Пример #1
0
def make_parcellation_surf_from_files(beta_files, mesh_file, parcellation_file,
                                      nbparcel, method, mu=10., verbose=0):

    if method not in ['ward', 'gkm', 'ward_and_gkm', 'kmeans']:
        raise ValueError('unknown method')

    # step 1: load the data ----------------------------
    # 1.1 the domain
    logger.info('domain from mesh: %s', mesh_file)
    domain = domain_from_mesh(mesh_file)

    coord = domain.coord

    # 1.3 read the functional data
    beta = np.array([read_texture(b)[0] for b in beta_files]).T

    logger.info('beta: %s', str(beta.shape))
    logger.info('mu * coord / np.std(coord): %s',
                (mu * coord / np.std(coord)).shape)
    feature = np.hstack((beta, mu * coord / np.std(coord)))

    if method is not 'kmeans':
        g = field_from_coo_matrix_and_data(domain.topology, feature)

    if method == 'kmeans':
        _, u, _ = kmeans(feature, nbparcel)

    if method == 'ward':
        u, _ = g.ward(nbparcel)

    if method == 'gkm':
        seeds = np.argsort(np.random.rand(g.V))[:nbparcel]
        _, u, _ = g.geodesic_kmeans(seeds)

    if method == 'ward_and_gkm':
        w, _ = g.ward(nbparcel)
        _, u, _ = g.geodesic_kmeans(label=w)

    lpa = SubDomains(domain, u, 'parcellation')

    if verbose:
        var_beta = np.array(
            [np.var(beta[lpa.label == k], 0).sum() for k in range(lpa.k)])
        var_coord = np.array(
            [np.var(coord[lpa.label == k], 0).sum() for k in range(lpa.k)])
        size = lpa.get_size()
        vf = np.dot(var_beta, size) / size.sum()
        va = np.dot(var_coord, size) / size.sum()
        print nbparcel, "functional variance", vf, "anatomical variance", va

    # step3:  write the resulting label image
    if parcellation_file is not None:
        label_image = parcellation_file
    else:
        label_image = None

    if label_image is not None:
        write_texture(u.astype(np.int32), label_image)
        if verbose:
            print "Wrote the parcellation images as %s" % label_image

    return u, label_image
Пример #2
0
def make_parcellation_surf_from_files(beta_files, mesh_file, parcellation_file,
                                      nbparcel, method, mu=10., verbose=0):

    if method not in ['ward', 'gkm', 'ward_and_gkm', 'kmeans']:
        raise ValueError('unknown method')


    # step 1: load the data ----------------------------
    # 1.1 the domain
    pyhrf.verbose(3, 'domain from mesh: %s' %mesh_file)
    domain = domain_from_mesh(mesh_file)

    coord = domain.coord

    # 1.3 read the functional data
    beta = np.array([read_texture(b)[0] for b in beta_files]).T

    pyhrf.verbose(3, 'beta: %s' %str(beta.shape))
    pyhrf.verbose(3, 'mu * coord / np.std(coord): %s' \
                      %(mu * coord / np.std(coord)).shape)
    feature = np.hstack((beta, mu * coord / np.std(coord)))

    if method is not 'kmeans':
        # print 'domain.topology:', domain.topology.__class__
        # print domain.topology
        #print dir(domain.topology)
        # print 'feature:', feature.shape
        # print feature
        g = field_from_coo_matrix_and_data(domain.topology, feature)
        # print 'g:', g.__class__
        # print g


    if method == 'kmeans':
        _, u, _ = kmeans(feature, nbparcel)

    if method == 'ward':
        u, _ = g.ward(nbparcel)

    if method == 'gkm':
        seeds = np.argsort(np.random.rand(g.V))[:nbparcel]
        _, u, _ = g.geodesic_kmeans(seeds)

    if method == 'ward_and_gkm':
        w, _ = g.ward(nbparcel)
        _, u, _ = g.geodesic_kmeans(label=w)

    # print 'u:'
    # print u

    lpa = SubDomains(domain, u, 'parcellation')

    if verbose:
        var_beta = np.array(
            [np.var(beta[lpa.label == k], 0).sum() for k in range(lpa.k)])
        var_coord = np.array(
            [np.var(coord[lpa.label == k], 0).sum() for k in range(lpa.k)])
        size = lpa.get_size()
        vf = np.dot(var_beta, size) / size.sum()
        va = np.dot(var_coord, size) / size.sum()
        print nbparcel, "functional variance", vf, "anatomical variance", va

    # step3:  write the resulting label image
    if parcellation_file is not None:
        label_image = parcellation_file
    # elif write_dir is not None:
    #     label_image = os.path.join(write_dir, "parcel_%s.nii" % method)
    else:
        label_image = None

    if label_image is not None:
        #lpa.to_image(label_image, descrip='Intra-subject parcellation image')
        write_texture(u.astype(np.int32), label_image)
        if verbose:
            print "Wrote the parcellation images as %s" % label_image

    return u, label_image
Пример #3
0
def make_parcellation_surf_from_files(beta_files,
                                      mesh_file,
                                      parcellation_file,
                                      nbparcel,
                                      method,
                                      mu=10.,
                                      verbose=0):

    if method not in ['ward', 'gkm', 'ward_and_gkm', 'kmeans']:
        raise ValueError('unknown method')

    # step 1: load the data ----------------------------
    # 1.1 the domain
    logger.info('domain from mesh: %s', mesh_file)
    domain = domain_from_mesh(mesh_file)

    coord = domain.coord

    # 1.3 read the functional data
    beta = np.array([read_texture(b)[0] for b in beta_files]).T

    logger.info('beta: %s', str(beta.shape))
    logger.info('mu * coord / np.std(coord): %s',
                (mu * coord / np.std(coord)).shape)
    feature = np.hstack((beta, mu * coord / np.std(coord)))

    if method is not 'kmeans':
        g = field_from_coo_matrix_and_data(domain.topology, feature)

    if method == 'kmeans':
        _, u, _ = kmeans(feature, nbparcel)

    if method == 'ward':
        u, _ = g.ward(nbparcel)

    if method == 'gkm':
        seeds = np.argsort(np.random.rand(g.V))[:nbparcel]
        _, u, _ = g.geodesic_kmeans(seeds)

    if method == 'ward_and_gkm':
        w, _ = g.ward(nbparcel)
        _, u, _ = g.geodesic_kmeans(label=w)

    lpa = SubDomains(domain, u, 'parcellation')

    if verbose:
        var_beta = np.array(
            [np.var(beta[lpa.label == k], 0).sum() for k in range(lpa.k)])
        var_coord = np.array(
            [np.var(coord[lpa.label == k], 0).sum() for k in range(lpa.k)])
        size = lpa.get_size()
        vf = np.dot(var_beta, size) / size.sum()
        va = np.dot(var_coord, size) / size.sum()
        print nbparcel, "functional variance", vf, "anatomical variance", va

    # step3:  write the resulting label image
    if parcellation_file is not None:
        label_image = parcellation_file
    else:
        label_image = None

    if label_image is not None:
        write_texture(u.astype(np.int32), label_image)
        if verbose:
            print "Wrote the parcellation images as %s" % label_image

    return u, label_image