def create_logo():
# Adapted from
# <https://matplotlib.org/gallery/images_contours_and_fields/tripcolor_demo.html#sphx-glr-gallery-images-contours-and-fields-tripcolor-demo-py>
# First create the x and y coordinates of the points.
n_angles = 314
n_radii = 100
radii = numpy.linspace(0.0, 1.0, n_radii)
angles = numpy.linspace(0, 2 * numpy.pi, n_angles, endpoint=False)
angles = numpy.repeat(angles[..., numpy.newaxis], n_radii, axis=1)
angles[:, 1::2] += numpy.pi / n_angles
x = (radii * numpy.cos(angles)).flatten()
y = (radii * numpy.sin(angles)).flatten()
# Create the Triangulation; no triangles so Delaunay triangulation created.
triang = tri.Triangulation(x, y)
# print(triang)
# exit(1)
# import dmsh
# geo = dmsh.Circle([0.0, 0.0], 1.0)
# X, cells = dmsh.generate(geo, 0.1)
z = x + 1j * y
# z /= numpy.abs(z)
cplot.tripcolor(triang, z, alpha=0)
plt.gca().set_aspect("equal", "datalim")
plt.axis("off")
plt.savefig("logo.png", transparent=True)
return
def test_tripcolor():
# Adapted from
# <https://matplotlib.org/gallery/images_contours_and_fields/tripcolor_demo.html#sphx-glr-gallery-images-contours-and-fields-tripcolor-demo-py>
# First create the x and y coordinates of the points.
n_angles = 36
n_radii = 8
min_radius = 0.25
radii = numpy.linspace(min_radius, 0.95, n_radii)
angles = numpy.linspace(0, 2 * numpy.pi, n_angles, endpoint=False)
angles = numpy.repeat(angles[..., numpy.newaxis], n_radii, axis=1)
angles[:, 1::2] += numpy.pi / n_angles
x = (radii * numpy.cos(angles)).flatten()
y = (radii * numpy.sin(angles)).flatten()
z = 2 * (x + 1j * y)
# Create the Triangulation; no triangles so Delaunay triangulation created.
triang = tri.Triangulation(x, y)
# Mask off unwanted triangles.
triang.set_mask(
numpy.hypot(x[triang.triangles].mean(axis=1), y[triang.triangles].mean(
axis=1)) < min_radius)
cplot.tripcolor(triang, z)
plt.gca().set_aspect("equal", "datalim")
plt.show()
return
def plot_data():
filename = "data.yml"
with open(filename) as fh:
data = yaml.safe_load(fh)
reader = meshio.xdmf.TimeSeriesReader(data["filename"])
points, cells = reader.read_points_cells()
x, y, _ = points.T
# compute all energies in advance
energies = []
mesh = meshplex.MeshTri(points, cells["triangle"])
for k in range(len(data["mu"])):
_, point_data, _ = reader.read_data(k)
psi = point_data["psi"]
psi = psi[:, 0] + 1j * psi[:, 1]
energies.append(gibbs_energy(mesh, psi))
energies = np.array(energies)
for k in range(len(data["mu"])):
plt.figure(figsize=(11, 4))
ax1 = plt.subplot(1, 2, 1)
ax1.plot(data["mu"], energies)
ax1.set_xlim(0.0, 1.0)
ax1.set_ylim(-1.0, 0.0)
ax1.grid()
ax1.plot(data["mu"][k], energies[k], "o", color="#1f77f4")
ax1.set_xlabel("$\\mu$")
ax1.set_ylabel("$\\mathcal{E}(\\psi)$")
_, point_data, _ = reader.read_data(k)
psi = point_data["psi"]
psi = psi[:, 0] + 1j * psi[:, 1]
ax2 = plt.subplot(1, 2, 2)
triang = matplotlib.tri.Triangulation(x, y)
# The absolute values of the solution psi of the Ginzburg-Landau equations all
# sit between 0 and 1, so we don't need a fancy scaling of absolute values for
# cplot. This results in the values with |psi|=1 being displayed as white,
# however, losing visual information about the complex argument. On the other
# hand, plots are rather more bright, resulting in more visually appealing
# figures.
cplot.tripcolor(triang, psi, abs_scaling=lambda r: r)
# plt.tripcolor(triang, np.abs(psi))
ax2.axis("square")
ax2.set_xlim(-5.0, 5.0)
ax2.set_ylim(-5.0, 5.0)
ax2.set_title("$\\psi$")
# plt.colorbar()
# plt.set_cmap("gray")
# plt.clim(0.0, 1.0)
plt.tight_layout()
plt.savefig(f"fig{k:03d}.png")
# plt.show()
plt.close()