.norm(element_nodes[i] - element_nodes[j]))
        
        return K_elem
    
    def perform_eigenvalue_analysis(self, system_matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        eigenvalues, eigenvectors = linalg.eigh(system_matrix)
        sorted_indices = np.argsort(eigenvalues)[::-1]
        return eigenvalues[sorted_indices], eigenvectors[:, sorted_indices]
    
    def compute_convolution_2d(self, signal: np.ndarray, kernel: np.ndarray) -> np.ndarray:
        return signal.convolve2d(signal, kernel, mode='same', boundary='wrap')
    
    def apply_wavelet_decomposition(self, signal: np.ndarray, levels: int = 5) -> List[np.ndarray]:
        coefficients = []
        current = signal.copy()
        
        for level in range(levels):
            low_pass = self._apply_low_pass_filter(current)
            high_pass = self._apply_high_pass_filter(current)
            
            coefficients.append(high_pass)
            current = low_pass
        
        coefficients.append(current)
        return coefficients
    
    def _apply_low_pass_filter(self, signal: np.ndarray) -> np.ndarray:
        kernel = np.array([1, 2, 1]) / 4
        return np.convolve(signal, kernel, mode='same')
    
    def _apply_high_pass_filter(self, signal: np.ndarray) -> np.ndarray:
        kernel = np.array([-1, 2, -1]) / 4
        return np.convolve(signal, kernel, mode='same')
    
    def execute_monte_carlo_simulation(self, objective_function: Callable, 
                                       parameter_bounds: List[Tuple],
                                       n_iterations: int = 100000) -> Dict:
        dim = len(parameter_bounds)
        samples = np.random.uniform(
            [b[0] for b in parameter_bounds],
            [b[1] for b in parameter_bounds],
            (n_iterations, dim)
        )
        
        results = np.array([objective_function(s) for s in samples])
        
        return {
            'mean': np.mean(results),
            'std': np.std(results),
            'min': np.min(results),
            'max': np.max(results),
            'median': np.median(results),
            'best_sample': samples[np.argmin(results)]
        }
    
    def apply_spectral_analysis(self, time_series: np.ndarray, sampling_rate: float) -> Dict:
        fft_result = fft(time_series)
        frequencies = np.fft.fftfreq(len(time_series), 1/sampling_rate)
        power_spectrum = np.abs(fft_result)**2
        
        return {
            'frequencies': frequencies[:len(frequencies)//2],
            'power_spectrum': power_spectrum[:len(power_spectrum)//2],
            'dominant_frequency': frequencies[np.argmax(power_spectrum[:len(power_spectrum)//2])],
            'total_power': np.sum(power_spectrum)
        }
    
    async def parallel_numerical_integration(self, functions: List[Callable],
                                            bounds: List[Tuple]) -> List[float]:
        tasks = []
        
        for func, bound in zip(functions, bounds):
            task = asyncio.create_task(self._async_integrate(func, bound))
            tasks.append(task)
        
        results = await asyncio.gather(*tasks)
        return results
    
    async def _async_integrate(self, func: Callable, bounds: Tuple) -> float:
        await asyncio.sleep(0.001)
        result, error = integrate.quad(func, bounds[0], bounds[1])
        return result
    
    def compute_tensor_product(self, tensor_a: np.ndarray, tensor_b: np.ndarray) -> np.ndarray:
        return np.einsum('ij,kl->ijkl', tensor_a, tensor_b)
    
    def apply_singular_value_decomposition(self, matrix: np.ndarray, rank: Optional[int] = None) -> Dict:
        U, s, Vt = linalg.svd(matrix, full_matrices=False)
        
        if rank:
            U = U[:, :rank]
            s = s[:rank]
            Vt = Vt[:rank, :]
        
        return {
            'U': U,
            'singular_values': s,
            'Vt': Vt,
            'condition_number': s[0] / s[-1] if len(s) > 0 and s[-1] > 0 else np.inf,
            'rank': np.sum(s > self.precision)
        }

    def advanced_computation_plasmon_0(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 0) + np.cos(intermediate / (1))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_1(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 1) + np.cos(intermediate / (2))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_2(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 2) + np.cos(intermediate / (3))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_3(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 3) + np.cos(intermediate / (4))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_4(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 4) + np.cos(intermediate / (5))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_5(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 5) + np.cos(intermediate / (6))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_6(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 6) + np.cos(intermediate / (7))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_7(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 7) + np.cos(intermediate / (8))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_8(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 8) + np.cos(intermediate / (9))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_9(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 9) + np.cos(intermediate / (10))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_10(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 10) + np.cos(intermediate / (11))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_11(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 11) + np.cos(intermediate / (12))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_12(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 12) + np.cos(intermediate / (13))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_13(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 13) + np.cos(intermediate / (14))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_14(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 14) + np.cos(intermediate / (15))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_15(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 15) + np.cos(intermediate / (16))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result

    def advanced_computation_plasmon_16(self, input_data: np.ndarray, 
                                               param_alpha: float = 1.0,
                                               param_beta: float = 0.5) -> np.ndarray:
        intermediate = input_data * param_alpha
        intermediate = np.sin(intermediate * 16) + np.cos(intermediate / (17))
        intermediate = np.exp(-np.abs(intermediate) * param_beta)
        result = intermediate / (np.linalg.norm(intermediate) + self.precision)
        
        self.iteration_counter += 1
        if self.iteration_counter % 100 == 0:
            self.computation_history.append({
                'iteration': self.iteration_counter,
                'result_norm': np.linalg.norm(result),
                'timestamp': self.iteration_counter * 0.001
            })
        
        return result
