def refine_func(sweep_axis):
points, mean = sw.load_switching_data(wr.filename)
new_points = refine.refine_scalar_field(points,
mean,
all_points=False,
criterion="integral",
threshold="one_sigma")
if len(points) + len(new_points) > max_points:
print("Reached maximum points ({}).".format(max_points))
return False
print("Reached {} points.".format(len(points) + len(new_points)))
sweep_axis.add_points(new_points)
# Plot previous mesh
x = [list(el) for el in points[mesh.simplices, 0]]
y = [list(el) for el in points[mesh.simplices, 1]]
val = [np.mean(vals) for vals in mean[mesh.simplices]]
desc = DataStreamDescriptor()
desc.add_axis(sweep_axis)
exp.push_to_plot(fig1, desc, points)
time.sleep(1)
return True
exp.push_to_plot(fig1, desc, points)
time.sleep(1)
return True
sweep_axis = exp.add_sweep([exp.pulse_duration, exp.pulse_voltage],
points,
refine_func=refine_func)
# Borrow the descriptor from the main sweep and use it for our direct plotter
exp.add_plotter(fig1, desc)
exp.run_sweeps()
points, mean = sw.load_switching_data(wr.filename)
mesh, scale_factors = sw.scaled_Delaunay(points)
fig_mesh = sw.phase_diagram_mesh(points, mean, shading='gouraud')
plt.triplot(mesh.points[:, 0] / scale_factors[0],
mesh.points[:, 1] / scale_factors[1], mesh.simplices.copy())
plt.show()
# t1 = time.time()
# for i in range(exp.iterations):
# exp.reset()
# exp.run_sweeps()
# points, mean = sw.load_switching_data(wr.filename)
# figs.append(sw.phase_diagram_mesh(points, mean, title="Iteration={}".format(i)))
# new_points = refine.refine_scalar_field(points, mean, all_points=False,
# criterion="integral", threshold = "one_sigma")