loops.plot_loops()
B_target = loops.magnetic_field(target_points)
mlab.quiver3d(*target_points.T, *B_target.T)
#%%
# Lets also do the same coil optimization using regularized least-squares.
# Now we can't specify inequality constraints (e.g. use error margins in the specification).
from bfieldtools.coil_optimize import optimize_lsq
coil.s2 = optimize_lsq(
coil, [target_spec, stray_spec], objective="minimum_ohmic_power", reg=1e6
)
#%%
# Plot the optimized stream function, then discretize it and plot coil windings and the resultant magnetic field
coil.s2.plot()
loops2 = coil.s2.discretize(N_contours=10)
loops2.plot_loops()
B_target = loops2.magnetic_field(target_points)
mlab.quiver3d(*target_points.T, *B_target.T)
coil.set_sph_options(sph_radius=1.4)
target_spec = {
"coupling": coil.sph_couplings[1],
"abs_error": 0.01,
"target": target_blms,
}
#%%
# Run QP solver
import mosek
coil.s2, prob = optimize_streamfunctions(
coil,
[target_spec],
objective="minimum_ohmic_power",
solver="MOSEK",
solver_opts={"mosek_params": {
mosek.iparam.num_threads: 8
}},
)
#%%
# Plot coil windings
f2 = coil.plot_mesh(opacity=0.2)
loops2 = coil.s2.discretize(N_contours=6)
loops2.plot_loops(figure=f2)
secondary_C = shield.B_coupling(target_points) @ shield.coupling
total_C = coil.B_coupling(target_points) + secondary_C
target_spec_w_shield = {
"coupling": total_C,
"rel_error": 0,
"abs_error": target_abs_error,
"target": target_field,
}
coil.s2, coil.prob2 = optimize_streamfunctions(
coil,
[target_spec_w_shield],
objective="minimum_inductive_energy",
solver="MOSEK",
solver_opts={"mosek_params": {
mosek.iparam.num_threads: 8
}},
)
#%%
# Plot the newly designed coil windings and field at the target points
loops = scalar_contour(coil.mesh, coil.s2.vert, N_contours=10)
f = mlab.figure(None,
bgcolor=(1, 1, 1),
fgcolor=(0.5, 0.5, 0.5),
size=(800, 800))
mlab.clf()