import matplotlib.animation as animation
import matplotlib.pyplot as plt
import processing.post as post
import processing.plot as plot
import processing.readwritedatafiles as readwritedatafiles
plot.prepare_plot(linewidth=0.5)
fig = plt.figure()
ax = plt.gca()
imgs_all = []
j = 0
# Loop through data files
# Note: this loop only uses the first 15 frames. This is all that should be
# necessary to see the dynamics of the energy being damped.
for i in range(25):
print(i)
# Read data file
fname = "Data_" + str(i) + ".pkl"
solver = readwritedatafiles.read_data_file(fname)
# Unpack
mesh = solver.mesh
physics = solver.physics
# Plot solution
plot.plot_solution(mesh,
physics,
solver,
import processing.post as post
import processing.plot as plot
import processing.readwritedatafiles as readwritedatafiles
# Read data file
fname = "Data_final.pkl"
#fname = "Data_n160_P2.pkl"
solver = readwritedatafiles.read_data_file(fname)
# Unpack
mesh = solver.mesh
physics = solver.physics
''' Plot '''
levels = np.arange(0., 5., 0.5)
# Density contour
plot.prepare_plot(linewidth=0.5)
plot.plot_solution(mesh,
physics,
solver,
"Density",
plot_numerical=True,
plot_exact=False,
plot_IC=False,
create_new_figure=True,
fmt='bo',
legend_label="DG",
include_mesh=False,
regular_2D=True,
show_elem_IDs=False,
levels=levels)
import processing.plot as plot
import processing.readwritedatafiles as readwritedatafiles
# Read data file
fname = "Data_final.pkl"
solver = readwritedatafiles.read_data_file(fname)
# Unpack
mesh = solver.mesh
physics = solver.physics
# Compute L2 error
post.get_error(mesh, physics, solver, "Scalar")
''' Plot '''
# DG solution
plot.prepare_plot()
plot.plot_solution(mesh,
physics,
solver,
"Scalar",
plot_numerical=True,
plot_exact=False,
plot_IC=False,
create_new_figure=True,
fmt='bo',
legend_label="DG")
# Exact solution
plot.plot_solution(mesh,
physics,
solver,
"Scalar",
# node_type = "GaussLobatto"
# Basis type
basis = basis_defs.LagrangeSeg(p) # Lagranage basis
# basis = basis_defs.LegendreSeg(p) # Legendre basis
'''
Pre-processing
'''
# Solution nodes
basis.get_1d_nodes = basis_tools.set_1D_node_calc(node_type)
# Sample points
p_plot = 100 # (p_plot + 1) points
xp = basis.equidistant_nodes(p_plot)
'''
Evaluate
'''
basis.get_basis_val_grads(xp, get_val=True)
'''
Plot
'''
plot.prepare_plot(linewidth=1.)
fig = plt.figure()
for i in range(p + 1):
if plot_all or (not plot_all and i == b):
phi = basis.basis_val[:, i]
plt.plot(xp, phi, label="$\\phi_{%d}$" % (i + 1))
plt.xlabel('$\\xi$')
plt.ylabel('$\\phi$')
plt.legend(loc="best")
plt.xticks((-1., -0.5, 0., 0.5, 1.))
plt.show()
