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)