  Here is the Python code that performs the tasks described in the instruction:
```
import numpy as np
import argparse
import matplotlib.pyplot as plt
from scipy import sparse
from fealpy import ADI_2d, FirstKindNedelecFiniteElementSpace2d, ScaledMonomialSpace2d

# Define command line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--time_division', type=int, default=10)
parser.add_argument('--initial_spatial_division', type=int, default=10)
parser.add_argument('--number_of_spatial_iterations', type=int, default=10)
args = parser.parse_args()

# Create an initial 2D triangular mesh
mesh = fealpy.create_mesh(args.initial_spatial_division, args.initial_spatial_division)

# Define the parameters for the PDE
adi_2d = ADI_2d(mesh, args.time_division)

# Initialize the electric and magnetic fields
E = FirstKindNedelecFiniteElementSpace2d(mesh, args.time_division)
H = ScaledMonomialSpace2d(mesh, args.time_division)

# Define a function to get the phi curl matrix
def get_phi_curl_matrix(E, H):
    return sparse.kron(E.curl_matrix, H.curl_matrix)

# Create mass and curl matrices
mass_matrix = sparse.kron(E.mass_matrix, H.mass_matrix)
curl_matrix = get_phi_curl_matrix(E, H)

# Iterate over the number of spatial iterations
for i in range(args.number_of_spatial_iterations):
    # Compute the right-hand side of the equation for the next time layer
    rhs = adi_2d.assembly_for_sspace_and_vspace_with_vector_basis(mass_matrix, curl_matrix)

    # Handle the boundary conditions for the next time layer
    bc = adi_2d.test_assembly_cell_righthand_side_and_matrix(rhs, mass_matrix, curl_matrix)

    # Compute the electric and magnetic fields for the next time layer
    E.update(bc)
    H.update(bc)

    # Calculate the error between the computed and actual solutions for the electric and magnetic fields
    error = np.abs(E.get_all_gauss_point_and_weight() - E.get_next_time())
    error += np.abs(H.get_all_gauss_point_and_weight() - H.get_next_time())

    # If not the last iteration, refine the mesh uniformly
    if i < args.number_of_spatial_iterations - 1:
        mesh.refine_uniform()

# Finally, display the error matrix and plot the error rates using the fealpy library
error_matrix = np.abs(E.get_all_gauss_point_and_weight() - E.get_next_time())
error_matrix += np.abs(H.get_all_gauss_point_and_weight() - H.get_next_time())
plt.plot(error_matrix)
plt.xlabel('Time step')
plt.ylabel('Error rate')
plt.show()
```