def test_four_surfaces_square_max(): surfaces_1 = load_conte69() surfaces_2 = load_conte69(as_sphere=True) t, names = temp_surfaces(surfaces_1 + surfaces_2) namesArr = np.reshape(np.array(names, ndmin=2),(2,2)) dummy_test(namesArr, np.fmax) for i in range(0,len(t)): t[i].close()
def test_two_surfaces_row_plus(): surfaces = load_conte69() t, names = temp_surfaces(surfaces) namesArr = np.array(names, ndmin=2) dummy_test(namesArr) for i in range(0,len(t)): t[i].close()
def test_12(): surf, _ = load_conte69() p = np.random.randint(1, 10) n = np.random.randint(2, 10) A = np.random.rand(n, 32492) B = np.random.rand(n, p) B[:, 0] = 1 # Constant term. B = Term(B) dummy_test(A, B, surf)
# <https://www.sciencedirect.com/science/article/pii/S1053811918304968>`_, # which preserves the auto-correlation of the permuted feature(s) by rotating # the feature data on the spherical domain. # We will start by loading the conte69 surfaces for left and right hemispheres, # their corresponding spheres, midline mask, and t1w/t2w intensity as well as # cortical thickness data, and a template functional gradient. import warnings warnings.simplefilter('ignore') import numpy as np from brainspace.datasets import load_gradient, load_marker, load_conte69 # load the conte69 hemisphere surfaces and spheres surf_lh, surf_rh = load_conte69() sphere_lh, sphere_rh = load_conte69(as_sphere=True) # Load the data t1wt2w_lh, t1wt2w_rh = load_marker('t1wt2w') t1wt2w = np.concatenate([t1wt2w_lh, t1wt2w_rh]) thickness_lh, thickness_rh = load_marker('thickness') thickness = np.concatenate([thickness_lh, thickness_rh]) # Template functional gradient embedding = load_gradient('fc', idx=0, join=True) ############################################################################### # Let’s first generate some null data using spintest.
############################################################################### # As before, we’ll start by loading the sample data. import warnings warnings.simplefilter('ignore') from brainspace.datasets import load_group_fc, load_parcellation, load_conte69 # First load mean connectivity matrix and Schaefer parcellation conn_matrix = load_group_fc('schaefer', scale=400) labeling = load_parcellation('schaefer', scale=400, join=True) mask = labeling != 0 # and load the conte69 hemisphere surfaces surf_lh, surf_rh = load_conte69() ############################################################################### # The GradientMaps object allows for many different kernels and dimensionality # reduction techniques. Let’s have a look at three different kernels. import numpy as np from brainspace.gradient import GradientMaps from brainspace.plotting import plot_hemispheres from brainspace.utils.parcellation import map_to_labels kernels = ['pearson', 'spearman', 'normalized_angle'] gradients_kernel = [None] * len(kernels) for i, k in enumerate(kernels):
# Load the Schaefer 400 atlas schaefer_400 = load_parcellation("schaefer", scale=400, join=True) # Run the analysis histology_profiles = read_histology_profile(template="fs_LR_64k") mpc = compute_mpc(histology_profiles, labels=schaefer_400) gradient_map = compute_histology_gradients(mpc) ######################################################################## # Lets plot the first gradient of histology to see what it looks like. # We will use BrainSpace to create our plots. For full details on how # BrainSpace's plotting functionality works, please consult the BrainSpace # ReadTheDocs. (NOTE: Temporarily disabled due to build errors) from brainspace.plotting.surface_plotting import plot_hemispheres from brainspace.utils.parcellation import map_to_labels from brainspace.datasets import load_conte69 left_surface, right_surface = load_conte69() vertexwise_data = [] for i in range(0, 2): vertexwise_data.append( map_to_labels( gradient_map.gradients_[:, i], schaefer_400, mask=schaefer_400 != 0, fill=np.nan, ) ) # plot_hemispheres(left_surface, right_surface, vertexwise_data, embed_nb=True)
'#8EA06F', '#8C9D70', '#8B9B71', '#8A9972', '#899673', '#889475', '#879176', '#868F77', '#858C78', '#848A79', '#82877A', '#81857C', '#80827D', '#7F807E', '#807D7D', '#827A7A', '#857777', '#877575', '#8A7272', '#8C6F6F', '#8F6C6C', '#916969', '#946666', '#966464', '#996161', '#9B5E5E', '#9D5B5B', '#A05858', '#A25656', '#A55353', '#A75050', '#AA4D4D', '#AC4A4A', '#AF4747', '#B14545', '#B44242', '#B63F3F', '#B93C3C', '#BB3939', '#BE3636', '#C03434', '#C33131', '#C52E2E', '#C82B2B', '#CA2828', '#CD2626' ]) # Load fsaverage5 inflated fs5I_lh = read_surface(dir_fS + 'fsaverage5/surf/lh.inflated', itype='fs') fs5I_rh = read_surface(dir_fS + 'fsaverage5/surf/rh.inflated', itype='fs') # Load conte69 c69_lh, c69_rh = load_conte69() # Load native mid surface mid_lh, mid_rh = load_surface( dir_fS + subBIDS + '/surf/lh.midthickness.surf.gii', dir_fS + subBIDS + '/surf/rh.midthickness.surf.gii', with_normals=True, join=False) # Load native surface surf_lh = read_surface(dir_fS + subBIDS + '/surf/lh.pial', itype='fs') surf_rh = read_surface(dir_fS + subBIDS + '/surf/rh.pial', itype='fs') # Load native white matter surface wm_lh = read_surface(dir_fS + subBIDS + '/surf/lh.white', itype='fs') wm_rh = read_surface(dir_fS + subBIDS + '/surf/rh.white', itype='fs')
def test_bspolydata(): surf, _ = load_conte69() dummy_test(surf)