def reduce3_to_bci_lh(reduce3labs):
class h32k:
pass
class h:
pass
class s:
pass
class bs:
pass
class bci:
pass
''' reduce3 to h32k'''
r3 = readdfs('lh.Yeo2011_17Networks_N1000_reduce3.dfs')
r3.labels = np.squeeze(reduce3labs.T)
'''h32k to full res FS'''
g_surf = nib.load('/big_disk/ajoshi/HCP_data/reference/100307/MNINonLinea\
r/Native/100307.L.very_inflated.native.surf.gii')
h.vertices = g_surf.darrays[0].data
h.faces = g_surf.darrays[1].data
h = interpolate_labels(r3, h)
''' native FS ref to native FS BCI'''
g_surf = nib.load('/big_disk/ajoshi/HCP_data/reference/100307/MNINon\
Linear/Native/100307.L.sphere.reg.native.surf.gii')
s.vertices = g_surf.darrays[0].data
s.faces = g_surf.darrays[1].data
s.labels = h.labels
''' map to bc sphere'''
bs.vertices, bs.faces = fsio.read_geometry('/big_disk/ajoshi/data/BCI\
_DNI_Atlas/surf/lh.sphere.reg')
bs = interpolate_labels(s, bs)
bci.vertices, bci.faces = fsio.read_geometry(
'/big_disk/ajoshi/data/BCI_DNI_A\
tlas/surf/lh.white')
bci.labels = bs.labels
# writedfs('BCI_orig_rh.dfs', bci)
bci.vertices, bci.faces = fsio.read_geometry(
'/big_disk/ajoshi/data/BCI_DNI_A\
tlas/surf/lh.inflated')
# view_patch(bci, bci.labels)
# writedfs('BCI_pial_rh.dfs.', bci)
bci.vertices, bci.faces = fsio.read_geometry('/big_disk/ajoshi/data/BCI_\
DNI_Atlas/surf/lh.white')
# writedfs('BCI_white_rh.dfs.', bci)
bci.vertices[:, 0] += 96 * 0.8
bci.vertices[:, 1] += 192 * 0.546875
bci.vertices[:, 2] += 192 * 0.546875
bci_bst = readdfs('/big_disk/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_\
brain.left.inner.cortex.dfs')
bci_bst = interpolate_labels(bci, bci_bst)
labs = bci_bst.labels
return labs
def sub(file1, file2, fileout):
s1 = dfsio.readdfs(file1)
s2 = dfsio.readdfs(file2)
sout = s1
if not s1.attributes.any() and not s2.attributes.any():
raise excepts.AttributeMissingError(
'Missing attributes in {0:s} and/or {1:s}'.format(
file1,
file2)) # TODO: Change this to a custom exception in future
if len(s1.attributes) != len(s2.attributes):
raise excepts.AttributeLengthError(
'Attribute lengths of {0:s} and {1:s} do not match.\n'.format(
file1, file2))
sout.attributes = s1.attributes - s2.attributes
dfsio.writedfs(fileout, sout)
def ALL_parcellate_region(roilist, sub, R_all, scan_type):
p_dir = '/home/ajoshi/data/HCP_data'
r_factor = 3
ref_dir = os.path.join(p_dir, 'reference')
ref = '100307'
fn1 = ref + '.reduce' + str(r_factor) + '.LR_mask.mat'
fname1 = os.path.join(ref_dir, fn1)
msk = scipy.io.loadmat(fname1) # h5py.File(fname1);
'''dfs_left = readdfs(os.path.join(p_dir, 'reference', ref + '.aparc.\
a2009s.32k_fs.reduce3.left.dfs'))
dfs_left_sm = readdfs(os.path.join(p_dir, 'reference', ref + '.aparc.\
a2009s.32k_fs.reduce3.very_smooth.left.dfs'))'''
dfs_left_sm = readdfs(
os.path.join('/home/ajoshi/for_gaurav',
'100307.BCI2reduce3.very_smooth.' + scan_type + '.dfs'))
dfs_left = readdfs(
os.path.join('/home/ajoshi/for_gaurav',
'100307.BCI2reduce3.very_smooth.' + scan_type + '.dfs'))
data = scipy.io.loadmat(
os.path.join(
p_dir, 'data', sub, sub + '.rfMRI_REST' + str(1) + '_RL.\
reduce3.ftdata.NLM_11N_hvar_25.mat'))
LR_flag = msk['LR_flag']
# 0= right hemisphere && 1== left hemisphere
if scan_type == 'right':
LR_flag = np.squeeze(LR_flag) == 0
else:
LR_flag = np.squeeze(LR_flag) == 1
data = data['ftdata_NLM']
temp = data[LR_flag, :]
m = np.mean(temp, 1)
temp = temp - m[:, None]
s = np.std(temp, 1) + 1e-16
temp = temp / s[:, None]
msk_small_region = np.in1d(dfs_left.labels, roilist)
if R_all.size == 0:
R_all = temp
else:
R_all = np.concatenate((R_all, temp), axis=1)
print R_all.shape[1]
return (R_all, msk_small_region, dfs_left_sm.vertices, dfs_left_sm.faces)
def RI_mean(lst):
import scipy as sp
roilists = []
scan_type = ['left', 'right']
for hemi in range(0, 1):
direct = np.load('very_smooth_data_' + scan_type[hemi] + '.npz')
if roilists.__len__() == 0:
roilists = direct['roilists'].tolist()
else:
roilists += direct['roilists'].tolist()
map_roilists = {}
for i in range(left_hemisphere.shape[0]):
map_roilists[left_hemisphere[i]] = roiregion[i]
refined_left = readdfs(
os.path.join('/home/ajoshi/for_gaurav',
'100307.BCI2reduce3.very_smooth.left.dfs'))
refined_left = refined_left.labels
refined_right = readdfs(
os.path.join('/home/ajoshi/for_gaurav',
'100307.BCI2reduce3.very_smooth.right.dfs'))
refined_right = refined_right.labels
temp = []
temp1 = []
cnt = 0
for roilist in map_roilists.viewkeys():
#print roilist
cnt += 1
# msk_small_region = np.in1d(refined_right,roilist)
msk_small_region = np.in1d(refined_left, roilist)
if temp.__len__() > 0:
# temp = sp.vstack([calculate_mean(msk_small_region, sub, scan_type[1]), temp])
temp = sp.vstack(
[dir_session(msk_small_region, scan_type[0], lst), temp])
temp1 = sp.vstack([
session_to_session(msk_small_region, scan_type[0], lst), temp1
])
else:
# temp=calculate_mean(msk_small_region,sub,scan_type[1])
temp = dir_session(msk_small_region, scan_type[0], lst)
temp1 = session_to_session(msk_small_region, scan_type[0], lst)
# msk_small_region = np.in1d(refined_left, roilist+1)
# temp=sp.vstack([calculate_mean(msk_small_region,sub,scan_type[0]),temp])
box_plot(temp.tolist(), temp1.tolist(), map_roilists, '60')
def dfs(filename):
NFV = dfsio.readdfs(filename)
coords = NFV.vertices
faces = NFV.faces
if hasattr(NFV, 'attributes'):
attributes = NFV.attributes
else:
attributes = []
isMultilevelUCF = False
return coords, faces, attributes, isMultilevelUCF
def __init__(self, demographics_file, model, max_block_size=2000):
self.demographic_data = ''
self.dataframe = None
self.phenotype_files = []
self.phenotype_array = None
self.pre_data_frame = {}
self.surface_average = None
self.phenotype_dataframe = None
# self.attribue_matrix = None
self.phenotype_array = []
self.demographic_data = self.read_demographics(demographics_file)
self.data_read_flag = False
self.max_block_size = max_block_size
# if not model.phenotype_attribute_matrix_file and not model.phenotype:
# sys.stdout.write('Error: Phenotype is not set. Data frame will not be created.')
# return
# # Choose the phenotype_attribute_matrix binary data if phenotype is also set
# if model.phenotype_attribute_matrix_file and model.phenotype:
# self.read_subject_phenotype_attribute_matrix(model)
# self.create_data_frame(model)
# return
# if model.phenotype:
# self.read_subject_phenotype(model)
s1_atlas = dfsio.readdfs(model.atlas_surface)
self.phenotype_array = self.read_aggregated_attributes_from_surfacefilelist(self.demographic_data[model.fileid],
s1_atlas.vertices.shape[0])
if len(self.phenotype_array) == 0:
self.data_read_flag = False
else:
self.data_read_flag = True
# self.create_data_frame(model)
self.blocks_idx = []
# At this point the data is completely read, so create indices of blocks
if self.phenotype_array.shape[1] > self.max_block_size:
quotient, remainder = divmod(self.phenotype_array.shape[1], self.max_block_size)
for i in np.arange(quotient)+1:
self.blocks_idx.append(((i-1)*self.max_block_size, (i-1)*self.max_block_size + self.max_block_size))
if remainder != 0:
i = quotient + 1
self.blocks_idx.append(((i-1)*self.max_block_size, (i-1)*self.max_block_size + remainder))
else:
self.blocks_idx.append((0, self.phenotype_array.shape[1]))
return
def label_surf(pval, colorbar_lim, smooth_iter, colormap, bfp_path='.'):
lsurf = readdfs(os.path.join(bfp_path, 'supp_data/bci32kleft.dfs'))
rsurf = readdfs(os.path.join(bfp_path, 'supp_data/bci32kright.dfs'))
num_vert = lsurf.vertices.shape[0]
lsurf.attributes = sp.zeros((lsurf.vertices.shape[0]))
rsurf.attributes = sp.zeros((rsurf.vertices.shape[0]))
if VTK_INSTALLED:
#smooth surfaces
lsurf = smooth_patch(lsurf, iterations=smooth_iter)
rsurf = smooth_patch(rsurf, iterations=smooth_iter)
else:
print('VTK is not installed, surface will not be smoothed')
# write on surface attributes
lsurf.attributes = pval.squeeze()
lsurf.attributes = lsurf.attributes[:num_vert]
rsurf.attributes = pval.squeeze()
rsurf.attributes = rsurf.attributes[num_vert:2 * num_vert]
lsurf = patch_color_attrib(lsurf, clim=colorbar_lim, cmap=colormap)
rsurf = patch_color_attrib(rsurf, clim=colorbar_lim, cmap=colormap)
return lsurf, rsurf
def read_dfs_to_label(path, shape):
'''
read .dfs file and return 3d matrix of label
:param path: (String) file path
:param shape: (tuple) img shape
:return: (np.array) 3d matrix of label
'''
reader = dfsio.readdfs(path)
vertices = reader.vertices
#print(vertices)
#print('vertices shape {0}'.format(vertices.shape))
vertices = vertices.astype(np.int32)
#print(vertices.astype(np.int32))
labels = np.zeros(shape)
for vertix in vertices:
labels[vertix[0]][vertix[1]][vertix[2]] = 1
return labels
def save(self, outdir, outprefix, atlas_filename):
sys.stdout.write('Saving output files...\n')
self.statsresult.adjust_for_multi_comparisons()
s1 = dfsio.readdfs(atlas_filename)
s1.attributes = self.statsresult.pvalues
# print s1.attributes
if len(s1.attributes) == s1.vertices.shape[0]:
# Also write color to the field
s1.vColor = colormaps.Colormap.get_rgb_color_array(
'pvalue', s1.attributes)
dfsio.writedfs(
os.path.join(outdir, outprefix + '_atlas_pvalues.dfs'), s1)
if len(self.statsresult.pvalues_adjusted) > 0:
s1.attributes = self.statsresult.pvalues_adjusted
# Also write color to the field
s1.vColor = colormaps.Colormap.get_rgb_color_array(
'pvalue', s1.attributes)
dfsio.writedfs(
os.path.join(outdir,
outprefix + '_atlas_pvalues_adjusted.dfs'),
s1)
else:
sys.stdout.write(
'Error: Dimension mismatch between the p-values and the number of vertices. '
'Quitting without saving.\n')
if len(self.statsresult.corrvalues) > 0:
s1.attributes = self.statsresult.corrvalues
s1.vColor = colormaps.Colormap.get_rgb_color_array(
'corr', s1.attributes)
dfsio.writedfs(os.path.join(outdir, outprefix + '_corr.dfs'), s1)
self.statsresult.corrvalues[
np.abs(self.statsresult.pvalues_adjusted) > 0.05] = 0
s1.attributes = self.statsresult.corrvalues
# Also write color to the field
s1.vColor = colormaps.Colormap.get_rgb_color_array(
'corr', s1.attributes)
dfsio.writedfs(
os.path.join(outdir, outprefix + '_corr_adjusted.dfs'), s1)
sys.stdout.write('Done.\n')
#%% Atlas to normal subjects diff & Do PCA of ADHD
diffAdhdInatt = sp.zeros([sub_data.shape[1], 50])
fADHD = sp.zeros((NDim, sub_data.shape[1], 50))
for ind in range(50):
Y2, _ = brainSync(X=atlas, Y=sub_data[:, :, ind])
fADHD[:, :, ind] = pca.transform(Y2.T).T
diffAdhdInatt[:, ind] = sp.sum((Y2 - atlas)**2, axis=0)
print(ind, )
spio.savemat('ADHD_diff_adhd_inattentive.mat',
{'diffAdhdInatt': diffAdhdInatt})
#%% Read surfaces for visualization
lsurf = readdfs('/home/ajoshi/coding_ground/bfp/supp_data/bci32kleft.dfs')
rsurf = readdfs('/home/ajoshi/coding_ground/bfp/supp_data/bci32kright.dfs')
a = spio.loadmat(
'/home/ajoshi/coding_ground/bfp/supp_data/USCBrain_grayord_labels.mat')
labs = a['labels']
lsurf.attributes = np.zeros((lsurf.vertices.shape[0]))
rsurf.attributes = np.zeros((rsurf.vertices.shape[0]))
lsurf = smooth_patch(lsurf, iterations=1500)
rsurf = smooth_patch(rsurf, iterations=1500)
labs[sp.isnan(labs)] = 0
diff = diff * (labs.T > 0)
diffAdhdInatt = diffAdhdInatt * (labs.T > 0)
nVert = lsurf.vertices.shape[0]
#%% Visualization of normal diff from the atlas
def save_surface(self, atlas_filename):
s1 = dfsio.readdfs(atlas_filename)
if self.mask_idx.any():
pvalues = np.ones(s1.vertices.shape[0])
pvalues[self.mask_idx] = self.statsresult.pvalues
self.statsresult.pvalues = pvalues
tvalues = np.zeros(s1.vertices.shape[0])
tvalues[self.mask_idx] = self.statsresult.tvalues
self.statsresult.tvalues = tvalues
s1.attributes = self.statsresult.pvalues
# print s1.attributes
if len(s1.attributes) == s1.vertices.shape[0]:
# Also write color to the field
self.statsresult.pvalues = log10_transform(
self.statsresult.pvalues)
s1.vColor, pex, cmap = colormaps.Colormap.log_pvalues_to_rgb(
self.statsresult.pvalues)
s1.attributes = self.statsresult.pvalues
dfsio.writedfs(
os.path.join(self.outdir,
self.outprefix + '_atlas_log_pvalues.dfs'), s1)
colormaps.Colormap.save_colorbar(file=os.path.join(
self.outdir, self.outprefix + '_atlas_log_pvalues_cbar.pdf'),
cmap=cmap,
vmin=-1 * pex,
vmax=pex,
labeltxt='Unadjusted p-values')
s1.attributes = self.statsresult.tvalues
s1.vColor, tmin, tmax, cmap_tvalues = colormaps.Colormap.tvalues_to_rgb(
self.statsresult.tvalues)
dfsio.writedfs(
os.path.join(self.outdir,
self.outprefix + '_atlas_tvalues_all.dfs'), s1)
colormaps.Colormap.save_colorbar(file=os.path.join(
self.outdir, self.outprefix + '_atlas_tvalues_all_cbar.pdf'),
cmap=cmap_tvalues,
vmin=tmin,
vmax=tmax,
labeltxt='t-values (all)')
self.statsresult.tvalues[np.abs(self.statsresult.pvalues) <= -1 *
np.log10(0.05)] = 0
s1.vColor, tmin, tmax, cmap_tvalues = colormaps.Colormap.tvalues_to_rgb(
self.statsresult.tvalues)
s1.attributes = self.statsresult.tvalues
dfsio.writedfs(
os.path.join(self.outdir,
self.outprefix + '_atlas_tvalues.dfs'), s1)
colormaps.Colormap.save_colorbar(file=os.path.join(
self.outdir, self.outprefix + '_atlas_tvalues_cbar.pdf'),
cmap=cmap_tvalues,
vmin=tmin,
vmax=tmax,
labeltxt='t-values (unadjusted)')
LUT = cmap_tvalues._lut[0:256, 0:3]
colormaps.Colormap.exportBrainSuiteLUT(
os.path.join(self.outdir,
self.outprefix + '_atlas_tvalues_cbar.lut'), LUT)
with open(
os.path.join(
self.outdir,
self.outprefix + '_unadjusted_pvalue_range.txt'),
"wt") as text_file:
text_file.write("Log P-value range: -{0:s} to +{1:s}\n".format(
str(pex), str(pex)))
text_file.write("P-value range: {0:s} to +{1:s}\n".format(
str(-1 * 10**(-1 * pex)), str(10**(-1 * pex))))
if len(self.statsresult.pvalues_adjusted) > 0:
if self.mask_idx.any():
pvalues = np.ones(s1.vertices.shape[0])
pvalues[self.mask_idx] = self.statsresult.pvalues_adjusted
self.statsresult.pvalues_adjusted = pvalues
s1.attributes = self.statsresult.pvalues_adjusted
self.statsresult.pvalues_adjusted = log10_transform(
self.statsresult.pvalues_adjusted)
s1.vColor, pex, cmap = colormaps.Colormap.log_pvalues_to_rgb(
self.statsresult.pvalues_adjusted)
s1.attributes = self.statsresult.pvalues_adjusted
dfsio.writedfs(
os.path.join(
self.outdir,
self.outprefix + '_atlas_log_pvalues_adjusted.dfs'),
s1)
colormaps.Colormap.save_colorbar(file=os.path.join(
self.outdir,
self.outprefix + '_atlas_log_pvalues_adjusted_cbar.pdf'),
cmap=cmap,
vmin=-1 * pex,
vmax=pex,
labeltxt='Adjusted p-values')
self.statsresult.tvalues[
np.abs(self.statsresult.pvalues_adjusted) < -1 *
np.log10(0.05)] = 0
s1.vColor, tmin, tmax, cmap_tvalues = colormaps.Colormap.tvalues_to_rgb(
self.statsresult.tvalues)
s1.attributes = self.statsresult.tvalues
dfsio.writedfs(
os.path.join(
self.outdir,
self.outprefix + '_atlas_tvalues_adjusted.dfs'), s1)
colormaps.Colormap.save_colorbar(
file=os.path.join(
self.outdir,
self.outprefix + '_atlas_tvalues_adjusted_cbar.pdf'),
cmap=cmap_tvalues,
vmin=tmin,
vmax=tmax,
labeltxt='t-values (adjusted)')
LUT = cmap_tvalues._lut[0:256, 0:3]
colormaps.Colormap.exportBrainSuiteLUT(
os.path.join(
self.outdir,
self.outprefix + '_atlas_tvalues_adjusted_cbar.lut'),
LUT)
with open(
os.path.join(
self.outdir,
self.outprefix + '_adjusted_pvalue_range.txt'),
"wt") as text_file:
text_file.write(
"Log P-value range: -{0:s} to +{1:s}\n".format(
str(pex), str(abs(pex))))
text_file.write("P-value range: {0:s} to +{1:s}\n".format(
str(-1 * 10**(-1 * pex)), str(10**(-1 * pex))))
else:
sys.stdout.write(
'Error: Dimension mismatch between the p-values and the number of vertices. '
'Quitting without saving.\n')
if len(self.statsresult.corrvalues) > 0:
if self.mask_idx.any():
corrvalues = np.zeros(s1.vertices.shape[0])
corrvalues[self.mask_idx] = self.statsresult.corrvalues
self.statsresult.corrvalues = corrvalues
s1.attributes = self.statsresult.corrvalues
s1.vColor, cex, cmap = colormaps.Colormap.correlation_to_rgb(
self.statsresult.corrvalues)
dfsio.writedfs(
os.path.join(self.outdir, self.outprefix + '_corr.dfs'), s1)
colormaps.Colormap.save_colorbar(
file=os.path.join(self.outdir,
self.outprefix + '_corr_cbar.pdf'),
cmap=cmap,
vmin=-1 * cex,
vmax=cex,
labeltxt='Correlations (unadjusted)')
with open(
os.path.join(self.outdir,
self.outprefix + '_corr_range.txt'),
"wt") as text_file:
text_file.write(
"Correlation values range: -{0:s} to +{1:s}\n".format(
str(cex), str(cex)))
# Also write color to the field
self.statsresult.corrvalues[np.abs(
self.statsresult.pvalues_adjusted) < -1 * np.log10(0.05)] = 0
s1.attributes = self.statsresult.corrvalues
s1.vColor, cex, cmap = colormaps.Colormap.correlation_to_rgb(
self.statsresult.corrvalues)
dfsio.writedfs(
os.path.join(self.outdir,
self.outprefix + '_corr_adjusted.dfs'), s1)
colormaps.Colormap.save_colorbar(
file=os.path.join(self.outdir,
self.outprefix + '_corr_adjusted_cbar.pdf'),
cmap=cmap,
vmin=-1 * cex,
vmax=cex,
labeltxt='Correlations (adjusted)')
with open(
os.path.join(self.outdir,
self.outprefix + '_adjusted_corr_range.txt'),
"wt") as text_file:
text_file.write(
"Adjusted Correlation values range: {0:s} to +{1:s}\n".
format(str(cex), str(cex)))
sys.stdout.write('Done.\n')
class bci:
pass
outputfile='gaurav_bci.dfs'
p_dir='/home/sgaurav/Documents/git_sandbox/cortical_parcellation/src/intensity_mode_map'
data1 = scipy.io.loadmat(os.path.join(p_dir, 'intensity_file_cingulate_184_nCluster=3_BCI.mat'))
labs = data1['labs_all']
''' reduce3 to h32k'''
r3 = readdfs('rh.Yeo2011_17Networks_N1000_reduce3.dfs')
r3.labels=np.squeeze(labs.T)
'''h32k to full res FS'''
g_surf = gread('/home/ajoshi/data/HCP_data/reference/100307/MNINonLinear/N\
ative/100307.R.very_inflated.native.surf.gii')
h.vertices = g_surf.darrays[0].data
h.faces = g_surf.darrays[1].data
h = interpolate_labels(r3, h)
''' native FS ref to native FS BCI'''
g_surf = gread('/home/ajoshi/data/HCP_data/reference/100307/MNINonLinear/Nativ\
e/100307.R.sphere.reg.native.surf.gii')
s.vertices = g_surf.darrays[0].data
s.faces = g_surf.darrays[1].data
s.labels = h.labels
p_dir = '/big_disk/ajoshi/HCP_data/data'
p_dir_ref = '/big_disk/ajoshi/HCP_data'
lst = os.listdir(p_dir)
r_factor = 3
ref_dir = os.path.join(p_dir_ref, 'reference')
nClusters = 30
ref = '100307'
print(ref + '.reduce' + str(r_factor) + '.LR_mask.mat')
fn1 = ref + '.reduce' + str(r_factor) + '.LR_mask.mat'
fname1 = os.path.join(ref_dir, fn1)
msk = scipy.io.loadmat(fname1) # h5py.File(fname1);
dfs_left = readdfs(
os.path.join(p_dir_ref, 'reference', ref + '.aparc.\
a2009s.32k_fs.reduce3.left.dfs'))
dfs_left_sm = readdfs(
os.path.join(p_dir_ref, 'reference', ref + '.aparc.\
a2009s.32k_fs.reduce3.very_smooth.left.dfs'))
view_patch_vtk(dfs_left_sm,
azimuth=90,
elevation=180,
roll=90,
outfile='sub.png',
show=1)
count1 = 0
rho_rho = []
rho_all = []
cc_msk = (dfs_left.labels > 0)
# %%
"""
@author: ajoshi
"""
from fmri_methods_sipi import interpolate_labels
from dfsio import readdfs, writedfs
import time
import numpy as np
import scipy as sp
import nibabel as nib
from nilearn import image
subbasename = 'BCI-DNI_DesikanKilliany'
BCI_base = '/home/ajoshi/BrainSuite19b/svreg/BCI-DNI_brain_atlas/BCI-DNI_brain'
left_mid = readdfs(BCI_base + '.left.mid.cortex.dfs')
right_mid = readdfs(BCI_base + '.right.mid.cortex.dfs')
left_inner = readdfs(BCI_base + '.left.inner.cortex.dfs')
right_inner = readdfs(BCI_base + '.right.inner.cortex.dfs')
left_pial = readdfs(BCI_base + '.left.pial.cortex.dfs')
right_pial = readdfs(BCI_base + '.right.pial.cortex.dfs')
lsurf = readdfs(subbasename + '.left.mid.cortex.dfs')
rsurf = readdfs(subbasename + '.right.mid.cortex.dfs')
r1_vert = (right_pial.vertices + right_mid.vertices) / 2.0
r2_vert = (right_inner.vertices + right_mid.vertices) / 2.0
l1_vert = (left_pial.vertices + left_mid.vertices) / 2.0
l2_vert = (left_inner.vertices + left_mid.vertices) / 2.0
vol_lab = image.load_img(
'/home/ajoshi/BrainSuite19b/svreg/BCI-DNI_brain_atlas/BCI-DNI_brain.dws.label.nii.gz'
return tosurf
class s:
pass
class bci:
pass
inputfile = 'rrh.Yeo2011_17Networks_N1000_reduce3.dfs'
outputfile = '100307.reduce3.very_smooth.right.dfs'
''' BCI to FS processed BCI '''
bci_bst = readdfs(
'/home/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.right.inner.cortex.refined.dfs'
)
bci_bst.vertices[:, 0] -= 96 * 0.8
bci_bst.vertices[:, 1] -= 192 * 0.546875
bci_bst.vertices[:, 2] -= 192 * 0.546875
bci.vertices, bci.faces = fsio.read_geometry(
'/home/ajoshi/data/BCI_DNI_Atlas/surf/rh.white')
bci = interpolate_labels(bci_bst, bci)
''' FS_BCI to FS BCI Sphere'''
bci.vertices, bci.faces = fsio.read_geometry(
'/home/ajoshi/data/BCI_DNI_Atlas/surf/rh.sphere.reg')
''' FS BCI Sphere to ref FS Sphere'''
g_surf = gread(
'/big_disk/ajoshi/HCP_data/reference/100307/MNINonLinear/Native/100307.R.sphere.reg.native.surf.gii'
)
s.vertices = g_surf.darrays[0].data
#a.to_filename('outfile_task.nii')
#
#a = nib.cifti2.Cifti2Image(Xtsk-Xnew, sub1.header, file_map=sub1.file_map)
#a.to_filename('outfile_diff.nii')
#loading cifti files has indices garbled
#%%
fname1 = 'right_motor1.png'
fname2 = 'right_motor2.png'
p_dir_ref = '/big_disk/ajoshi/HCP_data/'
ref = '196750' # '100307'
#lsurf = surfObj
ls = readdfs(
os.path.join(p_dir_ref, 'reference', ref + '.aparc.\
a2009s.32k_fs.reduce3.very_smooth.right.dfs'))
lsurf = ls
#lind = np.where(ls.labels > -10)[0]
lsurf.attributes = np.zeros((lsurf.vertices.shape[0]))
#lsurf.attributes = X[150,:lsurf.vertices.shape[0]] #
nVert = lsurf.vertices.shape[0]
diffafter = Xtsk - Xnew
lsurf.attributes = np.sum((diffafter)**2, axis=0)
lsurf.attributes = lsurf.attributes[nVert:]
#lsurf.attributes = smooth_surf_function(lsurf, lsurf.attributes)#, a1=1.1, a2=1.1)
lsurf = patch_color_attrib(lsurf, clim=[1, 2])
view_patch_vtk(lsurf,
import matlab.engine as meng
USCBrainbaseLatest = '/ImagePTE1/ajoshi/code_farm/hybridatlas/USCBrain_9_8'
eng = meng.start_matlab()
eng.addpath(eng.genpath('/ImagePTE1/ajoshi/code_farm/svreg/MEX_Files'))
eng.addpath(eng.genpath('/ImagePTE1/ajoshi/code_farm/svreg/3rdParty'))
eng.addpath(eng.genpath('/ImagePTE1/ajoshi/code_farm/svreg/src'))
xmlf = USCBrainbaseLatest + '/brainsuite_labeldescription.xml'
for hemi in {'left', 'right'}:
mid = USCBrainbaseLatest + '/BCI-DNI_brain.' + hemi + '.mid.cortex.dfs'
eng.recolor_by_label(mid, '', xmlf, nargout=0)
s = readdfs(mid)
sin = readdfs(USCBrainbaseLatest + '/BCI-DNI_brain.' + hemi +
'.inner.cortex.dfs')
spial = readdfs(USCBrainbaseLatest + '/BCI-DNI_brain.' + hemi +
'.pial.cortex.dfs')
sin.vColor = s.vColor
sin.labels = s.labels
writedfs(
USCBrainbaseLatest + '/BCI-DNI_brain.' + hemi + '.inner.cortex.dfs',
sin)
spial.vColor = s.vColor
spial.labels = s.labels
writedfs(
USCBrainbaseLatest + '/BCI-DNI_brain.' + hemi + '.pial.cortex.dfs',
bci.get_fdata().flatten(),
bci_dict,
tiny_threhsold=80)
v = ni.new_img_like(bci, error_indicator1.reshape(bci.shape))
v.to_filename('errorvol.nii.gz')
print('Tiny region overlaps: %d or %d ' %
(np.sum(error_indicator1), np.sum(error_indicator2)))
class error_surf:
pass
# Left hemisphere surface
print('=====Checking Left Hemisphere Surface=====')
uscbrain = readdfs(USCBrainbaseLatest +
'/BCI-DNI_brain.left.mid.cortex.dfs')
bci = readdfs(BCIbase + '/BCI-DNI_brain.left.mid.cortex.dfs')
error_indicator1 = check_uscbrain_bci(uscbrain.labels.flatten(),
uscbrain_dict, bci.labels.flatten(),
bci_dict)
error_indicator2 = check_bci_uscbrain(uscbrain.labels.flatten(),
uscbrain_dict, bci.labels.flatten(),
bci_dict)
error_surf.vertices = bci.vertices
error_surf.faces = bci.faces
error_surf.attributes = 255.0 * error_indicator1
error_surf.labels = error_indicator1
def read_surface(filename, mask_idx=None):
filetype = NimgDataio.validatetype(filename)
if filetype == 'surface':
return dfsio.readdfs(filename)
def parcellate_region(roilist,
sub,
nClusters,
scan,
scan_type,
savepng=0,
session=1,
algo=0,
type_cor=0):
p_dir = '/big_disk/ajoshi/HCP100-fMRI-NLM/HCP100-fMRI-NLM'
out_dir = '/big_disk/ajoshi/out_dir'
r_factor = 3
ref_dir = os.path.join(p_dir, 'reference')
ref = '100307'
fn1 = ref + '.reduce' + str(r_factor) + '.LR_mask.mat'
fname1 = os.path.join(ref_dir, fn1)
msk = scipy.io.loadmat(fname1)
dfs_left_sm = readdfs(
os.path.join('/home/ajoshi/for_gaurav',
'100307.BCI2reduce3.very_smooth.' + scan_type + '.dfs'))
dfs_left = readdfs(
os.path.join('/home/ajoshi/for_gaurav',
'100307.BCI2reduce3.very_smooth.' + scan_type + '.dfs'))
data = scipy.io.loadmat(
os.path.join(
p_dir, sub, sub + '.rfMRI_REST' + str(session) + scan +
'.reduce3.ftdata.NLM_11N_hvar_25.mat'))
LR_flag = msk['LR_flag']
# 0= right hemisphere && 1== left hemisphere
if scan_type == 'right':
LR_flag = np.squeeze(LR_flag) == 0
else:
LR_flag = np.squeeze(LR_flag) == 1
data = data['ftdata_NLM']
temp = data[LR_flag, :]
m = np.mean(temp, 1)
temp = temp - m[:, None]
s = np.std(temp, 1) + 1e-16
temp = temp / s[:, None]
msk_small_region = np.in1d(dfs_left.labels, roilist)
d = temp[msk_small_region, :]
rho = np.corrcoef(d)
rho[~np.isfinite(rho)] = 0
d_corr = temp[~msk_small_region, :]
rho_1 = np.corrcoef(d, d_corr)
rho_1 = rho_1[range(d.shape[0]), d.shape[0]:]
rho_1[~np.isfinite(rho_1)] = 0
f_rho = np.arctanh(rho_1)
f_rho[~np.isfinite(f_rho)] = 0
B = np.corrcoef(f_rho)
B[~np.isfinite(B)] = 0
SC = SpectralClustering(n_clusters=nClusters, affinity='precomputed')
affinity_matrix = np.arcsin(rho)
labels_corr_sininv = SC.fit_predict(np.abs(affinity_matrix))
affinity_matrix = sp.exp((-2.0 * (1 - rho)) / (.72**2))
labels_corr_exp = SC.fit_predict(np.abs(affinity_matrix))
affinity_matrix = sp.sqrt(2.0 + 2.0 * rho)
labels_corr_dist = SC.fit_predict(np.abs(affinity_matrix))
B1 = sp.exp((-2.0 * (1.0 - B)) / (0.72**2.0))
labels_corr_corr_exp = SC.fit_predict(B1)
sp.savez(os.path.join(
out_dir, sub + '.rfMRI_REST' + str(session) + scan + str(roilist) +
'.labs.npz'),
labels_corr_sininv=labels_corr_sininv,
labels_corr_corr_exp=labels_corr_corr_exp,
labels_corr_dist=labels_corr_dist,
labels_corr_exp=labels_corr_exp,
msk_small_region=msk_small_region)
return labels_corr_sininv, msk_small_region, dfs_left_sm
"""
@author: ajoshi
"""
from fmri_methods_sipi import interpolate_labels
from dfsio import readdfs, writedfs
import time
import numpy as np
import scipy as sp
import nibabel as nib
from nilearn import image
subbasename = 'Yeo2011_17Networks'
bcibase = '/home/ajoshi/BrainSuite19b/svreg/BCI-DNI_brain_atlas/BCI-DNI_brain'
left_mid = readdfs(bcibase + '.left.mid.cortex.dfs')
right_mid = readdfs(bcibase + '.right.mid.cortex.dfs')
left_inner = readdfs(bcibase + '.left.inner.cortex.dfs')
right_inner = readdfs(bcibase + '.right.inner.cortex.dfs')
left_pial = readdfs(bcibase + '.left.pial.cortex.dfs')
right_pial = readdfs(bcibase + '.right.pial.cortex.dfs')
left_lab = readdfs(subbasename + '.left.mid.cortex.dfs')
right_lab = readdfs(subbasename + '.right.mid.cortex.dfs')
r1_vert = (right_pial.vertices + right_mid.vertices) / 2.0
r2_vert = (right_inner.vertices + right_mid.vertices) / 2.0
l1_vert = (left_pial.vertices + left_mid.vertices) / 2.0
l2_vert = (left_inner.vertices + left_mid.vertices) / 2.0
vol_lab = image.load_img(
'/home/ajoshi/BrainSuite19b/svreg/BCI-DNI_brain_atlas/BCI-DNI_brain.dws.label.nii.gz'
""" from fmri_methods_sipi import interpolate_labels from dfsio import readdfs import numpy as np from nilearn import image as ni import copy BCIbase = '/ImagePTE1/ajoshi/code_farm/svreg/BCI-DNI_brain_atlas' USCBrainbase = '/ImagePTE1/ajoshi/code_farm/hybridatlas/USCBrain_anand_8_29' USCBrainbaseLatest = '/ImagePTE1/ajoshi/code_farm/hybridatlas/USCBrain_9_3_2020' # # %% Change Precentral to match BCI-DNI brain boundaries left_mid = readdfs(USCBrainbase + '/BCI-DNI_brain.left.mid.cortex.dfs') right_mid = readdfs(USCBrainbase + '/BCI-DNI_brain.right.mid.cortex.dfs') left_inner = readdfs(USCBrainbase + '/BCI-DNI_brain.left.inner.cortex.dfs') right_inner = readdfs(USCBrainbase + '/BCI-DNI_brain.right.inner.cortex.dfs') left_pial = readdfs(USCBrainbase + '/BCI-DNI_brain.left.pial.cortex.dfs') right_pial = readdfs(USCBrainbase + '/BCI-DNI_brain.right.pial.cortex.dfs') r1_vert = (right_pial.vertices + right_mid.vertices) / 2.0 r2_vert = (right_inner.vertices + right_mid.vertices) / 2.0 l1_vert = (left_pial.vertices + left_mid.vertices) / 2.0 l2_vert = (left_inner.vertices + left_mid.vertices) / 2.0 vol_lab_new = ni.load_img(USCBrainbase + '/BCI-DNI_brain.label.nii.gz') vol_img_new = vol_lab_new.get_fdata() vol_lab = ni.load_img(BCIbase + '/BCI-DNI_brain.label.nii.gz')
print(cmd1) os.system(cmd1) vol_lab = image.load_img(outvol) vol_lab = image.new_img_like(vol_lab, np.int16(vol_lab.get_fdata())) vol_lab.to_filename(outvol) vol_img = vol_lab.get_fdata() xres = vol_lab.header['pixdim'][1] yres = vol_lab.header['pixdim'][2] zres = vol_lab.header['pixdim'][3] sl = readdfs(lmid) sr = readdfs(rmid) xx = np.arange(vol_lab.shape[0]) * xres yy = np.arange(vol_lab.shape[1]) * yres zz = np.arange(vol_lab.shape[2]) * zres sl.labels = interpn((xx, yy, zz), vol_img, sl.vertices, method='nearest') sr.labels = interpn((xx, yy, zz), vol_img, sr.vertices, method='nearest') sl = smooth_patch(sl, iterations=3000, relaxation=.5) sr = smooth_patch(sr, iterations=3000, relaxation=.5) patch_color_labels(sl) view_patch_vtk(sl) patch_color_labels(sr)
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 16 15:51:16 2016
@author: ajoshi
"""
from dfsio import readdfs
from surfproc import view_patch_vtk
subbasename = '/big_disk/ajoshi/fs_dir/co20050723_090747MPRAGET1Coronals002a001'
hemi = 'left'
s = readdfs(subbasename + '/' + hemi + '.mid.dfs')
view_patch_vtk(s,
outfile=subbasename + '/mri/BST/fs_' + hemi + '1.png',
show=0)
view_patch_vtk(s,
outfile=subbasename + '/mri/BST/fs_' + hemi + '2.png',
azimuth=-90,
roll=90,
show=0)
s = readdfs(subbasename + '/mri/BST/orig.' + hemi + '.mid.cortex.svreg.dfs')
view_patch_vtk(s,
outfile=subbasename + '/mri/BST/bst_' + hemi + '1.png',
show=0)
view_patch_vtk(s,
outfile=subbasename + '/mri/BST/bst_' + hemi + '2.png',
azimuth=-90,
# save 1mm T1
lab1mm = image.resample_img(lab, target_affine=sp.eye(4),
interpolation='nearest', target_shape=vol1.shape)
lab1mm.to_filename('/big_disk/ajoshi/coding_ground/hybridatlas/hBCI_DNI_fsl/\
BCI-DNI_brain-1mm.label.nii.gz')
# save 2mm T1
lab2mm = image.resample_img(lab, target_affine=sp.eye(4)*2,
interpolation='nearest', target_shape=vol2.shape)
lab2mm.to_filename('/big_disk/ajoshi/coding_ground/hybridatlas/hBCI_DNI_fsl/\
BCI-DNI_brain-2mm.label.nii.gz')
# %%
right_mod = readdfs('/big_disk/ajoshi/coding_ground/hybridatlas/BCI-DNI\
_brain_atlas_refined_4_18_2017/BCI-DNI_brain.right.mid.cortex.mod.dfs')
left_mod = readdfs('/big_disk/ajoshi/coding_ground/hybridatlas/BCI-DNI\
_brain_atlas_refined_4_18_2017/BCI-DNI_brain.left.mid.cortex.mod.dfs')
right_mid = readdfs('/big_disk/ajoshi/coding_ground/hybridatlas/BCI-DNI\
_brain_atlas_refined_4_18_2017/BCI-DNI_brain.right.mid.cortex.dfs')
left_mid = readdfs('/big_disk/ajoshi/coding_ground/hybridatlas/BCI-DNI\
_brain_atlas_refined_4_18_2017/BCI-DNI_brain.left.mid.cortex.dfs')
right_inner = readdfs('/big_disk/ajoshi/coding_ground/hybridatlas/BCI-DNI\
_brain_atlas_refined_4_18_2017/BCI-DNI_brain.right.inner.cortex.dfs')
left_inner = readdfs('/big_disk/ajoshi/coding_ground/hybridatlas/BCI-DNI\
_brain_atlas_refined_4_18_2017/BCI-DNI_brain.left.inner.cortex.dfs')
right_pial = readdfs('/big_disk/ajoshi/coding_ground/hybridatlas/BCI-DNI\
_brain_atlas_refined_4_18_2017/BCI-DNI_brain.right.pial.cortex.dfs')
broadmann = ["perirhinal"]
hemi = 'left'
fshemi = 'lh'
for hemi in {'left', 'right'}:
if hemi == 'left':
fshemi = 'lh'
else:
fshemi = 'rh'
outfile = 'BCI-DNI_Perirhinal' + '.' + hemi + '.mid.cortex.dfs'
''' BCI to FS processed BCI '''
bci_bsti = readdfs(
'/home/ajoshi/BrainSuite19b/svreg/BCI-DNI_brain_atlas/BCI-DNI_brain.' +
hemi + '.inner.cortex.dfs')
bci_bst_mid = readdfs(
'/home/ajoshi/BrainSuite19b/svreg/BCI-DNI_brain_atlas/BCI-DNI_brain.' +
hemi + '.mid.cortex.dfs')
bci_bsti.vertices[:, 0] -= 96 * 0.8
bci_bsti.vertices[:, 1] -= 192 * 0.546875
bci_bsti.vertices[:, 2] -= 192 * 0.546875
bci.vertices, bci.faces = fsio.read_geometry(
'/big_disk/ajoshi/data/BCI_DNI_Atlas/surf/' + fshemi + '.white')
bci.labels = np.zeros(bci.vertices.shape[0])
for i in range(len(broadmann)):
labind = fsio.read_label('/big_disk/ajoshi/data/BCI_DNI_Atlas/label/' +
fshemi + '.' + broadmann[i] + '.label')
bci.labels[labind] = i + 1
def readdfsVTK(fname):
s = readdfs(fname)
poly = createPolyData(s.vertices, s.faces)
return poly
def parcellate_region(roilist, sub, nClusters, scan, scan_type, savepng=0, session=1, algo=0, type_cor=0, n_samples=0):
'''algo = 0:Spectral Clustering, 1: region growing '''
p_dir = '/big_disk/ajoshi/HCP100-fMRI-NLM/HCP100-fMRI-NLM'
out_dir = '/big_disk/ajoshi/out_dir'
r_factor = 3
seeds = sp.zeros(nClusters)
ref_dir = os.path.join(p_dir, 'reference')
ref = '100307'
fn1 = ref + '.reduce' + str(r_factor) + '.LR_mask.mat'
fname1 = os.path.join(ref_dir, fn1)
msk = scipy.io.loadmat(fname1)
dfs_left_sm = readdfs(
os.path.join('/home/ajoshi/for_gaurav', '100307.BCI2reduce3.very_smooth.' + scan_type + '.dfs'))
dfs_left = readdfs(os.path.join('/home/ajoshi/for_gaurav', '100307.BCI2reduce3.very_smooth.' + scan_type + '.dfs'))
data = scipy.io.loadmat(os.path.join(p_dir, sub, sub + '.rfMRI_REST' + str(
session) + scan + '.reduce3.ftdata.NLM_11N_hvar_25.mat'))
LR_flag = msk['LR_flag']
# 0= right hemisphere && 1== left hemisphere
if scan_type == 'right':
LR_flag = np.squeeze(LR_flag) == 0
else:
LR_flag = np.squeeze(LR_flag) == 1
data = data['ftdata_NLM']
temp = data[LR_flag, :]
m = np.mean(temp, 1)
temp = temp - m[:, None]
s = np.std(temp, 1) + 1e-16
temp = temp / s[:, None]
msk_small_region = np.in1d(dfs_left.labels, roilist)
if n_samples == 0:
n_samples = temp.shape[1]
ind = random.sample(range(temp.shape[1]), n_samples)
temp = temp[:, ind]
d = temp[msk_small_region, :]
rho = np.corrcoef(d)
rho[~np.isfinite(rho)] = 0
d_corr = temp[~msk_small_region, :]
rho_1 = np.corrcoef(d, d_corr)
rho_1 = rho_1[range(d.shape[0]), d.shape[0]:]
rho_1[~np.isfinite(rho_1)] = 0
f_rho = np.arctanh(rho_1)
f_rho[~np.isfinite(f_rho)] = 0
B = np.corrcoef(f_rho)
B[~np.isfinite(B)] = 0
SC = SpectralClustering(n_clusters=nClusters, affinity='precomputed')
affinity_matrix = np.arcsin(rho)
if algo == 1:
s_a = readdfs('100307.reduce3.very_smooth.' + scan_type +
'.refined.dfs')
conn = sp.eye(dfs_left.vertices.shape[0])
conn[dfs_left.faces[:, 0], dfs_left.faces[:, 1]] = 1
conn[dfs_left.faces[:, 1], dfs_left.faces[:, 2]] = 1
conn[dfs_left.faces[:, 0], dfs_left.faces[:, 2]] = 1
conn = conn + conn.T
conn = conn > 0
conn = conn[msk_small_region, ]
conn = conn[:, msk_small_region]
for ind in range(nClusters):
lind = s_a.labels[msk_small_region] == roilist * 10 + ind + 1
lind = sp.where(lind)[0]
vert = s_a.vertices[msk_small_region, ]
m = sp.mean(vert[lind, ], axis=0)
dist = vert[lind, ] - m
diff = sp.sum(dist**2, axis=1)
indc = sp.argmin(diff)
seeds[ind] = lind[indc]
if algo == 0:
labels_corr_sininv = SC.fit_predict(np.abs(affinity_matrix))
else:
labels_corr_sininv = region_growing_fmri(seeds,
np.abs(affinity_matrix), conn)
affinity_matrix = sp.exp((-2.0*(1-rho))/(.72 ** 2))
if algo == 0:
labels_corr_exp = SC.fit_predict(np.abs(affinity_matrix))
else:
labels_corr_exp = region_growing_fmri(seeds,
np.abs(affinity_matrix), conn)
affinity_matrix = 2.0 - sp.sqrt(2.0 - 2.0*rho)
if algo == 0:
labels_corr_dist = SC.fit_predict(np.abs(affinity_matrix))
else:
labels_corr_dist = region_growing_fmri(seeds,
np.abs(affinity_matrix), conn)
B1 = sp.exp((-2.0*(1.0-B))/(0.72 ** 2.0))
if algo == 0:
labels_corr_corr_exp = SC.fit_predict(B1)
else:
labels_corr_corr_exp = region_growing_fmri(seeds, B1, conn)
return labels_corr_sininv, labels_corr_exp, labels_corr_dist,\
labels_corr_corr_exp, msk_small_region, dfs_left_sm
def main():
# Import the brain map
ss = readdfs("./harmonic_mapping_code/surf.cortex.dfs")
# Generate the u,v map
uv_map = np.loadtxt("./data/uv_brain_map.txt")
# Interpolate the 3d coordinates on the 2d grid
n_pts = 100
xmin = np.min(uv_map[:, 0])
xmax = np.max(uv_map[:, 0])
ymin = np.min(uv_map[:, 1])
ymax = np.max(uv_map[:, 1])
xg, yg = np.meshgrid(np.linspace(xmin, xmax, n_pts),
np.linspace(ymin, ymax, n_pts))
# Interpolating the x, y,z coordiantes
grid_x = griddata(uv_map, ss.vertices[:, 0], (xg, yg), method='linear')
grid_y = griddata(uv_map, ss.vertices[:, 1], (xg, yg), method='linear')
grid_z = griddata(uv_map, ss.vertices[:, 2], (xg, yg), method='linear')
# Compute the differences
diffx_u = np.diff(grid_x, axis=0)
diffx_v = np.diff(grid_x, axis=1)
diffy_u = np.diff(grid_y, axis=0)
diffy_v = np.diff(grid_y, axis=1)
diffz_u = np.diff(grid_z, axis=0)
diffz_v = np.diff(grid_z, axis=1)
# Compute difference in the u and v direction for the 3d coordinates fn
alpha_u = np.stack((diffx_u[:, :-1], diffy_u[:, :-1], diffz_u[:, :-1]),
axis=2)
alpha_v = np.stack((diffx_v[:-1, :], diffy_v[:-1, :], diffz_v[:-1, :]),
axis=2)
# Compute E,F,G for the nodes in the grid
E = np.squeeze(np.sum(np.multiply(alpha_u, alpha_u), axis=2))
F = np.squeeze(np.sum(np.multiply(alpha_u, alpha_v), axis=2))
G = np.squeeze(np.sum(np.multiply(alpha_v, alpha_v), axis=2))
# Visualize E,F and G for the square
plt.figure()
plt.imshow(E)
plt.colorbar()
plt.title("E over the square")
plt.savefig("./images/E_brain.png")
plt.figure()
plt.imshow(F)
plt.colorbar()
plt.title("F over the square")
plt.savefig("./images/F_brain.png")
plt.figure()
plt.imshow(G)
plt.colorbar()
plt.title("G over the square")
plt.savefig("./images/G_brain.png")
plt.show()
return
from dfsio import readdfs
import os.path
from surfproc import view_patch_vtk, patch_color_attrib
import pandas as pd
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
p_dir_ref = '/big_disk/ajoshi/HCP_data/'
hemi = 'left'
ref = '100307'
TR = 2
fmri_run3 = loadmat('/deneb_disk/studyforrest/sub-02-run3\
/fmri_tnlm_0p5_reduce3_v2.mat') # h5py.File(fname1);
dfs_ref = readdfs(
os.path.join(
p_dir_ref, 'reference', ref + '.aparc\
.a2009s.32k_fs.reduce3.smooth.' + hemi + '.dfs'))
segl = 219
fseg1 = normdata(fmri_run3['func_' + hemi][:, 4:segl])
fseg2 = normdata(fmri_run3['func_' + hemi][:, segl:2 * segl - 4])
hemi = 'right'
fseg1r = normdata(fmri_run3['func_' + hemi][:, 4:segl])
fseg2r = normdata(fmri_run3['func_' + hemi][:, segl:2 * segl - 4])
fseg1 = sp.concatenate([fseg1, fseg1r], axis=0)
fseg2 = sp.concatenate([fseg2, fseg2r], axis=0)
annot = pd.read_csv('/deneb_disk/studyforrest/ioats_2s_av_allchar.csv')