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')
face_annot = sp.array(annot['face'])
tst = int(1760.0 / TR)
tend = int(2620.0 / TR)
from surfproc import view_patch_vtk, patch_color_attrib
import pandas as pd
p_dir_ref = '/big_disk/ajoshi/HCP_data/'
hemi = 'left'
ref = '100307'
fmri_sub1 = loadmat('/deneb_disk/studyforrest/sub-01-run1\
/fmri_tnlm_5_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 = 225
sub1seg1 = normdata(fmri_sub1['func_' + hemi][:, :segl])
sub1seg2 = normdata(fmri_sub1['func_' + hemi][:, segl:2 * segl])
v = pd.read_csv('/home/ajoshi/Downloads/emotions_av_1s_thr50.tsv', sep='\t')
annot = []
stseg = []
edseg = []
for ind in range(v.shape[0]):
annot.append(v.values[ind, 2])
stseg.append(v.values[ind, 0])
edseg.append(v.values[ind, 1])
print annot
print sp.array(stseg)
fmri_sub21 = loadmat('/deneb_disk/studyforrest/sub-02-run1\
/fmri_tnlm_5_reduce3_v2.mat') # h5py.File(fname1);
fmri_sub12 = loadmat('/deneb_disk/studyforrest/sub-01-run2\
/fmri_tnlm_5_reduce3_v2.mat') # h5py.File(fname1);
fmri_sub22 = loadmat('/deneb_disk/studyforrest/sub-02-run2\
/fmri_tnlm_5_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'))
sub1seg1 = normdata(fmri_sub11['func_' + hemi][:, :420])
sub1seg2 = normdata(fmri_sub12['func_' + hemi][:, :420])
sub2seg1 = normdata(fmri_sub21['func_' + hemi][:, :420])
sub2seg2 = normdata(fmri_sub22['func_' + hemi][:, :420])
rho_before = sp.sum(sub1seg1 * sub1seg2, axis=1) / sub1seg1.shape[1]
dfs_ref = patch_color_attrib(dfs_ref, rho_before, clim=[0, .7])
view_patch_vtk(dfs_ref,
azimuth=90,
elevation=180,
roll=90,
outfile='before2_seg1to2_1.png')
view_patch_vtk(dfs_ref,
azimuth=-90,
elevation=180,
hemi = 'left'
ref = '100307'
TR = 2
fmri_run3 = loadmat('/deneb_disk/studyforrest/sub-02-run3\
/fmri_tnlm_0p5_reduce3_v2.mat') # h5py.File(fname1);
fmri_run4 = loadmat('/deneb_disk/studyforrest/sub-02-run4\
/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'))
len1 = 100
fseg1 = normdata(fmri_run3['func_' + hemi][:, 4:len1 + 4])
fseg2 = normdata(fmri_run4['func_' + hemi][:, 4:len1 + 4])
#hemi = 'right'
#fseg1r = normdata(fmri_run3['func_'+hemi][:, 4:-4])
#fseg2r = normdata(fmri_run4['func_'+hemi][:, 4:len1+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')
face_annot = sp.array(annot['face'])
tst = int(1760.0 / TR)
tend = int(2620.0 / TR)