Animation Of A 3d Surface From Calculated Matrices

I'm trying to animate a 3D surface generated out of Jacobi iterative method, after each iteration a matrix UF is generated and stored in a list UFK i was able to plot each iteratio

Solution 1:

First of all the call to animation.FuncAnimation must happen before plt.show().

Second, you do not need to give the arguments UFK,surf to the animating function.

Third, the loop inside data is running for each animation step, therefore you would end up with identical plots at the end. Get rid of that loop.

Fourth, restrict the animation to the number of matrices you have calculated.

Here is a working code:

import numpy as np 
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import matplotlib.animation as animation


Nx = 15        
Ny = 15         
tol = 1e-3      
err = 1        
k = 0         

Uy0 = 200*np.ones((1,Nx)) # Boundry condition at y=0 # lower boundry
UNy = 200*np.ones((1,Nx)) # Boundry condition at y=Ny # Upper boundary
Ux0 = 200*np.ones(Ny) # Boundry condition at x=0 # left boundry 
UNx = 200*np.ones(Ny) # Boundry condition at x=Nx # Right boundary # initial the whole matrix: the value at the interior nodes 
U = np.zeros((Ny,Nx))
#Adding boundry conditions to the matrix 
U[0] = UNy
U[Ny-1] = Uy0
U[:,Nx-1] = UNx
U[:,0]= Ux0
# Iterate Jacobi method  
UFK=[]
UFK.append(U.copy())
NFK=[]
UF=U.copy()

whileTrue:
    k=k+1for i inrange (1,Nx-1):
        for j inrange (1,Ny-1):
            UF[j,i] = (UF[j+1,i]+UF[j,i+1]+UF[j-1,i]+UF[j,i-1])*0.25#the matrix i want to plot after each iteration 
    UFK.append(UF.copy())
    H = UFK[-1]-UFK[-2]
    N = np.linalg.norm(H)
    NFK.append(N)
    if N <= tol:
        breakdefdata(t):
    # remove for loop here
    L = UFK[t]
    ax.clear()
    surf = ax.plot_surface(XX, YY, L, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
    ax.set_zlim([0,200]) # set zlim to be always the same for every frame


fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(0, Nx)
Y = np.arange(0, Ny)
XX,YY = np.meshgrid(X, Y)
surf = ax.plot_surface(XX, YY, UFK[0],rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
ax.set_zlim(0, 200)
ax.zaxis.set_major_locator(LinearLocator(10))
fig.colorbar(surf, shrink=0.5, aspect=10)
ax.set_xlabel('X nodes - Axis')
ax.set_ylabel('Y nodes - Axis')
ax.set_zlabel('Value')

ani = animation.FuncAnimation(fig, data, len(UFK), interval=50, repeat=True )
plt.show()