3D Beta space

3D_Beta_Space.py

hyper_output = []
with tqdm(range(-180, 181, 10), position=0) as t:
    for x in t:
        for y in tqdm(range(-180, 181, 10), disable=True):
            for z in tqdm(range(-180, 181, 10), disable=True):
            
                angles = torch.tensor([x,y,z])/180*torch.pi
                Hyper_x = transform_angles(angles=angles).to(device=gpu_computation)
                
                hyper_output.append(model.hyper_stack(Hyper_x).cpu().detach().numpy())
                
                # Update the description of progressbar
                t.set_postfix(Angles = [x,y,z]) 
print("Done!")
hyper_output = np.stack(hyper_output)
print(hyper_output.shape)

Beta = hyper_output
beta_selected = []

Beta_d0_to_d2_mean = Beta[:, 0:3].mean(axis=0)
Beta_d0_to_d2_var = Beta[:, 0:3].var(axis=0)
print(f'mean: {Beta_d0_to_d2_mean}  var: {Beta_d0_to_d2_var}')

for i, beta in enumerate(Beta):
    if np.linalg.norm(beta[0:3]-Beta_d0_to_d2_mean)<=0.08:
        beta_selected.append(beta)

beta_selected  = np.array(beta_selected)


fig = go.Figure(data=[go.Scatter3d(x=beta_selected[:,3], y=beta_selected[:,4], z=beta_selected[:,5],
                                   mode='markers', marker_size=1)])
fig.show()