import numpy as np
from svirl import GLSolver
import seaborn as sns
import matplotlib.pyplot as plt
gl = GLSolver(
dx = 0.5, dy = 0.5,
Lx = 64, Ly = 64,
order_parameter = 'random',
random_seed = 4,
homogeneous_external_field = 0.05,
external_field = 0.00,
gl_parameter = 5.0, # np.inf
normal_conductivity = 200.0,
dtype = np.float64,
)
aax = gl.vars.vector_potential[0] # store the vector potential for uniform field for future use
aay = gl.vars.vector_potential[1]
gl.solve.td(dt=0.01, Nt=10000) # Now do the time evolution
gl.solve.cg(n_iter = 1000)
heat = sns.heatmap(gl.observables.superfluid_density, cmap="CMRmap_r", vmin=0, vmax=1)
heat.invert_yaxis()
plt.savefig('1.png')
Produces this output:

However, passing A produces different output as shown below:
ginz = GLSolver(
dx = 0.5, dy = 0.5,
Lx = 64, Ly = 64,
order_parameter = 'random',
random_seed = 4,
homogeneous_external_field = 0.00,
external_field = 0.00,
gl_parameter = 5.0, # np.inf
normal_conductivity = 200.0,
dtype = np.float64,
)
ginz.params.external_vector_potential = (aax, aay) # aax and aay are obtained from the piece of code above
ginz.solve.td(dt=0.01, Nt=10000) # Do the same time evolution as before
ginz.solve.cg(n_iter = 1000)
heat = sns.heatmap(ginz.observables.superfluid_density, cmap="CMRmap_r", vmin=0, vmax=1)
heat.invert_yaxis()
plt.savefig('2.png')

Produces this output:

However, passing A produces different output as shown below: