def test_map_to_labels(source_lab, target_lab, kwds, out, expects):
if expects:
with pytest.raises(expects):
parc.map_to_labels(source_lab, target_lab, **kwds)
else:
res = parc.map_to_labels(source_lab, target_lab, **kwds)
assert np.all((res == out) | (np.isnan(out) & np.isnan(out)))
assert res.dtype == out.dtype
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):
gm = GradientMaps(kernel=k, approach='dm', random_state=0)
gm.fit(conn_matrix)
gradients_kernel[i] = map_to_labels(gm.gradients_[:, i],
labeling,
mask=mask,
fill=np.nan)
label_text = ['Pearson', 'Spearman', 'Normalized\nAngle']
plot_hemispheres(surf_lh,
surf_rh,
array_name=gradients_kernel,
size=(1200, 800),
cmap='viridis_r',
color_bar=True,
label_text=label_text)
###############################################################################
# It seems the gradients provided by these kernels are quite similar although
# their scaling is quite different. Do note that the gradients are in arbitrary
# units, so the smaller/larger axes across kernels do not imply anything.
# 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)
# Visualization
from brainspace.datasets import load_group_fc, load_parcellation, load_conte69
from brainspace.gradient import GradientMaps
from brainspace.plotting import plot_hemispheres
from brainspace.utils.parcellation import map_to_labels
atlas = np.load("Z:\\hschoi\\backup\\hschoi\\template\\MMP\\MMP.10k_fs_LR.npy")
pc_num = 2
ref_PCs = ref_PC[:, pc_num]
X = ref_PCs
labeling = atlas
conn_matrix = X # X # ref_PCs
mask = labeling != 0
grad = map_to_labels(conn_matrix, labeling, mask=mask, fill=np.nan)
plot_hemispheres(
surf_lh,
surf_rh,
array_name=grad,
size=(1300, 200),
color_bar=True,
cmap='jet',
zoom=1.85
) #'jet' # 'viridis_r', 'Blues', , 'seismic' # color_range = (-0.1,0.16)
g1 = gm.gradients_[:, 0]
print(g1.shape)
#plt.imshow(g1)
#plt.show()
#print(g1)
print(np.shape(gm.gradients_[:, 1]))
mask = labeling != 0
grad = [None] * 4
for i in range(4):
# map the gradient to the parcels
grad[i] = map_to_labels(gm.gradients_[:, i],
labeling,
mask=mask,
fill=np.nan)
# Plot first gradient on the cortical surface.
#plot_hemispheres(surf_lh, surf_rh, array_name=grad, size=(1200, 600), label_text=['Grad1', 'Grad2', 'Grad3', 'Grad4'])
#plt.scatter(range(gm.lambdas_.size), gm.lambdas_)
#plt.show()
"""
img=nib.load('brainspace/datasets/surfaces/mmpL.func.gii')
img_data_L = [x.data for x in img.darrays]
img=nib.load('brainspace/datasets/surfaces/mmpR.func.gii')
img_data_R = [x.data for x in img.darrays]
from brainspace.gradient import GradientMaps
gm = GradientMaps(n_components=2, random_state=0)
gm.fit(correlation_matrix)
###############################################################################
# Visualize results
from brainspace.datasets import load_fsa5
from brainspace.plotting import plot_hemispheres
from brainspace.utils.parcellation import map_to_labels
# Map gradients to original parcels
grad = [None] * 2
for i, g in enumerate(gm.gradients_.T):
grad[i] = map_to_labels(g, labeling, mask=mask, fill=np.nan)
# Load fsaverage5 surfaces
surf_lh, surf_rh = load_fsa5()
# sphinx_gallery_thumbnail_number = 2
plot_hemispheres(surf_lh,
surf_rh,
array_name=grad,
size=(1200, 400),
cmap='viridis_r',
color_bar=True,
label_text=['Grad1', 'Grad2'],
zoom=1.5)
###############################################################################
plt.xlabel("t-statistic", fontdict={"fontsize": 16})
plt.ylabel("WFDC1 expression", fontdict={"fontsize": 16})
plt.plot(np.unique(df.x),
np.poly1d(np.polyfit(df.x, df.y, 1))(np.unique(df.x)), "k")
plt.text(-1.0, 0.75, f"r={df.x.corr(df.y):.2f}", fontdict={"size": 14})
plt.show()
########################################################################
# Plot WFDC1 gene to the surface.
from brainspace.plotting.surface_plotting import plot_hemispheres
from brainspace.utils.parcellation import map_to_labels
vertexwise_WFDC1 = map_to_labels(
expression["WFDC1"].to_numpy(),
schaefer_100_fs5,
mask=schaefer_100_fs5 != 0,
fill=np.nan,
)
plot_hemispheres(
surfaces[0],
surfaces[1],
vertexwise_WFDC1,
color_bar=True,
embed_nb=True,
size=(1400, 200),
zoom=1.45,
nan_color=(0.7, 0.7, 0.7, 1),
cb__labelTextProperty={"fontSize": 12},
)
# show plot
plt.show()
# ## Functional gradients to fsaverage5 surface
# In[152]:
# Mask of the medial wall on fsaverage 5
mask_fs5 = labels_fs5 != 0
# Map gradients to original parcels
grad = [None] * 3
for i, g in enumerate(gm.gradients_.T[0:3,:]):
grad[i] = map_to_labels(g, labels_fs5, fill=np.nan, mask=mask_fs5)
# Plot Gradients RdYlBu
plot_hemispheres(fs5_lh, fs5_rh, array_name=grad, size=(1000, 600), cmap='coolwarm',
embed_nb=True, label_text={'left':['Grad1', 'Grad2','Grad3']}, color_bar='left',
zoom=1.25, nan_color=(1, 1, 1, 1) )
# ## Functional gradients to conte69 surface
# In[153]:
# mask of the medial wall
mask_c69 = labels_c69 != 0
