<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Load the data.
Step1:  Fit a MAPL model to the data.
Step2:  We want to use an FA image as background, this requires us to fit a DTI model.
Step3:  Fit GP without mean and with DTI and MAPL as mean.
Step4:  gp_model = GaussianProcessModel(gtab, spatial_dims=2, q_magnitude_transform=np.sqrt, verbose=False)
gp_fit = gp_model.fit(np.squeeze(data), mean=None, voxel_size=voxel_size[0
Step5:  Compute the ODFs
Load an odf reconstruction sphere
Step6:  The radial order $s$ can be increased to sharpen the results, but it might
also make the odfs noisier. Note that a "proper" ODF corresponds to $s=0$.
Step7:  Display the ODFs
<ASSISTANT_TASK:>
Python Code:
dataset = 'SPARC'
if dataset == 'HCP':
    subject_path = conf['HCP']['data_paths']['mgh_1007']
    loader = get_HCP_loader(subject_path)
    small_data_path = '{}/mri/small_data.npy'.format(subject_path)
    loader.update_filename_data(small_data_path)
    data = loader.data
    gtab = loader.gtab
    voxel_size = loader.voxel_size
elif dataset == 'SPARC':
    subject_path = conf['SPARC']['data_paths']['gradient_60']
    gtab, data, voxel_size = preprocess_SPARC(subject_path, normalize=True)
    
btable = np.loadtxt(get_data('dsi4169btable'))
#btable = np.loadtxt(get_data('dsi515btable'))
gtab_dsi = gradient_table(btable[:, 0], btable[:, 1:],
                          big_delta=gtab.big_delta, small_delta=gtab.small_delta)
Explanation: Load the data.
End of explanation
map_model_laplacian_aniso = mapmri.MapmriModel(gtab, radial_order=6,
                                               laplacian_regularization=True,
                                               laplacian_weighting='GCV')
mapfit_laplacian_aniso = map_model_laplacian_aniso.fit(data)
Explanation: Fit a MAPL model to the data.
End of explanation
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data)
fitted = {'MAPL': mapfit_laplacian_aniso.predict(gtab)[:, :, 0],
          'DTI': tenfit.predict(gtab)[:, :, 0]}
Explanation: We want to use an FA image as background, this requires us to fit a DTI model.
End of explanation
kern = get_default_kernel(n_max=6, spatial_dims=2)
gp_model = GaussianProcessModel(gtab, spatial_dims=2, kernel=kern, verbose=False)
gp_fit = gp_model.fit(np.squeeze(data), mean=None, voxel_size=voxel_size[0:2], retrain=True)
kern = get_default_kernel(n_max=2, spatial_dims=2)
gp_dti_model = GaussianProcessModel(gtab, spatial_dims=2, kernel=kern, verbose=False)
gp_dti_fit = gp_dti_model.fit(np.squeeze(data), mean=fitted['DTI'], voxel_size=voxel_size[0:2], retrain=True)
kern = get_default_kernel(n_max=2, spatial_dims=2)
gp_mapl_model = GaussianProcessModel(gtab, spatial_dims=2, kernel=kern, verbose=False)
gp_mapl_fit = gp_mapl_model.fit(np.squeeze(data), mean=fitted['MAPL'], voxel_size=voxel_size[0:2], retrain=True)
Explanation: Fit GP without mean and with DTI and MAPL as mean.
End of explanation
pred = {'MAPL': mapfit_laplacian_aniso.predict(gtab_dsi)[:, :, 0],
        'DTI': tenfit.predict(gtab_dsi)[:, :, 0]}
Explanation: gp_model = GaussianProcessModel(gtab, spatial_dims=2, q_magnitude_transform=np.sqrt, verbose=False)
gp_fit = gp_model.fit(np.squeeze(data), mean=None, voxel_size=voxel_size[0:2], retrain=True)
gp_dti_fit = gp_model.fit(np.squeeze(data), mean=fitted['DTI'], voxel_size=voxel_size[0:2], retrain=True)
gp_mapl_fit = gp_model.fit(np.squeeze(data), mean=fitted['MAPL'], voxel_size=voxel_size[0:2], retrain=True)
End of explanation
sphere = get_sphere('symmetric724').subdivide(1)
Explanation: Compute the ODFs
Load an odf reconstruction sphere
End of explanation
odf = {'MAPL': mapfit_laplacian_aniso.odf(sphere, s=0),
       'DTI': tenfit.odf(sphere)}
odf['GP'] = gp_fit.odf(sphere, gtab_dsi=gtab_dsi, mean=None)[:, :, None, :]
odf['DTI_GP'] = gp_dti_fit.odf(sphere, gtab_dsi=gtab_dsi, mean=pred['DTI'])[:, :, None, :]
odf['MAPL_GP'] = gp_mapl_fit.odf(sphere, gtab_dsi=gtab_dsi, mean=pred['MAPL'])[:, :, None, :]
Explanation: The radial order $s$ can be increased to sharpen the results, but it might
also make the odfs noisier. Note that a "proper" ODF corresponds to $s=0$.
End of explanation
for name, _odf in odf.items():
    ren = window.Renderer()
    ren.background((1, 1, 1))
    odf_actor = actor.odf_slicer(_odf, sphere=sphere, scale=0.5, colormap='jet')
    background_actor = actor.slicer(tenfit.fa, opacity=1)
    odf_actor.display(z=0)
    odf_actor.RotateZ(90)
    background_actor.display(z=0)
    background_actor.RotateZ(90)
    background_actor.SetPosition(0, 0, -1)
    ren.add(background_actor)
    ren.add(odf_actor)
    window.record(ren, out_path='odfs_{}.png'.format(name), size=(1000, 1000))
Explanation: Display the ODFs
End of explanation
<END_TASK>