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)