fig, ax = plt.subplots()
clf = ax.pcolor(x, y, np.abs(err), vmin=1e-5, norm=mpl.colors.LogNorm())
plt.plot(new_ebdy.bdy.x, ebdy.bdy.y, color='red')
plt.colorbar(clf)
print('On grid: {:0.1e}'.format(merr))
# assess error in position
err_bx = np.abs(bxe - ebdy.bdy.x)
err_by = np.abs(bye - ebdy.bdy.y)
err_b = max(err_bx.max(), err_by.max())
print('Boundary motion is: {:0.1e}'.format(err_b))
# assess on radial grid (via NUFFT of eulerian solution)
cr = ebdy.interpolate_grid_to_radial(c_eulerian)
err_r = np.abs(cr - c.radial_value_list[0])
merr_r = err_r.max()
print('On radial grid: {:0.1e}'.format(merr_r))
# plot the errors properly...
cerr = EmbeddedFunction(ebdyc)
cerr.load_data(err, [
err_r,
])
fig, ax = plt.subplots()
cerr.plot(ax, vmin=1e-5, norm=mpl.colors.LogNorm())
# c_new.grid_value[new_ebdyc.phys_not_in_annulus] = ch[:gp.N]
# # set the radial values
# c_new.radial_value_list[0][:] = ch[gp.N:].reshape(ebdy.radial_shape)
# bb = c_new.radial_value_list[0][-1].copy()
# visualize fc3n
if False:
fig, ax = plt.subplots()
is_fc3 = EmbeddedFunction(new_ebdyc)
is_fc3_grid = np.zeros(x.shape, dtype=bool)
is_fc3_grid[new_ebdyc.phys] = fc3[:gp.N]
is_fc3_radial = fc3[gp.N:].reshape(new_ebdy.radial_shape)
is_fc3.load_data(is_fc3_grid, [
is_fc3_radial,
])
clf = is_fc3.plot(ax)
for bb, col in zip([new_ebdyc, ebdyc, ebdyc_old],
['red', 'pink', 'gray']):
bbb = bb.ebdys[0].bdy
ax.plot(bbb.x, bbb.y, color=col)
ax.scatter(new_ebdyc.ebdys[0].radial_x,
new_ebdyc.ebdys[0].radial_y,
color='white')
# now reset naming conventions
try:
ebdy_old_save = ebdy_old
ebdyc_old_save = ebdyc_old
c_old_save = c_old
except:
pass
# set the radial values c_new.radial_value_list[0][:] = ch[gp.N:].reshape(ebdy.radial_shape) # merge the grid and the radial values where they overlap new_ebdyc.merge_grids(c_new) # overwrite under grid under annulus by radial grid # _ = new_ebdyc.interpolate_radial_to_grid(c_new.radial_value_list, c_new.grid_value) # visualize fc3n if False: fig, ax = plt.subplots() is_fc3 = EmbeddedFunction(new_ebdyc) is_fc3_grid = np.zeros(x.shape, dtype=bool) is_fc3_grid[new_ebdyc.phys] = fc3[:gp.N] is_fc3_radial = fc3[gp.N:].reshape(new_ebdy.radial_shape) is_fc3.load_data(is_fc3_grid, [is_fc3_radial,]) clf = is_fc3.plot(ax) for bb, col in zip([new_ebdyc, ebdyc], ['red', 'pink', 'gray']): bbb = bb.ebdys[0].bdy ax.plot(bbb.x, bbb.y, color=col) ax.scatter(new_ebdyc.ebdys[0].radial_x, new_ebdyc.ebdys[0].radial_y, color='white') # now reset naming conventions old_ebdy = ebdy old_ebdyc = ebdyc ebdy = new_ebdy ebdyc = new_ebdyc c = c_new # assess error on underlying grid err = (np.abs(c.grid_value - ce)*ebdyc.phys).max()