def macclaurin_test():
# setup the grid
g = Grid(128, 256, rlim=(0, 0.5), zlim=(-0.5, 0.5))
# create a MacLaurin spheriod on the grid
ms = MacLaurinSpheroid(g, 0.25, 0.1)
# plot the density
dens = ms.density()
plt.clf()
plt.imshow(np.transpose(dens),
origin="lower",
interpolation="nearest",
extent=[g.rlim[0], g.rlim[1], g.zlim[0], g.zlim[1]])
ax = plt.gca()
ax.set_aspect("equal")
plt.savefig("dens.png")
# plot the analytic potential
phi_analytic = ms.phi()
plt.clf()
plt.imshow(np.log10(np.abs(np.transpose(phi_analytic))),
origin="lower",
interpolation="nearest",
extent=[g.rlim[0], g.rlim[1], g.zlim[0], g.zlim[1]])
plt.colorbar()
ax = plt.gca()
ax.set_aspect("equal")
plt.savefig("phi_analytic.png")
# compute the multipole expansion and sample the potential
center = (0.0, 0.0)
m = Multipole(g, 12, 2 * g.dr, center=center)
m.compute_expansion(dens)
phi = g.scratch_array()
for i in range(g.nr):
for j in range(g.nz):
phi[i, j] = m.phi(g.r[i], g.z[j])
# plot the potential
plt.clf()
plt.imshow(np.log10(np.abs(np.transpose(phi))),
origin="lower",
interpolation="nearest",
extent=[g.rlim[0], g.rlim[1], g.zlim[0], g.zlim[1]])
plt.colorbar()
ax = plt.gca()
ax.set_aspect("equal")
plt.savefig("phi.png")
