Python matplotlib animation issue on Mac OS

chart.py

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import math

# define x coordinates
x0, x1 = 0, 1
n = 500
x = np.linspace(x0, x1, n)
dx = x[1] - x[0]

# define time coordinates
t0, t1 = 0, 1
m = 500
t = np.linspace(t0, t1, m)
dt = t[1] - t[0]

# define velocity
uleft, uright = 1.0, 1.0
c = np.linspace(uleft, uright, x.shape[0])

# calculate stability
stab = np.amax(c) * dt / dx
print(f"stability r: {stab}")

# position in x
xs = 0.5
# width of the wavelet
rick = 0.1
xxsq = np.square((x - xs) / rick)
r = (1 - 12 * np.pi * xxsq) * np.exp(-6 * np.pi * xxsq)

# define two subplots
fig, ax = plt.subplots(2)

# plot ricker wavelet
ax[0].plot(x,r)
ax[0].set(
    xlabel="x",
    ylabel="displacement",
    title="initial displacement, Ricker wavelet",
    ylim=[-1.0, 1.0]
)

# initial displacement
u0 = r
# make sure boundary conditions are fulfilled
u0[0], u0[-1] = 0, 0
# initial velocity
ut0 = np.zeros(n)
# estimate second space derivative at t = 0
uxx0 = np.concatenate((u0, np.zeros(2))) \
    - 2 * np.concatenate((np.zeros(1), u0, np.zeros(1))) \
    + np.concatenate((np.zeros(2), u0))
# estimate the first time step
u1 = u0 + dt * ut0 + dt * dt / 2 / dx / dx * c * c * uxx0[1:-1]
# initialize u with zeros
u = np.zeros(n)

# define matrix to make all spatial derivatives at once
A = np.diag(2 * np.ones(n - 2)) \
    + np.diag(-np.ones(n - 3), -1) \
    + np.diag(-np.ones(n - 3), 1)
A = dt * dt / dx / dx * A

# the free inner points where no boundary conditions are prescribed
free = np.linspace(1, n - 2, n - 2, dtype="int")
# velocity squared
c2 = c * c
c2 = c2[free]

# time loop
tmp0 = u0[free]  # first time step
tmp1 = u1[free]  # second time step

#prepare the subplot for the displacement
ax[1].set(
    ylim=(-1.0, 1.0),
    xlabel="x",
    ylabel="displacement",
    title="explicit fd solution of 1d wave eqn"
)


def animate(it):
    global tmp0, tmp1
    # predict u in the next time step
    u[free] = 2 * tmp1 - tmp0 - c2 * np.dot(A, tmp1)
    # update the previous time steps
    tmp0 = tmp1
    tmp1 = u[free]
    line, = ax[1].plot(x, u)
    return line,


# total time of animation in ms
totaltime = 1000
tint = totaltime / m
ani = animation.FuncAnimation(
    fig,
    animate,
    np.arange(m),
    interval=tint,
    blit=True,
    repeat=False
)
plt.show()