import numpy as np
from scipy.interpolate import interp2d

class Chebyshev:

    def __init__():
        self.Dx, self.x = chebyshevMatrix(self.Nx-1)
        self.Dy, self.y = chebyshevMatrix(self.Ny-1)
        self.D2x = np.dot(self.Dx, self.Dx)
        self.D2y = np.dot(self.Dy, self.Dy)
        
        self.X, self.Y = np.meshgrid(self.x, self.y)
        
        self.X0 = 2 / (self.x_max - self.x_min)
        self.Y0 = 2 / (self.y_max - self.y_min)
        
        self.u0 = lambda x, y: self.u0(self.tx(x), self.tx(y))
        self.b0 = lambda x, y: self.b0(self.tx(x), self.tx(y))

    # Chebyshev approximation in space
    def RHS(self, t, y):
        """
        Compute right hand side of PDE
        """
        # Vector field evaluation
        V1 = self.v[0](self.X, self.Y, t)
        V2 = self.v[1](self.X, self.Y, t)
        
        Uf = np.copy(y[:self.Ny * self.Nx].reshape((self.Ny, self.Nx)))
        Bf = np.copy(y[self.Ny * self.Nx:].reshape((self.Ny, self.Nx)))
        U = Uf
        B = Bf

        # Compute derivatives
        if self.sparse:
            Ux, Uy = (self.Dx.dot(U.T)).T, self.Dy.dot(U) # grad(U) = (u_x, u_y)
            Uxx, Uyy = (self.D2x.dot(U.T)).T, self.D2y.dot(U) # 
        else:
            Ux, Uy = np.dot(U, self.Dx.T), np.dot(self.Dy, U)
            Uxx, Uyy = np.dot(U, self.D2x.T), np.dot(self.D2y, U)
            
        lapU = self.X0 ** 2 * Uxx + self.Y0 ** 2 * Uyy
        
        if self.complete:
            K = self.K(U) 
            Kx = self.Ku(U) * Ux #(Dx.dot(K.T)).T
            Ky = self.Ku(U) * Uy #Dy.dot(K)
            diffusion = Kx * Ux + Ky * Uy + K * lapU
        else:
            diffusion = self.kap * lapU # k \nabla U
        
        convection = self.X0 * Ux * V1 + self.Y0 * Uy * V2 # v \cdot grad u.    
        reaction = self.f(U, B) # eval fuel

        # Components
        diffusion *= self.cmp[0]
        convection *= self.cmp[1]
        reaction *= self.cmp[2]
        
        Uf = diffusion -  convection + reaction 
        Bf = self.g(U, B)
        
        # Boundary conditions
        Uf, Bf = self.boundaryConditions(Uf, Bf)
        
        return np.r_[Uf.flatten(), Bf.flatten()]

    # Domain transformations
    def tx(self, t):  
        return (self.x_max - self.x_min) * t / 2 + (self.x_max + self.x_min)/2 # [-1,1] to [xa, xb]
    
    def xt(self, x):
        return 2 * x / (self.x_max - self.x_min) - (self.x_max +self.x_min) / (self.x_max - self.x_min) # [xa, xb] to [-1, 1]
import numpy as np
from scipy.interpolate import interp2d

class Chebyshev:

    def __init__():
        self.Dx, self.x = chebyshevMatrix(self.Nx-1)
        self.Dy, self.y = chebyshevMatrix(self.Ny-1)
        self.D2x = np.dot(self.Dx, self.Dx)
        self.D2y = np.dot(self.Dy, self.Dy)
        
        self.X, self.Y = np.meshgrid(self.x, self.y)
        
        self.X0 = 2 / (self.x_max - self.x_min)
        self.Y0 = 2 / (self.y_max - self.y_min)
        
        self.u0 = lambda x, y: self.u0(self.tx(x), self.tx(y))
        self.b0 = lambda x, y: self.b0(self.tx(x), self.tx(y))

    # Chebyshev approximation in space
    def RHS(self, t, y):
        """
        Compute right hand side of PDE
        """
        # Vector field evaluation
        V1 = self.v[0](self.X, self.Y, t)
        V2 = self.v[1](self.X, self.Y, t)
        
        Uf = np.copy(y[:self.Ny * self.Nx].reshape((self.Ny, self.Nx)))
        Bf = np.copy(y[self.Ny * self.Nx:].reshape((self.Ny, self.Nx)))
        U = Uf
        B = Bf

        # Compute derivatives
        if self.sparse:
            Ux, Uy = (self.Dx.dot(U.T)).T, self.Dy.dot(U) # grad(U) = (u_x, u_y)
            Uxx, Uyy = (self.D2x.dot(U.T)).T, self.D2y.dot(U) # 
        else:
            Ux, Uy = np.dot(U, self.Dx.T), np.dot(self.Dy, U)
            Uxx, Uyy = np.dot(U, self.D2x.T), np.dot(self.D2y, U)
            
        lapU = self.X0 ** 2 * Uxx + self.Y0 ** 2 * Uyy
        
        if self.complete:
            K = self.K(U) 
            Kx = self.Ku(U) * Ux #(Dx.dot(K.T)).T
            Ky = self.Ku(U) * Uy #Dy.dot(K)
            diffusion = Kx * Ux + Ky * Uy + K * lapU
        else:
            diffusion = self.kap * lapU # k \nabla U
        
        convection = self.X0 * Ux * V1 + self.Y0 * Uy * V2 # v \cdot grad u.    
        reaction = self.f(U, B) # eval fuel

        # Components
        diffusion *= self.cmp[0]
        convection *= self.cmp[1]
        reaction *= self.cmp[2]
        
        Uf = diffusion -  convection + reaction 
        Bf = self.g(U, B)
        
        # Boundary conditions
        Uf, Bf = self.boundaryConditions(Uf, Bf)
        
        return np.r_[Uf.flatten(), Bf.flatten()]

    # Domain transformations
    def tx(self, t):  
        return (self.x_max - self.x_min) * t / 2 + (self.x_max + self.x_min)/2 # [-1,1] to [xa, xb]
    
    def xt(self, x):
        return 2 * x / (self.x_max - self.x_min) - (self.x_max +self.x_min) / (self.x_max - self.x_min) # [xa, xb] to [-1, 1]
# Chebyshev approximation in space Compute right hand side of PDE # Vector field evaluation # Compute derivatives # grad(U) = (u_x, u_y) # #(Dx.dot(K.T)).T #Dy.dot(K) # k \nabla U # v \cdot grad u. # eval fuel # Components # Boundary conditions # Domain transformations # [-1,1] to [xa, xb] # [xa, xb] to [-1, 1]