def main(subject_loc, background_subject_locs, graph, meanDfLoc, verbose, brain):
##########################################################
# Freesurfer setting
##########################################################
##########################################################
# Summarize cortical thickness
##########################################################
# if no mean table is given but background list is given
if meanDfLoc == False and background_subject_locs != None:
print "Calculating cortical thickness in {0}".format(background_subject_locs)
# make mean table
meanDfName = raw_input("Name of the background subject : ")
if verbose:
meanDfList = []
for subjectDir in background_subject_locs:
meanDfList.append(collectStats_v2(subjectDir))
meanDf = pd.concat(meanDfList)
else:
meanDf = collectStats(background_subject_locs)
# if no mean table is given, and backround list is empty
# use subject_loc as the background
elif meanDfLoc == False and background_subject_locs == None:
print "No background subjects are given, now running only {0}".format(subject_loc)
meanDfName = ""
if verbose:
meanDf = collectStats_v2([subject_loc])
else:
meanDf = collectStats([subject_loc])
# if meanDfLoc is given
else:
print "Now comparing with mean_thickness.csv in /ccnc_bin/meanThickness/"
meanDfName = "NOR"
# meanDf = pd.read_csv('/ccnc_bin/meanThickness/new_mean_OCT15.csv')
if verbose:
meanDf = pd.read_csv("/ccnc_bin/meanThickness/detailed_mean_2015_12_28.csv", index_col=0)
else:
meanDf = pd.read_csv("/ccnc_bin/meanThickness/new_mean_OCT15.csv", index_col=0)
##########################################################
# Get roi dict : 8 cortex each hemisphere
##########################################################
roiDict = get_cortical_rois()
##########################################################
# annotation2label --> merge labels --> freesurfer/tmp
##########################################################
if graph:
if verbose:
thicknessDf = collectStats_v2([os.path.dirname(freesurfer_dir)]) # background_subject_locs)
draw_thickness_detailed(thicknessDf, meanDf, os.path.basename(subject_loc), meanDfName, subject_loc)
else:
thicknessDf = collectStats([os.path.dirname(freesurfer_dir)]) # background_subject_locs)
draw_thickness(thicknessDf, meanDf, os.path.basename(subject_loc), meanDfName, subject_loc)
if brain:
if verbose:
valueSwap.main(freesurfer_dir, os.path.join(freesurfer_dir, "tmp/thick_kev_detailed.csv"))
else:
print "Please select verbose mode"
pass
print "*" * 80
print "Outputs are saved in /ccnc/mri_team"
print "*" * 80
args = parser.parse_args()
# Main subject
main_freesurferDir = get_freesurferDir(os.path.abspath(args.inputDir))
infoDf = collectStats_v2(main_freesurferDir)
# Freesurfer environment Settings
# os.environ["FREESURFER_HOME"] = '/Applications/freesurfer'
# os.environ["SUBJECTS_DIR"] = '{0}'.format(os.path.dirname(main_freesurferDir))
# Mean Df
if args.createMeanFrom:
freesurferList = subjDirs_to_fsDirs(args.createMeanFrom)
concatDf = concatFsDf(freesurferList)
meanDf = concatDf_to_meanDf(concatDf)
if args.saveMeanDf:
meanDf.to_csv(args.saveMeanDf)
elif args.gender == "M":
meanDf = pd.read_csv("/ccnc_bin/meanThickness/male_df.csv", index_col=0)
elif args.gender == "F":
meanDf = pd.read_csv("/ccnc_bin/meanThickness/female_df.csv", index_col=0)
else:
meanDf = pd.read_csv("/ccnc_bin/meanThickness/detailed_mean_2015_12_28.csv", index_col=0)
subjectInitials = raw_input("Subject initial :")
# Graph
draw_thickness_detailed(infoDf, meanDf, subjectInitials, "CCNC_mean")
valueSwap.main(main_freesurfer_dir, os.path.join(main_freesurfer_dir, "tmp/thick_kev_detailed.csv"))
def main(subject_loc, background_subject_locs, graph, meanDfLoc, verbose,
brain):
##########################################################
# Freesurfer setting
##########################################################
##########################################################
# Summarize cortical thickness
##########################################################
# if no mean table is given but background list is given
if meanDfLoc == False and background_subject_locs != None:
print 'Calculating cortical thickness in {0}'.format(
background_subject_locs)
# make mean table
meanDfName = raw_input('Name of the background subject : ')
if verbose:
meanDfList = []
for subjectDir in background_subject_locs:
meanDfList.append(collectStats_v2(subjectDir))
meanDf = pd.concat(meanDfList)
else:
meanDf = collectStats(background_subject_locs)
# if no mean table is given, and backround list is empty
# use subject_loc as the background
elif meanDfLoc == False and background_subject_locs == None:
print 'No background subjects are given, now running only {0}'.format(
subject_loc)
meanDfName = ''
if verbose:
meanDf = collectStats_v2([subject_loc])
else:
meanDf = collectStats([subject_loc])
# if meanDfLoc is given
else:
print 'Now comparing with mean_thickness.csv in /ccnc_bin/meanThickness/'
meanDfName = 'NOR'
#meanDf = pd.read_csv('/ccnc_bin/meanThickness/new_mean_OCT15.csv')
if verbose:
meanDf = pd.read_csv(
'/ccnc_bin/meanThickness/detailed_mean_2015_12_28.csv',
index_col=0)
else:
meanDf = pd.read_csv('/ccnc_bin/meanThickness/new_mean_OCT15.csv',
index_col=0)
##########################################################
# Get roi dict : 8 cortex each hemisphere
##########################################################
roiDict = get_cortical_rois()
##########################################################
# annotation2label --> merge labels --> freesurfer/tmp
##########################################################
if graph:
if verbose:
thicknessDf = collectStats_v2([os.path.dirname(freesurfer_dir)
]) #background_subject_locs)
draw_thickness_detailed(thicknessDf, meanDf,
os.path.basename(subject_loc), meanDfName,
subject_loc)
else:
thicknessDf = collectStats([os.path.dirname(freesurfer_dir)
]) #background_subject_locs)
draw_thickness(thicknessDf, meanDf, os.path.basename(subject_loc),
meanDfName, subject_loc)
if brain:
if verbose:
valueSwap.main(
freesurfer_dir,
os.path.join(freesurfer_dir, 'tmp/thick_kev_detailed.csv'))
else:
print 'Please select verbose mode'
pass
print "*" * 80
print 'Outputs are saved in /ccnc/mri_team'
print "*" * 80
def main(subject_loc, backgrounds, roi_list, meanDfLoc,verbose, brain):
##########################################################
# Find freesurfer dir
##########################################################
FS_description= ['bem','mri','scripts',
'src','stats','surf','tmp']
freesurfer_dir = ccncpy.subDirSearch(FS_description,
subject_loc)
if len(freesurfer_dir) > 1:
print freesurfer_dir
sys.exit(re.sub('\s+',' ',
'There are more than 1 freesurfer directory \
under {0}'.format(subject_loc)))
else:
freesurfer_dir = ''.join(freesurfer_dir)
##########################################################
# Freesurfer setting
##########################################################
os.environ["FREESURFER_HOME"] = '/Applications/freesurfer'
# where is the freesurfer directory
os.environ["SUBJECTS_DIR"] = '{0}'.format(os.path.dirname(freesurfer_dir))
##########################################################
# Summarize cortical thickness
##########################################################
# if no mean table is given but background list is given
if meanDfLoc == False and backgrounds != None:
print 'Calculating cortical thickness in {0}'.format(backgrounds)
# make mean table
meanDfName = raw_input('Name of the background subject : ')
if verbose:
meanDf = collectStats_v2(backgrounds)
else:
meanDf = collectStats(backgrounds)
# if no mean table is given, and backround list is empty
# use subject_loc as the background
elif meanDfLoc == False and backgrounds == None:
print 'No background subjects are given, now running only {0}'.format(subject_loc)
meanDfName = ''
if verbose:
meanDf = collectStats_v2([subject_loc])
else:
meanDf = collectStats([subject_loc])
# if meanDfLoc is given
else:
print 'Now comparing with mean_thickness.csv in /ccnc_bin/meanThickness/'
meanDfName = 'NOR'
#meanDf = pd.read_csv('/ccnc_bin/meanThickness/new_mean_OCT15.csv')
if verbose:
meanDf = pd.read_csv('/ccnc_bin/meanThickness/detailed_mean_2015_12_28.csv', index_col=0)
else:
meanDf = pd.read_csv('/ccnc_bin/meanThickness/new_mean_OCT15.csv', index_col=0)
##########################################################
# Get roi dict : 8 cortex each hemisphere
##########################################################
roiDict = get_cortical_rois()
##########################################################
# annotation2label --> merge labels --> freesurfer/tmp
##########################################################
if args.graph:
if verbose:
thicknessDf = collectStats_v2([os.path.dirname(freesurfer_dir)])#backgrounds)
draw_thickness_detailed(thicknessDf,meanDf,os.path.basename(subject_loc), meanDfName, subject_loc)
else:
thicknessDf = collectStats([os.path.dirname(freesurfer_dir)])#backgrounds)
draw_thickness(thicknessDf,meanDf,os.path.basename(subject_loc), meanDfName, subject_loc)
if brain:
if verbose:
valueSwap.main(freesurfer_dir,
os.path.join(freesurfer_dir,
'tmp/thick_kev_detailed.csv'))
else:
print 'Please select verbose mode'
pass
print "*"*80
print 'Outputs are saved in /ccnc/mri_team'
print "*"*80
# Freesurfer environment Settings
# os.environ["FREESURFER_HOME"] = '/Applications/freesurfer'
# os.environ["SUBJECTS_DIR"] = '{0}'.format(os.path.dirname(main_freesurferDir))
# Mean Df
if args.createMeanFrom:
freesurferList = subjDirs_to_fsDirs(args.createMeanFrom)
concatDf = concatFsDf(freesurferList)
meanDf = concatDf_to_meanDf(concatDf)
if args.saveMeanDf:
meanDf.to_csv(args.saveMeanDf)
elif args.gender == 'M':
meanDf = pd.read_csv('/ccnc_bin/meanThickness/male_df.csv',
index_col=0)
elif args.gender == 'F':
meanDf = pd.read_csv('/ccnc_bin/meanThickness/female_df.csv',
index_col=0)
else:
meanDf = pd.read_csv(
'/ccnc_bin/meanThickness/detailed_mean_2015_12_28.csv',
index_col=0)
subjectInitials = raw_input('Subject initial :')
# Graph
draw_thickness_detailed(infoDf, meanDf, subjectInitials, 'CCNC_mean')
valueSwap.main(
main_freesurfer_dir,
os.path.join(main_freesurfer_dir, 'tmp/thick_kev_detailed.csv'))
