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freesurfer_img.py
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freesurfer_img.py
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from __future__ import division
#!/ccnc_bin/venv/bin/python
__author__ = 'kcho'
import re
import os
import sys
import pandas as pd
#import matplotlib
#matplotlib.use('GTK')
#import matplotlib.pyplot as plt
import argparse
import textwrap
import ccncpy.ccncpy as ccncpy
#from mpltools import style
#from mpltools import layout
#style.use('ggplot')
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)
makeBrainPic(freesurfer_dir, thicknessDf, meanDf)
else:
thicknessDf = collectStats([os.path.dirname(freesurfer_dir)])#backgrounds)
makeBrainPic(freesurfer_dir, thicknessDf, meanDf)
#volumeDf = openStatsTable(freesurfer_dir)
#volumeDf['name'] = volumeDf.side + '_' + volumeDf.ROI
#volumeDf = getSummary(volumeDf,roiDict)
#print volumeDf
# graph
#draw_graph(volumeDf)
#freesurfer_table = openTable('/Applications/freesurfer/FreeSurferColorLUT.txt')
#print freesurfer_table
#for roi in roi_list:
# print freesurfer_table[roi]
def makeBrainPic(freesurfer_dir, thicknessDf, meanDf):
from nilearn import datasets
from nilearn import plotting
import nipype.interfaces.freesurfer as fs # fsl
bgBrain = os.path.join(freesurfer_dir,'mri/brain.mgz')
bgBrainNii = os.path.dirname(bgBrain) + 'brain.nii.gz'
#converter = fs.MRIConvert(backgroundin_file = bgBrain,
#out_type='niigz',
#out_file=bgBrainNii)
#converter.run()
thicknessDf['roi'] = thicknessDf.subroi.str[3:]
thicknessDf['side'] = thicknessDf.subroi.str[:2]
thicknessDf['region'] = thicknessDf.roi.apply(getRegion)
print thicknessDf.head()
meanDf['roi'] = meanDf.subroi.str[3:]
meanDf['side'] = meanDf.subroi.str[:2]
meanDf = meanDf.groupby(['roi','side']).mean().reset_index()
meanDf['region'] = meanDf.roi.apply(getRegion)
print meanDf
gb = thicknessDf.groupby('region')
thicknessDf = pd.concat([gb.get_group('LPFC'),
gb.get_group('OFC'),
gb.get_group('MPFC'),
gb.get_group('LTC'),
gb.get_group('MTC'),
gb.get_group('SMC'),
gb.get_group('PC'),
gb.get_group('OCC')])
gbmean = meanDf.groupby('region')
meanDf = pd.concat([gbmean.get_group('LPFC'),
gbmean.get_group('OFC'),
gbmean.get_group('MPFC'),
gbmean.get_group('LTC'),
gbmean.get_group('MTC'),
gbmean.get_group('SMC'),
gbmean.get_group('PC'),
gbmean.get_group('OCC')])
gb = thicknessDf.groupby('side')
label = thicknessDf.subroi.str[3:].unique()
## annotation
mergedDf = pd.merge(meanDf.groupby('side').get_group('lh'),
gb.get_group('lh'),
on=['roi','side','region'],
how='inner')
mergedDf['mean_sub_indv'] = mergedDf.thickness_x - mergedDf.thickness_y
for side in ['lh','rh']:
sideMerged = mergedDf.groupby('side').get_group(side)
for row in sideMerged.iterrows():
print row
#shots = fs.SurfaceSnapshots(subject_id=os.path.basename(freesurfer_dir),
#hemi=side,
#surface="pial")
#shots.inputs.overlay
def draw_thickness_detailed(thicknessDf, meanDf, subjName, meanDfName,subject_loc):
roiOrder = ['LPFC', 'OFC', 'MPFC', 'LTC', 'MTC', 'SMC', 'PC', 'OCC']
def getRegion(roi):
roiDict = get_cortical_rois()
for region, roiList in roiDict.iteritems():
if roi in roiList:
return region
thicknessDf['roi'] = thicknessDf.subroi.str[3:]
thicknessDf['side'] = thicknessDf.subroi.str[:2]
thicknessDf['region'] = thicknessDf.roi.apply(getRegion)
print thicknessDf.head()
meanDf['roi'] = meanDf.subroi.str[3:]
meanDf['side'] = meanDf.subroi.str[:2]
meanDf = meanDf.groupby(['roi','side']).mean().reset_index()
meanDf['region'] = meanDf.roi.apply(getRegion)
print meanDf
gb = thicknessDf.groupby('region')
thicknessDf = pd.concat([gb.get_group('LPFC'),
gb.get_group('OFC'),
gb.get_group('MPFC'),
gb.get_group('LTC'),
gb.get_group('MTC'),
gb.get_group('SMC'),
gb.get_group('PC'),
gb.get_group('OCC')])
gbmean = meanDf.groupby('region')
meanDf = pd.concat([gbmean.get_group('LPFC'),
gbmean.get_group('OFC'),
gbmean.get_group('MPFC'),
gbmean.get_group('LTC'),
gbmean.get_group('MTC'),
gbmean.get_group('SMC'),
gbmean.get_group('PC'),
gbmean.get_group('OCC')])
gb = thicknessDf.groupby('side')
label = thicknessDf.subroi.str[3:].unique()
fig = plt.figure(figsize=(22,12))
fig.suptitle("Cortical thickness in all regions", fontsize=20)
#fig.set_ylabel('Cortical thickness in mm', fontsize=16)
#plt.ylabel('Cortical thickness', fontsize=16)
#plt.xticks(range(len(label)), label)
lh_g = plt.subplot2grid((2,2),(0, 0), colspan=2)
rh_g = plt.subplot2grid((2,2),(1, 0), colspan=2)
#ax1 = fig.add_subplot(211)
#ax2 = fig.add_subplot(212)
lh_g.plot(gb.get_group('lh')['thickness'],'r',label=subjName)
lh_g.plot(meanDf.groupby('side').get_group('lh')['thickness'],'r--',label=meanDfName)
# error bar
eb1 = lh_g.errorbar(range(len(meanDf.roi.unique())),
meanDf.groupby('side').get_group('lh')['thickness'],
meanDf.groupby('side').get_group('lh')['std'],
linestyle='None',
marker='^')
eb1[-1][0].set_linestyle('--')
lh_g.set_xlabel('Left', fontsize=16)
#lh_g.set_ylabel('Cortical thickness in mm', fontsize=16)
lh_g.set_ylim(1.0, 4.7)
lh_g.set_xticks(range(len(label)))
lh_g.set_xticklabels(['' for x in label])
lh_g.set_xlim(-.5, 32.5)
lh_g.legend()
legend = lh_g.legend(frameon = 1)
frame = legend.get_frame()
frame.set_facecolor('white')
######fill
roiDict = get_cortical_rois()
startNum = 0
switch = 0
x_starting_point = 0
for region in roiOrder:
if switch == 0:
switch = 1
pass
x_starting_point = startNum
startNum = startNum + len(roiDict[region])
lh_g.text((x_starting_point-.5 + startNum-.5)/2, 1.2,
region,
horizontalalignment='center',
alpha=.4,
fontsize=15)
else:
alpha = 0.2
col='green'
p = lh_g.axvspan(x_starting_point-.5, startNum-.5, facecolor=col, alpha=alpha)
x_starting_point = startNum
startNum = startNum + len(roiDict[region])
switch = 0
lh_g.text((x_starting_point-.5 + startNum-.5)/2, 1.2,
region,
horizontalalignment='center',
alpha=.4,
fontsize=15)
## annotation
mergedDf = pd.merge(meanDf.groupby('side').get_group('lh'),
gb.get_group('lh'),
on=['roi','side','region'],
how='inner')
mergedDf['mean_sub_indv'] = mergedDf.thickness_x - mergedDf.thickness_y
print mergedDf[abs(mergedDf['mean_sub_indv']) > 0.5]
for row in mergedDf[abs(mergedDf['mean_sub_indv']) > 0.5].iterrows():
print row
#lh_g.text(row[0], row[1].thickness_y - row[1].mean_sub_indv/2,
#row[1].roi,
#horizontalalignment='center',
#fontsize=20)
lh_g.annotate(row[1].roi,
xy=(row[0], row[1].thickness_y),
xytext=(row[0], 1.5-row[1].mean_sub_indv/3),
arrowprops=dict(facecolor='green', shrink=0.05),
horizontalalignment='left',
fontsize=20)
rh_g.plot(gb.get_group('rh')['thickness'],'b',label=subjName)
rh_g.plot(meanDf.groupby('side').get_group('rh')['thickness'],'b--',label=meanDfName)
#error bar
eb2 = rh_g.errorbar(range(len(meanDf.roi.unique())),
meanDf.groupby('side').get_group('rh')['thickness'],
meanDf.groupby('side').get_group('rh')['std'],
linestyle='None',
marker='^',
color='b')
eb2[-1][0].set_linestyle('--')
#label = ['LPFC' 'OFC' 'MPFC' 'LTC' 'MTC' 'SMC' 'PC' 'OCC','b']
xticksNum = range(8)
#rh_g.set_xticklabels(label)
rh_g.set_xlabel('Right', fontsize=16)
rh_g.set_ylim(1, 4.7)
rh_g.set_xlim(-.5, 32.5)
rh_g.set_xticks(range(len(label)))
rh_g.set_xticklabels(label)
rh_g.legend()
legend = rh_g.legend(frameon = 1)
frame = legend.get_frame()
frame.set_facecolor('white')
######fill
startNum = 0
switch = 0
x_starting_point = 0
for region in roiOrder:
if switch == 0:
switch = 1
pass
x_starting_point = startNum
startNum = startNum + len(roiDict[region])
rh_g.text((x_starting_point-.5 + startNum-.5)/2, 1.2,
region,
horizontalalignment='center',
alpha=.4,
fontsize=15)
else:
alpha = 0.2
col='green'
p = rh_g.axvspan(x_starting_point-.5, startNum-.5, facecolor=col, alpha=alpha)
x_starting_point = startNum
startNum = startNum + len(roiDict[region])
switch = 0
rh_g.text((x_starting_point-.5 + startNum-.5)/2, 1.2,
region,
horizontalalignment='center',
alpha=.4,
fontsize=15)
## annotation
mergedDf = pd.merge(meanDf.groupby('side').get_group('rh'),
gb.get_group('rh'),
on=['roi','side','region'],
how='inner')
mergedDf['mean_sub_indv'] = mergedDf.thickness_x - mergedDf.thickness_y
print mergedDf[abs(mergedDf['mean_sub_indv']) > 0.5]
for row in mergedDf[abs(mergedDf['mean_sub_indv']) > 0.5].iterrows():
print row
#rh_g.text(row[0], row[1].thickness_y - row[1].mean_sub_indv/2,
#row[1].roi,
#horizontalalignment='center',
#fontsize=20)
rh_g.annotate(row[1].roi,
xy=(row[0], row[1].thickness_y),
xytext=(row[0], 1.5-row[1].mean_sub_indv/3),
arrowprops=dict(facecolor='green', shrink=0.05),
horizontalalignment='left',
fontsize=20)
#plt.tight_layout()
plt.tight_layout(pad=7, w_pad=3, h_pad=0.2)
#legend = plt.legend(frameon = 1)
#frame = legend.get_frame()
##frame.set_color('white')
#frame.set_facecolor('white')
##frame.set_edgecolor('red')
labels = rh_g.get_xticklabels()
plt.setp(labels, rotation=30)
labels = lh_g.get_xticklabels()
plt.setp(labels, rotation=30)
#plt.show()
fig.savefig('/ccnc/mri_team/'+subject_loc)
def draw_thickness(thicknessDf,meanDf, subjName, meanDfName, subject_loc):
thicknessDf['roi'] = thicknessDf.subroi.str[3:]
thicknessDf['side'] = thicknessDf.subroi.str[:2]
meanDf['roi'] = meanDf.subroi.str[3:]
meanDf['side'] = meanDf.subroi.str[:2]
gb = thicknessDf.groupby('roi')
thicknessDf = pd.concat([gb.get_group('LPFC'),
gb.get_group('OFC'),
gb.get_group('MPFC'),
gb.get_group('LTC'),
gb.get_group('MTC'),
gb.get_group('SMC'),
gb.get_group('PC'),
gb.get_group('OCC')])
gbmean = meanDf.groupby('roi')
meanDf = pd.concat([gbmean.get_group('LPFC'),
gbmean.get_group('OFC'),
gbmean.get_group('MPFC'),
gbmean.get_group('LTC'),
gbmean.get_group('MTC'),
gbmean.get_group('SMC'),
gbmean.get_group('PC'),
gbmean.get_group('OCC')])
gb = thicknessDf.groupby('side')
label = thicknessDf.subroi.str[3:].unique()
fig = plt.figure(figsize=(12,8))
fig.suptitle("Cortical thickness in eight regions", fontsize=20)
#plt.ylabel('Cortical thickness', fontsize=16)
#plt.xticks(range(len(label)), label)
lh_g = plt.subplot2grid((2,2),(0, 0), rowspan=2)
rh_g = plt.subplot2grid((2,2),(0, 1), rowspan=2)
#ax1 = fig.add_subplot(211)
#ax2 = fig.add_subplot(212)
lh_g.plot(gb.get_group('lh')['thickness'],'r',label=subjName)
lh_g.plot(meanDf.groupby('side').get_group('lh')['thickness'],'r--',label=meanDfName)
# error bar
print meanDf.groupby('side').get_group('lh')['std']
eb1 = lh_g.errorbar(range(len(meanDf.roi.unique())),
meanDf.groupby('side').get_group('lh')['thickness'],
meanDf.groupby('side').get_group('lh')['std'],
linestyle='None',
marker='^')
eb1[-1][0].set_linestyle('--')
lh_g.set_xlabel('Left', fontsize=16)
lh_g.set_ylabel('Cortical thickness in mm', fontsize=16)
lh_g.set_ylim(1.0, 4)
lh_g.set_xticks(range(8))
lh_g.set_xticklabels(label)
lh_g.set_xlim(-.5, 7.5)
lh_g.legend()
legend = lh_g.legend(frameon = 1)
frame = legend.get_frame()
frame.set_facecolor('white')
rh_g.plot(gb.get_group('rh')['thickness'],'b',label=subjName)
rh_g.plot(meanDf.groupby('side').get_group('rh')['thickness'],'b--',label=meanDfName)
# error bar
eb2 = rh_g.errorbar(range(len(meanDf.roi.unique())),
meanDf.groupby('side').get_group('rh')['thickness'],
meanDf.groupby('side').get_group('rh')['std'],
linestyle='None',
marker='^',
color='b')
eb2[-1][0].set_linestyle('--')
#label = ['LPFC' 'OFC' 'MPFC' 'LTC' 'MTC' 'SMC' 'PC' 'OCC','b']
xticksNum = range(8)
#rh_g.set_xticklabels(label)
print label
rh_g.set_xlabel('Right', fontsize=16)
rh_g.set_ylim(1, 4)
rh_g.set_xlim(-.5, 7.5)
rh_g.set_xticks(range(8))
rh_g.set_xticklabels(label)
rh_g.legend()
legend = rh_g.legend(frameon = 1)
frame = legend.get_frame()
frame.set_facecolor('white')
#plt.tight_layout()
#plt.tight_layout(pad=2, w_pad=2, h_pad=20)
#legend = plt.legend(frameon = 1)
#frame = legend.get_frame()
##frame.set_color('white')
#frame.set_facecolor('white')
##frame.set_edgecolor('red')
plt.show()
plt.savefig('/ccnc/mri_team/'+subject_loc)
def dictWithTuple2df(thicknessDict):
print thicknessDict
df = pd.DataFrame.from_dict(thicknessDict)
df = df.stack().reset_index()
df = df[df.level_0==0].merge(df[df.level_0==1], on='level_1', how='inner')
df.columns = ['__','subroi','thickness','_','std']
df['side'] = df['subroi'].str[:2]
return df[['subroi','thickness','side','std']]
def getThickness(freesurfer_dir,roiDict):
thicknessDict={}
for side in ['lh','rh']:
for cortex, rois in roiDict.iteritems():
if len(rois) > 1:
command = 'mris_anatomical_stats \
-l {loc}/{side}_{cortex} FREESURFER {side} 2>/dev/null'.format(
loc=os.path.join(freesurfer_dir,'tmp'),
side=side,
cortex=cortex
)
else:
command = 'mris_anatomical_stats \
-l {loc}/{side}.{cortex}.label FREESURFER {side} 2>/dev/null'.format(
loc=os.path.join(freesurfer_dir,'tmp'),
side=side,
cortex=cortex
)
#print command
output=os.popen(re.sub('\s+',' ',command)).read()
#print cortex, rois, '****'*10
#print output
thickness = re.search('thickness\s+=\s+(\S+)\s+mm\s+\S+\s+(\S+)', output).group(1,2)
thickness = tuple([float(x) for x in thickness])
thicknessDict[side+'_'+cortex] = thickness
print cortex, thickness
try:
os.remove('{loc}/{side}_{cortex}'.format(
loc=os.path.join(freesurfer_dir,'tmp'),
side=side,
cortex=cortex
))
except:
pass
return thicknessDict
def mergeLabel(freesurfer_dir, roiDict):
for side in ['lh','rh']:
for cortex, rois in roiDict.iteritems():
command = 'mri_mergelabels {inLabel} -o {outLabel} 2>/dev/null'.format(
inLabel = ' '.join(['-i '+os.path.join(freesurfer_dir,'tmp',side+'.'+x+'.label') for x in rois]),
outLabel = os.path.join(freesurfer_dir,'tmp',side+'_'+cortex))
os.popen(command).read()
#for roi in [os.path.join(freesurfer_dir,'tmp',side+'.'+x+'.label') for x in rois]:
#print roi
#try:
#os.remove(roi)
#except:
#pass
def makeLabel(freesurfer_dir):
'''
Run mri_annotation2label for lh and rh hemisphere.
Creates labels in $freesurfer_dir/tmp
'''
for side in ['lh','rh']:
command = 'mri_annotation2label \
--subject {basename} \
--hemi {side} --outdir {outDir} 2>/dev/null'.format(basename=os.path.basename(freesurfer_dir),
side=side,
outDir=os.path.join(freesurfer_dir,'tmp'))
os.popen(re.sub('\s+',' ',command)).read()
def collectStats_v2(backgrounds):
'''
CollectStats version 2
Summarise cortical thickness in more than one subjects.
'backgrounds' should given in python list format.
For each background,
1. Creates labels using makeLabel
2. Merges labels according to the roiDict using mergeLabel
3. Estimates cortical thickness in each merged labels (dict)
4. Converts dict to pandas Dataframe using dictWithTuple2df
5. save df to freesurfer_dir/tmp/thick_kev_detailed.csv
Than mean df is returned.
'''
#collectStats('ha','ho',ha')
# cortical regions as a dictionary
roiDict = get_cortical_rois_detailed()
subjectDict = {}
for background in backgrounds:
# freesurfer sub-directory description
FS_description= ['bem','mri','scripts','src','stats','surf','tmp']
freesurfer_dir = ccncpy.subDirSearch(FS_description, background)
print freesurfer_dir
if len(freesurfer_dir) > 1:
sys.exit(re.sub('\s+',' ',
'There are more than 1 freesurfer directory \
under {0}'.format(subject_loc)))
else:
freesurfer_dir = ''.join(freesurfer_dir)
if not os.path.isfile(os.path.join(freesurfer_dir,'tmp','thick_kev_detailed.csv')):
makeLabel(freesurfer_dir)
mergeLabel(freesurfer_dir, roiDict)
thicknessDict = getThickness(freesurfer_dir, roiDict)
thicknessDf = dictWithTuple2df(thicknessDict)
thicknessDf.to_csv(os.path.join(freesurfer_dir,'tmp','thick_kev_detailed.csv'))
else:
thicknessDf = pd.read_csv(os.path.join(freesurfer_dir,'tmp','thick_kev_detailed.csv'))
subjectDict[background] = thicknessDf
# sum up dataframes in a subjectDict dictionary
finalDf = pd.concat([x for x in subjectDict.values()])
# return mean
return finalDf.groupby('subroi').mean().reset_index()
def collectStats(backgrounds):
'''
Summarise cortical thickness in more than one subjects.
'backgrounds' should given in python list format.
For each background,
1. Creates labels using makeLabel
2. Merges labels according to the roiDcit using mergeLabel
3. Estimates cortical thickness in each merged labels (dict)
4. Converts dict to pandas Dataframe using dictWithTuple2df
5. save df to freesurfer_dir/tmp/thick_kev.csv
Than mean df is returned.
'''
#collectStats('ha','ho',ha')
# 8 cortex dictionary
roiDict = get_cortical_rois()
subjectDict = {}
for background in backgrounds:
# freesurfer sub-directory description
FS_description= ['bem','mri','scripts','src','stats','surf','tmp']
freesurfer_dir = ccncpy.subDirSearch(FS_description, background)
print freesurfer_dir
if len(freesurfer_dir) > 1:
sys.exit(re.sub('\s+',' ',
'There are more than 1 freesurfer directory \
under {0}'.format(subject_loc)))
else:
freesurfer_dir = ''.join(freesurfer_dir)
if not os.path.isfile(os.path.join(freesurfer_dir,'tmp','thick_kev.csv')):
makeLabel(freesurfer_dir)
mergeLabel(freesurfer_dir, roiDict)
thicknessDict = getThickness(freesurfer_dir, roiDict)
thicknessDf = dictWithTuple2df(thicknessDict)
thicknessDf.to_csv(os.path.join(freesurfer_dir,'tmp','thick_kev.csv'))
else:
thicknessDf = pd.read_csv(os.path.join(freesurfer_dir,'tmp','thick_kev.csv'))
subjectDict[background] = thicknessDf
# sum up dataframes in a subjectDict dictionary
finalDf = pd.concat([x for x in subjectDict.values()])
# return mean
return finalDf.groupby('subroi').mean().reset_index()
def draw_graph(volumeDf):
gb = volumeDf.groupby(['side','cortex'])
lh_volume_sums={}
rh_volume_sums={}
cortexList = []
for (side,cortex), grp in gb:
print cortex
if side == 'lh':
lh_volume_sums[side+'_'+cortex] = grp['Volume'].sum()
cortexList.append(cortex)
else:
rh_volume_sums[side+'_'+cortex] = grp['Volume'].sum()
print rh_volume_sums
plt.plot(lh_volume_sums.values(),'r')
plt.plot(rh_volume_sums.values(),'b')
plt.xticks(range(len(cortexList)), cortexList)
plt.show()
# plt.plot(gb.get_group('lh')['Volume'],'r-')
# plt.plot(gb.get_group('rh')['Volume'],'b-')
# plt.xticks(range(len(gb.get_group('lh')['ROI'])),gb.get_group('lh')['ROI'])
# plt.show()
def getSummary(volumeDf,roiDict):
# thalamus : lh, 10, rh, 49
# lh_OFC : 1019 1014 1012
columnMake = pd.DataFrame.from_dict(roiDict,orient='index').T.stack().reset_index()
columnMake.columns = ['order','cortex','subroi']
volumeDf = pd.merge(volumeDf,
columnMake[['cortex','subroi']],
left_on='ROI',
right_on='subroi',
how='inner').drop('subroi',axis=1)
return volumeDf
# ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
# ts = ts.cumsum()
# plt.figure(); ts.plot();plt.show()
def openStatsTable(freesurfer_dir):
statsROI = os.path.join(freesurfer_dir,'stats')
filesToRead = 'aparc.stats'
dfList = []
for side in ['lh','rh']:
statsFile = os.path.join(statsROI,side+'.'+filesToRead)
with open(statsFile,'r') as f:
lines = f.readlines()
linesEdited = [re.search('(\w+)\s+(\d+)\s+(\d+)\s+(\d+)',x).group(1,4) for x in lines if re.search('^\w',x)]
df = pd.DataFrame(linesEdited)
df.columns = ['ROI','Volume']
df['Volume'] = df['Volume'].astype(int)
df['side']=side
dfList.append(df)
return pd.concat(dfList)
def openTable(f_file):
with open(f_file,'r') as f:
lines = f.readlines()
lines_edited = [re.search('^(\d+)\s+(\S+)',x).group(2,1) for x in lines if re.search('^\d',x)]
lines_edited_dict = dict(lines_edited)
return lines_edited_dict
def roi_extraction(subjectDir, roiName, roiNumber=False, outputDir=False):
'''
Extracts ROI from freesurfer output
roiName : string
roiNumber : list of number
'''
# FREESURFER vs freesurfer
inputName = '{subjectDir}/{freesurferDir}/mri/aparc+aseg.mgz'.format(
subjectDir=subjectDir,
freesurferDir=freesurferDIR)
# If the output directory is specified
if outputDir:
outputName = '{outputDir}/{roiName}.nii.gz'.format(
outputDir = outputDir,
roiName = roiName)
else:
outputName = '{subjectDir}/ROI/{roiName}.nii.gz'.format(
subjectDir=subjectDir,
roiName=roiName)
# If the number of the ROI is secificed
if roiNumber:
command = 'mri_binarize --i {inputName} \
--match {roiNumber} \
--o {outputName}'.format(
inputName=inputName,
roiNumber=roiNumber,
outputName=outputName)
else:
command = 'mri_binarize --i {inputName} \
--match {roiNumber} \
--o {outputName}'.format(
inputName=inputName,
roiNumber=roiNumber,
outputName=outputName)
command = re.sub('\s+',' ',command)
output = os.popen(command).read()
print output
def get_cortical_rois():
'''
returns 8 cortical divisions in dictionary
'''
roiDict = {'OFC' : ['parsorbitalis', 'medialorbitofrontal', 'lateralorbitofrontal'],
'MPFC' : ['caudalanteriorcingulate', 'rostralanteriorcingulate', 'superiorfrontal'],
'LPFC' : [ 'parstriangularis', 'rostralmiddlefrontal', 'frontalpole', 'parsopercularis'],
'SMC' : [ 'precentral', 'caudalmiddlefrontal', 'postcentral', 'paracentral'],
'PC' : ['inferiorparietal', 'supramarginal', 'precuneus', 'posteriorcingulate', 'isthmuscingulate', 'superiorparietal'],
'MTC' : ['entorhinal', 'parahippocampal', 'fusiform'],
'LTC' : ['transversetemporal', 'superiortemporal', 'bankssts', 'inferiortemporal', 'middletemporal', 'temporalpole'],
'OCC' : ['pericalcarine', 'lingual', 'lateraloccipital', 'cuneus']}
return roiDict
def get_cortical_rois_detailed():
'''
returns more detailed cortical divisions
'''
roiDict = {'OFC' : ['parsorbitalis', 'medialorbitofrontal', 'lateralorbitofrontal'],
'MPFC' : ['caudalanteriorcingulate', 'rostralanteriorcingulate', 'superiorfrontal'],
'LPFC' : [ 'parstriangularis', 'rostralmiddlefrontal', 'frontalpole', 'parsopercularis'],
'SMC' : [ 'precentral', 'caudalmiddlefrontal', 'postcentral', 'paracentral'],
'PC' : ['inferiorparietal', 'supramarginal', 'precuneus', 'posteriorcingulate', 'isthmuscingulate', 'superiorparietal'],
'MTC' : ['entorhinal', 'parahippocampal', 'fusiform'],
'LTC' : ['transversetemporal', 'superiortemporal', 'bankssts', 'inferiortemporal', 'middletemporal', 'temporalpole'],
'OCC' : ['pericalcarine', 'lingual', 'lateraloccipital', 'cuneus']}
detailed = []
for key, roi_list in roiDict.iteritems():
detailed = detailed + roi_list
detailed_ROIs = {}
for roi in detailed:
detailed_ROIs[roi] = [roi]
print detailed_ROIs
return detailed_ROIs
if __name__ == '__main__':
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent('''\
{codeName} :
========================================
eg) {codeName} --input {in_put} --output {output}
'''.format(codeName=os.path.basename(__file__),
in_put = 'subjectLoc',
output = 'outLoc')))
parser.add_argument(
'-i', '--inputDir',
help='Subject location',
default=os.getcwd())
#parser.add_argument(
#'-o', '--output',
#help='Output',
#default=os.getcwd())
parser.add_argument(
'-b', '--backgrounds',
nargs='+',
help='backround subject inputs eg)-l subj1 subj2 subj3')
#default=[x for x in os.listdir(os.getcwd()) if os.path.isdir(x)])
parser.add_argument(
'-r', '--rois',
help='roi inputs in python list format',
default="ctx_lh_G_cuneus")
parser.add_argument(
'-g', '--graph',
help='Draw graph',
action='store_true')
parser.add_argument(
'-m', '--meanDf',
help='meanDf',
action='store_true')
#default = '/ccnc_bin/meanThickness/mean_thickness.csv')
parser.add_argument(
'-v', '--verbose',
help='Use detailed ROIs',
action='store_true')
#default = '/ccnc_bin/meanThickness/mean_thickness.csv')
parser.add_argument(
'-p', '--brain',
help='make brain picture',
action='store_true')
args = parser.parse_args()
main(args.inputDir, args.backgrounds, args.rois, args.meanDf, args.verbose, args.brain)