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():
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')
