Figures not displaying

I want to generate two graphs that show a solution of a simple harmonic oscillator. I'm running it in the latest version of Spyder.

I have two figures in this code which I have set-up to run in separate pop-up windows. The windows pop-up but the figures are not displaying.

What is the problem?

It seems like figure two also stops responding after I run so is it an issue with my computer instead of the code?

Thanks

#!/usr/bin/env python3

# Verifying a solution of the simple harmonic oscillator equation by
# establishing convergence to an exact solution.

import matplotlib.pyplot as plt
import numpy

k      = 1.0 # Spring constant
m      = 1.0 # Mass
cycles = 2.0 # No. of periods to integrate over
x0     = 1.0 # Initial displacement
v0     = 0.0 # Initial velocity

def leapfrog( steps ):
    """Solve the simple harmonic motion equations for several oscillation cycles,
       assuming that the mass (m) and spring constant (k) are defined in the
       global space.
    """
    omega = (k/m)**0.5
    delta = 2.0*cycles*numpy.pi/omega/steps
    x     = numpy.empty( steps+1 )
    v     = numpy.empty( steps+1 )
    t     = numpy.empty( steps+1 )
    t[0]  = 0.0
    x[0]  = x0
    v[0]  = v0 + 0.5*delta*x[0]*omega**2.0
    for i in range(steps):
        t[i+1] = t[i] + delta
        v[i+1] = v[i] - omega**2*delta*x[i]
        x[i+1] = x[i] + delta*v[i+1]
    return t, x, v

def l2_error_norm( t , x ):
    """Calculate the L2 relative error norm."""
    steps  = len( x ) - 1
    omega  = (k/m)**0.5
    l2_err = 0.0
    l2     = 0.0
    for i in range(steps):
        x_exact = x0*numpy.cos( omega*t[i] )
        l2_err += (x[i] - x_exact)**2.0
        l2     += x[i]**2.0
    return (l2_err/l2)**0.5

plt.switch_backend( 'TkAgg' )

# This loop integrates the SHM equations repeatedly using an increasing
# number of steps (doubling at each loop iteration).
n        = 14
steps    = 8
l2_error = numpy.empty( n )
delta    = numpy.empty( n )
for i in range(0,n):
    t, x, v     = leapfrog( steps )
    delta[i]    = (k/m)**0.5*(t[1]-t[0])
    l2_error[i] = l2_error_norm( t , x )
    plt.plot( t , x )
    #plt.show( block=False )
    steps *= 2

plt.figure()
plt.loglog( delta , l2_error , 'o' )
plt.loglog( delta , l2_error[0]*(delta/delta[0])**1.0 )
plt.loglog( delta , l2_error[0]*(delta/delta[0])**2.0 )
plt.loglog( delta , l2_error[0]*(delta/delta[0])**3.0 )
plt.show()