def run(idx, reduction, alpha, mask, raw, n_components, init, func_filenames):
    output_dir = join(trace_folder, 'experiment_%i' % idx)
    try:
        os.makedirs(output_dir)
    except OSError:
        pass
    dict_fact = SpcaFmri(mask=mask,
                         smoothing_fwhm=3,
                         batch_size=40,
                         shelve=not raw,
                         n_components=n_components,
                         replacement=False,
                         dict_init=fetch_atlas_smith_2009().rsn70 if
                         init else None,
                         reduction=reduction,
                         alpha=alpha,
                         random_state=0,
                         n_epochs=2,
                         l1_ratio=0.5,
                         backend='c',
                         memory=expanduser("~/nilearn_cache"), memory_level=2,
                         verbose=5,
                         n_jobs=1,
                         trace_folder=output_dir
                         )

    print('[Example] Learning maps')
    t0 = time.time()
    dict_fact.fit(func_filenames, raw=raw)
    t1 = time.time() - t0
    print('[Example] Dumping results')
    # Decomposition estimator embeds their own masker
    masker = dict_fact.masker_
    components_img = masker.inverse_transform(dict_fact.components_)
    components_img.to_filename(join(output_dir, 'components_final.nii.gz'))
    print('[Example] Run in %.2f s' % t1)
    # Show components from both methods using 4D plotting tools
    import matplotlib.pyplot as plt
    from nilearn.plotting import plot_prob_atlas, show

    print('[Example] Displaying')
    fig, axes = plt.subplots(2, 1)
    plot_prob_atlas(components_img, view_type="filled_contours",
                    axes=axes[0])
    plot_stat_map(index_img(components_img, 0),
                  axes=axes[1],
                  colorbar=False,
                  threshold=0)
    plt.savefig(join(output_dir, 'components.pdf'))
    show()

    # Grab extracted components umasked back to Nifti image.
    # Note: For older versions, less than 0.4.1. components_img_
    # is not implemented. See Note section above for details.
    components_img = estimator.components_img_
    components_img.to_filename('%s_resting_state.nii.gz' %
                               names[estimator])
    components_imgs.append(components_img)

###############################################################################
# Visualize the results
# ----------------------
from nilearn.plotting import (plot_prob_atlas, find_xyz_cut_coords, show,
                              plot_stat_map)
from nilearn.image import index_img

# Selecting specific maps to display: maps were manually chosen to be similar
indices = {dict_learning: 25, canica: 33}
# We select relevant cut coordinates for displaying
cut_component = index_img(components_imgs[0], indices[dict_learning])
cut_coords = find_xyz_cut_coords(cut_component)
for estimator, components in zip(estimators, components_imgs):
    # 4D plotting
    plot_prob_atlas(components, view_type="filled_contours",
                    title="%s" % names[estimator],
                    cut_coords=cut_coords, colorbar=False)
    # 3D plotting
    plot_stat_map(index_img(components, indices[estimator]),
                  title="%s" % names[estimator],
                  cut_coords=cut_coords, colorbar=False)
show()

                          black_bg=True, display_mode='xz', threshold=3)


###############################################################################
# Plotting the sign of the activation
plotting.plot_glass_brain(localizer_tmap_filename, threshold=0, colorbar=True,
                          plot_abs=False)


###############################################################################
# The sign of the activation and a colorbar
plotting.plot_glass_brain(localizer_tmap_filename, threshold=3,
                          colorbar=True, plot_abs=False)


###############################################################################
# Different projections for the left and right hemispheres
# ---------------------------------------------------------
#
# Hemispheric sagittal cuts
plotting.plot_glass_brain(localizer_tmap_filename,
                          title='plot_glass_brain with display_mode="lzr"',
                          black_bg=True, display_mode='lzr', threshold=3)

###############################################################################
plotting.plot_glass_brain(localizer_tmap_filename, threshold=0, colorbar=True,
                          title='plot_glass_brain with display_mode="lyrz"',
                          plot_abs=False, display_mode='lyrz')

plotting.show()