def whichSubs(): from getCueSubjects import getsubs subjects,gi = getsubs() print ' '.join(subjects) input_subs = raw_input('subject id(s) (hit enter to process all subs): ') print '\nyou entered: '+input_subs+'\n' if input_subs: subjects=input_subs.split(' ') return subjects
def whichSubs(base_dir='cueexp'): if base_dir == 'cueexp': from getCueSubjects import getsubs subjects, gi = getsubs() elif base_dir == 'cueexp_claudia': from getCueSubjects import getsubs_claudia subjects, gi = getsubs_claudia() print ' '.join(subjects) input_subs = raw_input('subject id(s) (hit enter to process all subs): ') print '\nyou entered: ' + input_subs + '\n' if input_subs: subjects = input_subs.split(' ') return subjects
def whichSubs(base_dir='cueexp'): if base_dir=='cueexp': from getCueSubjects import getsubs subjects,gi = getsubs() elif base_dir=='cueexp_claudia': from getCueSubjects import getsubs_claudia subjects,gi = getsubs_claudia() print ' '.join(subjects) input_subs = raw_input('subject id(s) (hit enter to process all subs): ') print '\nyou entered: '+input_subs+'\n' if input_subs: subjects=input_subs.split(' ') return subjects
########################################################################################## # make out directory if its not already defined if not os.path.exists(out_dir): os.makedirs(out_dir) subjects = raw_input('subject id (enter "all" to process all subs): ') print '\nyou entered: '+subjects+'\n' subjects=subjects.split(' ') if subjects[0]=='all': from getCueSubjects import getsubs subjects,gi = getsubs('cue') for subject in subjects: print '\n********** GLM FITTING FOR SUBJECT '+subject+' **********\n' this_out_str = subject+'_'+out_str # define subject-specific directories subj_dir = os.path.join(data_dir,subject) # subject dir os.chdir(subj_dir) # cd to subj directory cdir = os.getcwd() print '\nCurrent working directory: '+cdir+'\n\n' # NOTE: all input file paths in the 3dDeconvolve command are relative to the subject's directory
# sub_labels provides the labels of the volumes to be extracted from the infiles, and # corresponding t-stats in outfiles will be named according to out_sub_labels. import os,sys,re,glob,numpy as np justPrint = 0 # 1 to just print, 0 to print and execute # set up study-specific directories and file names, etc. if os.path.exists('/Volumes/G-DRIVE/cueexp/data'): data_dir = '/Volumes/G-DRIVE/cueexp/data' else: data_dir = os.path.join(os.path.expanduser('~'),'cueexp','data') from getCueSubjects import getsubs subjsA,_ = getsubs('cue',1) # patients subjsB,_ = getsubs('cue',0) # controls print(subjsA) print(subjsB) #res_dir = os.path.join(data_dir,'results_cue') # directory containing glm stat files res_dir = os.path.join(data_dir,'results_cue_afni_imgperiod') # directory containing glm stat files out_str = '' #out_str = '_n35' # suffix to add to the end of enach out file # file containing covariate data # cv_file = os.path.join(res_dir,'subj_age.txt')
# Infile names should be in the form of: *_in_str, where * is a # specific subject id that will be included in the out file. # sub_labels provides the labels of the volumes to be extracted from the infiles, and # corresponding t-stats in outfiles will be named according to out_sub_labels. import os,sys,re,glob # set up study-specific directories and file names, etc. data_dir = os.path.join(os.path.expanduser('~'),'cueexp','data') from getCueSubjects import getsubs subjsA,_ = getsubs(0) # controls subjsB,_ = getsubs(1) # patients #subjsB.remove('si151120') # data_dir = os.path.join(os.path.expanduser('~'),'cueexp_claudia','data') # from getCueSubjects import getsubs_claudia # subjects,gi = getsubs_claudia() # subjsA = subjects # subjsB=[] print subjsA print subjsB res_dir = os.path.join(data_dir,'results_cueimg_type_ants') # directory containing glm stat files
roi2 = 'Choi_ventralcaudateL' ########################################################################################## # make out directory if its not already defined if not os.path.exists(out_dir): os.makedirs(out_dir) subjects = raw_input('subject id (enter "all" to process all subs): ') print '\nyou entered: ' + subjects + '\n' subjects = subjects.split(' ') if subjects[0] == 'all': from getCueSubjects import getsubs subjects, gi = getsubs('cue') for subject in subjects: print '\n********** GLM FITTING FOR SUBJECT ' + subject + ' **********\n' this_out_str = subject + '_' + out_str # define subject-specific directories subj_dir = os.path.join(data_dir, subject) # subject dir os.chdir(subj_dir) # cd to subj directory cdir = os.getcwd() print '\nCurrent working directory: ' + cdir + '\n\n' # NOTE: all input file paths in the 3dDeconvolve command are relative to the subject's directory #-#-#-#-#-#-#-#-#-#-#- Run 3dDeconvolve: -#-#-#-#-#-#-#-#-#-#-#
########################################################################################## # make out directory if its not already defined if not os.path.exists(out_dir): os.makedirs(out_dir) subjects = raw_input('subject id (enter "all" to process all subs): ') print '\nyou entered: '+subjects+'\n' subjects=subjects.split(' ') if subjects[0]=='all': from getCueSubjects import getsubs subjects,gi = getsubs('mid') for subject in subjects: print '\n********** GLM FITTING FOR SUBJECT '+subject+' **********\n' this_out_str = subject+'_'+out_str # define subject-specific directories subj_dir = os.path.join(data_dir,subject) # subject dir os.chdir(subj_dir) # cd to subj directory cdir = os.getcwd() print '\nCurrent working directory: '+cdir+'\n\n' # NOTE: all input file paths in the 3dDeconvolve command are relative to the subject's directory
#!/usr/bin/python import os, sys ##################### fit glm using 3dDeconvolve ##################################################################### # EDIT AS NEEDED: data_dir = os.path.join(os.path.expanduser('~'), 'cueexp', 'data') from getCueSubjects import getsubs subjects, gi = getsubs() #subjects = ['pk160319','jc160320','jc160321'] # data_dir = os.path.join(os.path.expanduser('~'),'cueexp_claudia','data') # from getCueSubjects import getsubs_claudia # subjects,gi = getsubs_claudia() # pre-processed functional data to analyze func_dir = 'func_proc_cue' # relative to subject-specific directory func_files = 'fpsmtcue1_afni+tlrc' #func_files = 'fpsmtcue1_tlrc.nii' out_dir = os.path.join(data_dir, 'results_cueimg_type_afni') # directory for out files #out_dir = os.path.join(data_dir,'results_cueimg_type_ants') # directory for out files csfFName = 'csf1_afni.1D' wmFName = 'wm1_afni.1D' #csfFName = 'csf1.1D' #wmFName = 'wm1.1D' out_str = 'glm' # string for output files
out_str = 'glm' # string for output files ########################################################################################## # make out directory if its not already defined if not os.path.exists(out_dir): os.makedirs(out_dir) subjects = raw_input('subject id (enter "all" to process all subs): ') print '\nyou entered: ' + subjects + '\n' subjects = subjects.split(' ') if subjects[0] == 'all': from getCueSubjects import getsubs subjects, gi = getsubs('mid') for subject in subjects: print '\n********** GLM FITTING FOR SUBJECT ' + subject + ' **********\n' this_out_str = subject + '_' + out_str # define subject-specific directories subj_dir = os.path.join(data_dir, subject) # subject dir os.chdir(subj_dir) # cd to subj directory cdir = os.getcwd() print '\nCurrent working directory: ' + cdir + '\n\n' # NOTE: all input file paths in the 3dDeconvolve command are relative to the subject's directory #-#-#-#-#-#-#-#-#-#-#- Run 3dDeconvolve: -#-#-#-#-#-#-#-#-#-#-#
#!/usr/bin/python import os,sys ##################### fit glm using 3dDeconvolve ##################################################################### # EDIT AS NEEDED: data_dir = os.path.join(os.path.expanduser('~'),'cueexp','data') from getCueSubjects import getsubs subjects,gi = getsubs() # data_dir = os.path.join(os.path.expanduser('~'),'cueexp_claudia','data') # from getCueSubjects import getsubs_claudia # subjects,gi = getsubs_claudia() # pre-processed functional data to analyze func_dir = 'func_proc_cue' # relative to subject-specific directory func_files = 'fpsmtcue1+tlrc' out_dir = os.path.join(data_dir,'results_pref') # directory for out files out_str = 'glm' # string for output files ##########################################################################################
# Infile names should be in the form of: *_in_str, where * is a # specific subject id that will be included in the out file. # sub_labels provides the labels of the volumes to be extracted from the infiles, and # corresponding t-stats in outfiles will be named according to out_sub_labels. import os, sys, re, glob # set up study-specific directories and file names, etc. data_dir = os.path.join(os.path.expanduser('~'), 'cueexp', 'data') from getCueSubjects import getsubs subjsA, _ = getsubs(0) # controls subjsB, _ = getsubs(1) # patients #subjsB.remove('si151120') # data_dir = os.path.join(os.path.expanduser('~'),'cueexp_claudia','data') # from getCueSubjects import getsubs_claudia # subjects,gi = getsubs_claudia() # subjsA = subjects # subjsB=[] print subjsA print subjsB res_dir = os.path.join( data_dir,
# Infile names should be in the form of: *_in_str, where * is a # specific subject id that will be included in the out file. # sub_labels provides the labels of the volumes to be extracted from the infiles, and # corresponding t-stats in outfiles will be named according to out_sub_labels. import os,sys,re,glob,numpy as np # set up study-specific directories and file names, etc. data_dir = os.path.join(os.path.expanduser('~'),'cueexp','data') from getCueSubjects import getsubs subjsA,_ = getsubs('midi',1) # patients subjsB,_ = getsubs('midi',0) # controls # data_dir = os.path.join(os.path.expanduser('~'),'cueexp_claudia','data') # from getCueSubjects import getsubs_claudia # subjects,gi = getsubs_claudia() # subjsA = subjects # subjsB=[] print subjsA print subjsB #res_dir = os.path.join(data_dir,'results_midi') # directory containing glm stat files res_dir = os.path.join(data_dir,'results_midi_afni') # directory containing glm stat files
# corresponding t-stats in outfiles will be named according to out_sub_labels. import os,sys,re,glob,numpy as np justPrint = 0 # 1 to just print, 0 to print and execute # set up study-specific directories and file names, etc. if os.path.exists('/Volumes/G-DRIVE/cueexp/data'): data_dir = '/Volumes/G-DRIVE/cueexp/data' else: data_dir = os.path.join(os.path.expanduser('~'),'cueexp','data') from getCueSubjects import getsubs #subjsA,_ = getsubs('cue',1) # patients subjsB,_ = getsubs('cue',0) # controls subjsA=['cd171130','ab171208','tb171209','kk180117','rl180205','jc180212','ct180224','rm180316','cm180506','sh180518','rm180525','dl180602','ap180613','jj180618','lh180622','dr180715','md181018','lh181030','td181116','kd181119','zg181207','lm181213','wa181217'] print(subjsA) print(subjsB) #res_dir = os.path.join(data_dir,'results_cue') # directory containing glm stat files res_dir = os.path.join(data_dir,'results_cue_afni') # directory containing glm stat files out_str = '_sample2' #out_str = '_n35' # suffix to add to the end of enach out file doClustSim = 0 # 1 to do clustsim, otherwise 0 (it takes a while)
# sub_labels provides the labels of the volumes to be extracted from the infiles, and # corresponding t-stats in outfiles will be named according to out_sub_labels. import os,sys,re,glob,numpy as np justPrint = 1 # 1 to just print, 0 to print and execute # set up study-specific directories and file names, etc. if os.path.exists('/Volumes/G-DRIVE/cueexp/data'): data_dir = '/Volumes/G-DRIVE/cueexp/data' else: data_dir = os.path.join(os.path.expanduser('~'),'cueexp','data') from getCueSubjects import getsubs subjsA,_ = getsubs('cue',1) # patients subjsB,_ = getsubs('cue',0) # controls ### to do age matched control group: #subjsB.remove('zl150930') #subjsB.remove('ps151001') #subjsB.remove('aa151010') #subjsB.remove('al151016') ## subjsB.remove('jv151030') #subjsB.remove('kl160122') #subjsB.remove('ss160205') #subjsB.remove('bp160213') #subjsB.remove('cs160214') #subjsB.remove('yl160507')