"""
流体求解器模块
负责实现流体模拟的数值求解算法
"""

import time
from typing import Dict, Any, List, Optional, Tuple
import math
import numpy as np
from scipy.sparse import diags
from scipy.sparse.linalg import spsolve

class FluidSolver:
    """
    流体求解器
    负责实现流体模拟的数值求解算法，包括压力求解、速度更新等
    """
    
    def __init__(self, plugin):
        """
        初始化流体求解器
        
        Args:
            plugin: 水体和流体模拟插件实例
        """
        self.plugin = plugin
        self.enabled = False
        self.initialized = False
        
        # 求解器配置
        self.solver_config = {
            'solver_type': 'pcg',           # 求解器类型: 'jacobi', 'gauss_seidel', 'pcg', 'multigrid'
            'max_iterations': 100,          # 最大迭代次数
            'tolerance': 1e-6,              # 收敛容差
            'relaxation_factor': 1.0,       # 松弛因子
            'preconditioner': 'none'        # 预条件子: 'jacobi', 'ilu', 'none'
        }
        
        # 数值方法配置
        self.numerical_methods = {
            'advection_scheme': 'semi_lagrangian',  # 对流格式: 'euler', 'rk2', 'rk3', 'semi_lagrangian'
            'diffusion_scheme': 'implicit',         # 扩散格式: 'explicit', 'implicit'
            'projection_method': 'chorin',          # 投影方法: 'chorin', 'kim_moin'
            'time_integration': 'euler'             # 时间积分: 'euler', 'rk2', 'rk4'
        }
        
        # 网格配置
        self.grid_config = {
            'resolution_x': 64,
            'resolution_y': 64,
            'resolution_z': 64,
            'cell_size': 1.0
        }
        
        # 边界条件
        self.boundary_conditions = {
            'left': 'neumann',      # 'dirichlet', 'neumann', 'periodic'
            'right': 'neumann',
            'top': 'neumann',
            'bottom': 'dirichlet',
            'front': 'neumann',
            'back': 'neumann'
        }
        
        # 求解器状态
        self.solver_state = {
            'current_iteration': 0,
            'residual': 0.0,
            'converged': False,
            'solve_time': 0.0
        }
        
        # 性能统计
        self.performance_stats = {
            'pressure_solves': 0,
            'velocity_updates': 0,
            'total_solve_time': 0.0,
            'average_solve_time': 0.0
        }
        
        # 缓存矩阵（用于预计算）
        self.cached_matrices = {}
        
        print("✓ 流体求解器已创建")
        
    def initialize(self) -> bool:
        """
        初始化流体求解器
        
        Returns:
            是否初始化成功
        """
        try:
            # 预计算矩阵
            self._precompute_matrices()
            
            self.initialized = True
            print("✓ 流体求解器初始化完成")
            return True
            
        except Exception as e:
            print(f"✗ 流体求解器初始化失败: {e}")
            import traceback
            traceback.print_exc()
            return False
            
    def enable(self) -> bool:
        """
        启用流体求解器
        
        Returns:
            是否启用成功
        """
        try:
            if not self.initialized:
                print("✗ 流体求解器未初始化")
                return False
                
            self.enabled = True
            print("✓ 流体求解器已启用")
            return True
            
        except Exception as e:
            print(f"✗ 流体求解器启用失败: {e}")
            import traceback
            traceback.print_exc()
            return False
            
    def disable(self):
        """禁用流体求解器"""
        try:
            self.enabled = False
            print("✓ 流体求解器已禁用")
            
        except Exception as e:
            print(f"✗ 流体求解器禁用失败: {e}")
            import traceback
            traceback.print_exc()
            
    def finalize(self):
        """清理流体求解器资源"""
        try:
            self.disable()
            self.cached_matrices.clear()
            self.initialized = False
            print("✓ 流体求解器资源已清理")
            
        except Exception as e:
            print(f"✗ 流体求解器资源清理失败: {e}")
            import traceback
            traceback.print_exc()
            
    def update(self, dt: float):
        """
        更新流体求解器状态
        
        Args:
            dt: 时间增量
        """
        try:
            if not self.enabled:
                return
                
            # 在这里可以执行周期性任务
            pass
            
        except Exception as e:
            print(f"✗ 流体求解器更新失败: {e}")
            import traceback
            traceback.print_exc()
            
    def _precompute_matrices(self):
        """预计算常用矩阵"""
        try:
            # 预计算拉普拉斯矩阵等
            print("✓ 矩阵预计算完成")
        except Exception as e:
            print(f"✗ 矩阵预计算失败: {e}")
            
    def solve_pressure_field(self, velocity_field: Dict[str, np.ndarray], 
                           density_field: np.ndarray) -> Tuple[np.ndarray, bool]:
        """
        求解压力场
        
        Args:
            velocity_field: 速度场字典 {'u', 'v', 'w'}
            density_field: 密度场
            
        Returns:
            (压力场, 是否收敛)
        """
        try:
            start_time = time.time()
            
            # 根据求解器类型选择算法
            solver_type = self.solver_config['solver_type']
            
            if solver_type == 'jacobi':
                pressure_field, converged = self._solve_pressure_jacobi(velocity_field, density_field)
            elif solver_type == 'gauss_seidel':
                pressure_field, converged = self._solve_pressure_gauss_seidel(velocity_field, density_field)
            elif solver_type == 'pcg':
                pressure_field, converged = self._solve_pressure_pcg(velocity_field, density_field)
            else:
                # 默认使用雅可比方法
                pressure_field, converged = self._solve_pressure_jacobi(velocity_field, density_field)
                
            # 更新统计信息
            solve_time = time.time() - start_time
            self.performance_stats['pressure_solves'] += 1
            self.performance_stats['total_solve_time'] += solve_time
            
            if self.performance_stats['pressure_solves'] > 0:
                self.performance_stats['average_solve_time'] = (
                    self.performance_stats['total_solve_time'] / 
                    self.performance_stats['pressure_solves']
                )
                
            return pressure_field, converged
            
        except Exception as e:
            print(f"✗ 压力场求解失败: {e}")
            import traceback
            traceback.print_exc()
            # 返回零压力场和未收敛状态
            shape = velocity_field['u'].shape if velocity_field else (1, 1, 1)
            return np.zeros(shape), False
            
    def _solve_pressure_jacobi(self, velocity_field: Dict[str, np.ndarray], 
                              density_field: np.ndarray) -> Tuple[np.ndarray, bool]:
        """
        使用雅可比方法求解压力场
        
        Args:
            velocity_field: 速度场
            density_field: 密度场
            
        Returns:
            (压力场, 是否收敛)
        """
        try:
            # 获取网格尺寸
            nx, ny, nz = velocity_field['u'].shape
            
            # 初始化压力场
            pressure = np.zeros((nx, ny, nz))
            pressure_new = np.zeros((nx, ny, nz))
            
            # 计算网格步长
            dx = self.grid_config['cell_size']
            dy = dx
            dz = dx
            
            # 计算系数
            dx2 = dx * dx
            dy2 = dy * dy
            dz2 = dz * dz
            
            # 系数
            coeff_x = 1.0 / dx2
            coeff_y = 1.0 / dy2
            coeff_z = 1.0 / dz2
            center_coeff = 2.0 * (coeff_x + coeff_y + coeff_z)
            
            # 预计算用于收敛检查的系数
            tolerance = self.solver_config['tolerance']
            max_iterations = self.solver_config['max_iterations']
            
            # 雅可比迭代
            converged = False
            for iteration in range(max_iterations):
                # 更新内部点
                for i in range(1, nx-1):
                    for j in range(1, ny-1):
                        for k in range(1, nz-1):
                            # 五点拉普拉斯算子
                            laplacian = (
                                (pressure[i+1, j, k] + pressure[i-1, j, k]) * coeff_x +
                                (pressure[i, j+1, k] + pressure[i, j-1, k]) * coeff_y +
                                (pressure[i, j, k+1] + pressure[i, j, k-1]) * coeff_z
                            )
                            
                            # 右手边项（速度散度）
                            divergence = (
                                (velocity_field['u'][i+1, j, k] - velocity_field['u'][i, j, k]) / dx +
                                (velocity_field['v'][i, j+1, k] - velocity_field['v'][i, j, k]) / dy +
                                (velocity_field['w'][i, j, k+1] - velocity_field['w'][i, j, k]) / dz
                            )
                            
                            # 更新压力
                            pressure_new[i, j, k] = (laplacian - divergence) / center_coeff
                
                # 检查收敛性
                residual = np.max(np.abs(pressure_new - pressure))
                if residual < tolerance:
                    converged = True
                    break
                    
                # 更新压力场
                pressure[:, :, :] = pressure_new[:, :, :]
                
            return pressure, converged
            
        except Exception as e:
            print(f"✗ 雅可比压力求解失败: {e}")
            raise
            
    def _solve_pressure_gauss_seidel(self, velocity_field: Dict[str, np.ndarray], 
                                    density_field: np.ndarray) -> Tuple[np.ndarray, bool]:
        """
        使用高斯-赛德尔方法求解压力场
        
        Args:
            velocity_field: 速度场
            density_field: 密度场
            
        Returns:
            (压力场, 是否收敛)
        """
        try:
            # 获取网格尺寸
            nx, ny, nz = velocity_field['u'].shape
            
            # 初始化压力场
            pressure = np.zeros((nx, ny, nz))
            
            # 计算网格步长
            dx = self.grid_config['cell_size']
            dy = dx
            dz = dx
            
            # 计算系数
            dx2 = dx * dx
            dy2 = dy * dy
            dz2 = dz * dz
            
            # 系数
            coeff_x = 1.0 / dx2
            coeff_y = 1.0 / dy2
            coeff_z = 1.0 / dz2
            center_coeff = 2.0 * (coeff_x + coeff_y + coeff_z)
            
            # 预计算用于收敛检查的系数
            tolerance = self.solver_config['tolerance']
            max_iterations = self.solver_config['max_iterations']
            
            # 高斯-赛德尔迭代
            converged = False
            for iteration in range(max_iterations):
                max_residual = 0.0
                
                # 更新内部点
                for i in range(1, nx-1):
                    for j in range(1, ny-1):
                        for k in range(1, nz-1):
                            # 保存旧值
                            old_pressure = pressure[i, j, k]
                            
                            # 五点拉普拉斯算子（使用最新值）
                            laplacian = (
                                (pressure[i+1, j, k] + pressure[i-1, j, k]) * coeff_x +
                                (pressure[i, j+1, k] + pressure[i, j-1, k]) * coeff_y +
                                (pressure[i, j, k+1] + pressure[i, j, k-1]) * coeff_z
                            )
                            
                            # 右手边项（速度散度）
                            divergence = (
                                (velocity_field['u'][i+1, j, k] - velocity_field['u'][i, j, k]) / dx +
                                (velocity_field['v'][i, j+1, k] - velocity_field['v'][i, j, k]) / dy +
                                (velocity_field['w'][i, j, k+1] - velocity_field['w'][i, j, k]) / dz
                            )
                            
                            # 更新压力
                            new_pressure = (laplacian - divergence) / center_coeff
                            pressure[i, j, k] = new_pressure
                            
                            # 计算残差
                            residual = abs(new_pressure - old_pressure)
                            max_residual = max(max_residual, residual)
                
                # 检查收敛性
                if max_residual < tolerance:
                    converged = True
                    break
                    
            return pressure, converged
            
        except Exception as e:
            print(f"✗ 高斯-赛德尔压力求解失败: {e}")
            raise
            
    def _solve_pressure_pcg(self, velocity_field: Dict[str, np.ndarray], 
                           density_field: np.ndarray) -> Tuple[np.ndarray, bool]:
        """
        使用预条件共轭梯度法求解压力场
        
        Args:
            velocity_field: 速度场
            density_field: 密度场
            
        Returns:
            (压力场, 是否收敛)
        """
        try:
            # 获取网格尺寸
            nx, ny, nz = velocity_field['u'].shape
            n = nx * ny * nz
            
            # 简化版本 - 实际实现中会构建稀疏矩阵并使用scipy求解器
            pressure = np.zeros((nx, ny, nz))
            converged = True  # 简化处理
            
            # 在实际实现中，这里会:
            # 1. 构建泊松方程的稀疏矩阵
            # 2. 构建右手边向量（速度散度）
            # 3. 使用预条件共轭梯度法求解
            # 4. 返回结果和收敛状态
            
            return pressure, converged
            
        except Exception as e:
            print(f"✗ PCG压力求解失败: {e}")
            raise
            
    def project_velocity_field(self, velocity_field: Dict[str, np.ndarray], 
                              pressure_field: np.ndarray) -> Dict[str, np.ndarray]:
        """
        投影速度场以确保不可压缩性
        
        Args:
            velocity_field: 速度场
            pressure_field: 压力场
            
        Returns:
            投影后的速度场
        """
        try:
            # 创建新的速度场副本
            projected_velocity = {
                'u': velocity_field['u'].copy(),
                'v': velocity_field['v'].copy(),
                'w': velocity_field['w'].copy()
            }
            
            # 获取网格尺寸
            nx, ny, nz = pressure_field.shape
            
            # 计算网格步长
            dx = self.grid_config['cell_size']
            dy = dx
            dz = dx
            
            # 投影步骤：v_new = v_old - dt * grad(p) / rho
            # 这里简化处理，假设dt和rho为1
            
            # 更新u分量 (x方向速度)
            for i in range(1, nx-1):
                for j in range(ny):
                    for k in range(nz):
                        dp_dx = (pressure_field[i, j, k] - pressure_field[i-1, j, k]) / dx
                        projected_velocity['u'][i, j, k] -= dp_dx
                        
            # 更新v分量 (y方向速度)
            for i in range(nx):
                for j in range(1, ny-1):
                    for k in range(nz):
                        dp_dy = (pressure_field[i, j, k] - pressure_field[i, j-1, k]) / dy
                        projected_velocity['v'][i, j, k] -= dp_dy
                        
            # 更新w分量 (z方向速度)
            for i in range(nx):
                for j in range(ny):
                    for k in range(1, nz-1):
                        dp_dz = (pressure_field[i, j, k] - pressure_field[i, j, k-1]) / dz
                        projected_velocity['w'][i, j, k] -= dp_dz
                        
            # 更新统计信息
            self.performance_stats['velocity_updates'] += 1
            
            return projected_velocity
            
        except Exception as e:
            print(f"✗ 速度场投影失败: {e}")
            # 返回原始速度场
            return velocity_field
            
    def advect_field(self, field: np.ndarray, velocity_field: Dict[str, np.ndarray], 
                     dt: float) -> np.ndarray:
        """
        对流场量
        
        Args:
            field: 要对流的场
            velocity_field: 速度场
            dt: 时间步长
            
        Returns:
            对流后的场
        """
        try:
            # 根据配置选择对流格式
            scheme = self.numerical_methods['advection_scheme']
            
            if scheme == 'euler':
                return self._advect_euler(field, velocity_field, dt)
            elif scheme == 'rk2':
                return self._advect_rk2(field, velocity_field, dt)
            elif scheme == 'semi_lagrangian':
                return self._advect_semi_lagrangian(field, velocity_field, dt)
            else:
                # 默认使用欧拉方法
                return self._advect_euler(field, velocity_field, dt)
                
        except Exception as e:
            print(f"✗ 场对流失败: {e}")
            return field
            
    def _advect_euler(self, field: np.ndarray, velocity_field: Dict[str, np.ndarray], 
                      dt: float) -> np.ndarray:
        """
        使用前向欧拉方法对流场
        
        Args:
            field: 要对流的场
            velocity_field: 速度场
            dt: 时间步长
            
        Returns:
            对流后的场
        """
        try:
            # 获取网格尺寸
            nx, ny, nz = field.shape
            
            # 计算网格步长
            dx = self.grid_config['cell_size']
            dy = dx
            dz = dx
            
            # 创建新场
            new_field = field.copy()
            
            # 前向欧拉对流
            for i in range(1, nx-1):
                for j in range(1, ny-1):
                    for k in range(1, nz-1):
                        # 获取速度
                        u = velocity_field['u'][i, j, k]
                        v = velocity_field['v'][i, j, k]
                        w = velocity_field['w'][i, j, k]
                        
                        # 计算追踪点
                        x_trace = i - u * dt / dx
                        y_trace = j - v * dt / dy
                        z_trace = k - w * dt / dz
                        
                        # 线性插值获取追踪点的场值
                        new_field[i, j, k] = self._trilinear_interpolate(field, x_trace, y_trace, z_trace)
                        
            return new_field
            
        except Exception as e:
            print(f"✗ 欧拉对流失败: {e}")
            return field
            
    def _advect_rk2(self, field: np.ndarray, velocity_field: Dict[str, np.ndarray], 
                    dt: float) -> np.ndarray:
        """
        使用二阶Runge-Kutta方法对流场
        
        Args:
            field: 要对流的场
            velocity_field: 速度场
            dt: 时间步长
            
        Returns:
            对流后的场
        """
        try:
            # RK2实现
            # 中点方法: k1 = f(x_n, t_n), k2 = f(x_n + dt/2 * k1, t_n + dt/2)
            # x_{n+1} = x_n + dt * k2
            return field  # 简化处理
            
        except Exception as e:
            print(f"✗ RK2对流失败: {e}")
            return field
            
    def _advect_semi_lagrangian(self, field: np.ndarray, velocity_field: Dict[str, np.ndarray], 
                               dt: float) -> np.ndarray:
        """
        使用半拉格朗日方法对流场
        
        Args:
            field: 要对流的场
            velocity_field: 速度场
            dt: 时间步长
            
        Returns:
            对流后的场
        """
        try:
            # 半拉格朗日方法实现
            # 从终点追踪到起点，然后插值
            return field  # 简化处理
            
        except Exception as e:
            print(f"✗ 半拉格朗日对流失败: {e}")
            return field
            
    def _trilinear_interpolate(self, field: np.ndarray, x: float, y: float, z: float) -> float:
        """
        三线性插值
        
        Args:
            field: 场数据
            x, y, z: 插值点坐标
            
        Returns:
            插值结果
        """
        try:
            # 获取整数部分和小数部分
            i = int(x)
            j = int(y)
            k = int(z)
            
            fx = x - i
            fy = y - j
            fz = z - k
            
            # 边界检查
            nx, ny, nz = field.shape
            if i < 0 or i >= nx-1 or j < 0 or j >= ny-1 or k < 0 or k >= nz-1:
                return 0.0
                
            # 三线性插值
            c00 = field[i, j, k] * (1 - fx) + field[i+1, j, k] * fx
            c01 = field[i, j, k+1] * (1 - fx) + field[i+1, j, k+1] * fx
            c10 = field[i, j+1, k] * (1 - fx) + field[i+1, j+1, k] * fx
            c11 = field[i, j+1, k+1] * (1 - fx) + field[i+1, j+1, k+1] * fx
            
            c0 = c00 * (1 - fy) + c10 * fy
            c1 = c01 * (1 - fy) + c11 * fy
            
            return c0 * (1 - fz) + c1 * fz
            
        except Exception as e:
            return 0.0
            
    def diffuse_field(self, field: np.ndarray, diffusion_coeff: float, 
                      dt: float) -> np.ndarray:
        """
        扩散场量
        
        Args:
            field: 要扩散的场
            diffusion_coeff: 扩散系数
            dt: 时间步长
            
        Returns:
            扩散后的场
        """
        try:
            # 根据配置选择扩散格式
            scheme = self.numerical_methods['diffusion_scheme']
            
            if scheme == 'explicit':
                return self._diffuse_explicit(field, diffusion_coeff, dt)
            elif scheme == 'implicit':
                return self._diffuse_implicit(field, diffusion_coeff, dt)
            else:
                # 默认使用显式方法
                return self._diffuse_explicit(field, diffusion_coeff, dt)
                
        except Exception as e:
            print(f"✗ 场扩散失败: {e}")
            return field
            
    def _diffuse_explicit(self, field: np.ndarray, diffusion_coeff: float, 
                          dt: float) -> np.ndarray:
        """
        显式扩散
        
        Args:
            field: 要扩散的场
            diffusion_coeff: 扩散系数
            dt: 时间步长
            
        Returns:
            扩散后的场
        """
        try:
            # 拉普拉斯算子的显式更新
            nx, ny, nz = field.shape
            dx = self.grid_config['cell_size']
            
            # 扩散系数
            alpha = diffusion_coeff * dt / (dx * dx)
            
            # 确保稳定性 (CFL条件)
            if alpha > 0.166667:  # 1/6 for 3D
                alpha = 0.166667
                
            new_field = field.copy()
            
            for i in range(1, nx-1):
                for j in range(1, ny-1):
                    for k in range(1, nz-1):
                        laplacian = (
                            field[i+1, j, k] + field[i-1, j, k] +
                            field[i, j+1, k] + field[i, j-1, k] +
                            field[i, j, k+1] + field[i, j, k-1] -
                            6 * field[i, j, k]
                        )
                        new_field[i, j, k] = field[i, j, k] + alpha * laplacian
                        
            return new_field
            
        except Exception as e:
            print(f"✗ 显式扩散失败: {e}")
            return field
            
    def _diffuse_implicit(self, field: np.ndarray, diffusion_coeff: float, 
                          dt: float) -> np.ndarray:
        """
        隐式扩散
        
        Args:
            field: 要扩散的场
            diffusion_coeff: 扩散系数
            dt: 时间步长
            
        Returns:
            扩散后的场
        """
        try:
            # 隐式扩散需要求解线性系统
            # (I - alpha * L) * field_new = field_old
            return field  # 简化处理
            
        except Exception as e:
            print(f"✗ 隐式扩散失败: {e}")
            return field
            
    def set_solver_config(self, config: Dict[str, Any]):
        """
        设置求解器配置
        
        Args:
            config: 求解器配置字典
        """
        try:
            self.solver_config.update(config)
            print(f"✓ 求解器配置已更新: {self.solver_config}")
        except Exception as e:
            print(f"✗ 求解器配置设置失败: {e}")
            
    def get_solver_config(self) -> Dict[str, Any]:
        """
        获取求解器配置
        
        Returns:
            求解器配置字典
        """
        return self.solver_config.copy()
        
    def set_numerical_methods(self, methods: Dict[str, str]):
        """
        设置数值方法
        
        Args:
            methods: 数值方法字典
        """
        try:
            self.numerical_methods.update(methods)
            print(f"✓ 数值方法已更新: {self.numerical_methods}")
        except Exception as e:
            print(f"✗ 数值方法设置失败: {e}")
            
    def get_numerical_methods(self) -> Dict[str, str]:
        """
        获取数值方法
        
        Returns:
            数值方法字典
        """
        return self.numerical_methods.copy()
        
    def get_performance_stats(self) -> Dict[str, Any]:
        """
        获取性能统计信息
        
        Returns:
            性能统计字典
        """
        return self.performance_stats.copy()
        
    def reset_performance_stats(self):
        """重置性能统计信息"""
        try:
            self.performance_stats = {
                'pressure_solves': 0,
                'velocity_updates': 0,
                'total_solve_time': 0.0,
                'average_solve_time': 0.0
            }
            print("✓ 性能统计信息已重置")
        except Exception as e:
            print(f"✗ 性能统计信息重置失败: {e}")
            
    def get_solver_state(self) -> Dict[str, Any]:
        """
        获取求解器状态
        
        Returns:
            求解器状态字典
        """
        return self.solver_state.copy()