def motionBetween_measures():
studies = {'Diffusion1': 'dMRI_topup_eddy.nii.gz.eddy_parameters','Diffusion2': 'dMRI_topup_eddy.nii.gz.eddy_parameters','Diffusion3': 'dMRI_topup_eddy.nii.gz.eddy_parameters','Diffusion4':
'dMRI_topup_eddy.nii.gz.eddy_parameters','Rest1': 'fMRI_rest1_AP_motion.nii.gz.par','Rest2': 'fMRI_rest2_PA_motion.nii.gz.par','Rest3': 'fMRI_rest3_AP_motion.nii.gz.par','Rest4':
'fMRI_rest4_PA_motion.nii.gz.par', 'Gambling1' : 'tfMRI_gambling1_AP_motion.nii.gz.par','Gambling2' : 'tfMRI_gambling2_PA_motion.nii.gz.par','FaceMatching1' :
'tfMRI_faceMatching1_AP_motion.nii.gz.par','FaceMatching2' :
'tfMRI_faceMatching2_PA_motion.nii.gz.par','Conflict1':'tfMRI_conflict1_AP_motion.nii.gz.par','Conflict2':'tfMRI_conflict2_PA_motion.nii.gz.par','Conflict3':'tfMRI_conflict3_AP_motion.nii.gz.par',
'Conflict4':'tfMRI_conflict4_PA_motion.nii.gz.par', 'T1':'motion/T1_motion.nii.gz.par','T2':'motion/T2_motion.nii.gz.par'}
scans=['Diffusion1','Diffusion2','Diffusion3','Diffusion4','Rest1','Rest2','Rest3','Rest4','Gambling1','Gambling2','FaceMatching1','FaceMatching2','Conflict1','Conflict2','Conflict3','Conflict4','T1',
'T2']
metric = [getTranslation, getRotation] #, getTranslationAbsolute, getRotationAbsolute]
metric_label=["Translation (mm/s)" , "Rotation ($\degree/s$)", "Absolute translation per second", "Absolute rotation per second"]
pairsOfStudies = {'Diffusion1':'Diffusion2','Diffusion2':'Diffusion3','Diffusion3':'Diffusion4',
'Rest1':'Rest2','Rest2':'Rest3','Rest3':'Rest4','Gambling1':'Gambling2','FaceMatching1':'FaceMatching2','Conflict1':'Conflict2','Conflict2':'Conflict3', 'Conflict3':'Conflict4'}
pairsOfStudies=[('Diffusion1','Diffusion2'),('Diffusion2','Diffusion3'),('Diffusion3','Diffusion4'),('Rest1','Rest2'),('Rest2','Rest3'),('Rest3','Rest4'),('T1','T2'),('Gambling1','Gambling2'),(
'FaceMatching1', 'FaceMatching2'),('Conflict1','Conflict2'),('Conflict2','Conflict3'), ('Conflict3','Conflict4')]
diffusion=[ [0,98], [98,196],[196,295],[295,394]]
indexPerDiffusion = {'Diffusion1':[0,98], 'Diffusion2':[98,196], 'Diffusion3':[196,295],'Diffusion4':[295,394]}
fmri_order = {'Rest1':0,'Rest2':1,'Rest3':2,'Rest4':3,'Gambling1':4,'Gambling2':5, 'FaceMatching1':6,'FaceMatching2':7, 'Conflict1':8,'Conflict2':9,'Conflict3':10,'Conflict4':11}
subjects, sex, scores = utils.loadBANDA140(outliers)
motionBetween_output=csv.writer(open('/space/erebus/1/users/data/scores/new2/motionBetween_avg_output_rjj020419.csv','w+'))
motionBetween_output.writerow(['subject','rot_trans','avg_score','questionnaire','questionnaire_score'])
for key, score in scores.items():
f, axarr = plt.subplots(1, 2,figsize=(23,14))
f.subplots_adjust(left=.03, bottom=.06, right=.97, top=.95, wspace=.18, hspace=.28)
trans_boxplot_data = [[],[]]
rotat_boxplot_data = [[],[]]
#box plot measures
g, (boxes1,boxes2) = plt.subplots(nrows=1,ncols=2,figsize=(10,14))
for i,met in enumerate(metric):
axarr[i].set_title(str(met)[13:18])
if str(met)[13:18]=='Trans': boxes1.set_title(str(met)[13:18])
elif str(met)[13:18]=='Rotat': boxes2.set_title(str(met)[13:18])
#for calculating axis ranges
ymin_value = 0
ymax_value = 0
xmin_value = 0
xmax_value = 0
x_values = []
y_values = []
#for s in subjects:
for s_index,s in enumerate(subjects):
t_accumulate = 0
for a in range(len(pairsOfStudies)):
name1 , name2 = pairsOfStudies[a]
column,acq_time = getFileData(studies[name1])
if "Diffusion" in name1:
f1 = getFilePath(studies[name1],s)
f2 = getFilePath(studies[name2],s)
ind1=indexPerDiffusion[name1]
ind2=indexPerDiffusion[name2]
acq_time *= 99
elif "T1" in name1:
f1 = getFilePath("/motion/structural_motion.nii.gz.par",s)
f2 = getFilePath("/motion/structural_motion.nii.gz.par",s)
ind1=0,1
ind2=1,2
else:
f1 = getFilePath("/motion/fmriFirsts_motion.nii.gz.par",s)
f2 = getFilePath("/motion/fmriFirsts_motion.nii.gz.par",s)
ind1=fmri_order[name1], fmri_order[name1]+1
ind2=fmri_order[name2],fmri_order[name2]+1
if f1 != None and f2 != None :
t= met(f1,column, ind1,acq_time,0,f2, ind2)
t_accumulate += t
avg_t = t_accumulate/len(pairsOfStudies)
if s_index == 0: ymin_value = avg_t
ymin_value = min(ymin_value,avg_t)
ymax_value = max(ymax_value,avg_t)
if s_index == 0: xmin_value = float(score[s_index])
xmin_value = min(xmin_value,float(score[s_index]))
xmax_value = max(xmax_value,float(score[s_index]))
if str(str(key))=='Anhedonia':
if str(met)[13:18]=='Trans':
if float(score[s_index])<3: trans_boxplot_data[0].append(avg_t)
else: trans_boxplot_data[1].append(avg_t)
if str(met)[13:18]=='Rotat':
if float(score[s_index])<3: rotat_boxplot_data[0].append(avg_t)
else: rotat_boxplot_data[1].append(avg_t)
motionBetween_output.writerow([s,metric_label[metric.index(met)],avg_t,key,float(score[s_index])])
axarr[i].plot(float(score[s_index]),avg_t,"o",markersize=5, color='m')
x_values.append(float(score[s_index]))
y_values.append(avg_t)
#regression lines
(sl,b) = polyfit(x_values,y_values,1)
yp = polyval([sl,b],x_values)
axarr[i].plot(x_values,yp,'-',color='r')
axarr[i].set_ylabel("Average " + str(metric_label[metric.index(met)]))
axarr[i].set_xlabel(str(key))
axarr[i].set_xlim(xmin_value-(np.median(x_values)*.30),xmax_value+(np.median(x_values)*.30))
axarr[i].set_ylim(ymin_value-(np.median(y_values)*.30),ymax_value+(np.median(y_values)*.30))
if str(str(key))=='Anhedonia':
if i==0:
trans_box_max_list = [ max(a) for a in trans_boxplot_data]
trans_box_min_list = [ min(a) for a in trans_boxplot_data]
boxes1.set_ylabel("Average " + str(metric_label[metric.index(met)]))
boxes1.set_xlabel(str(key))
elif i==1:
rotat_box_max_list = [ max(a) for a in rotat_boxplot_data]
rotat_box_min_list = [ min(a) for a in rotat_boxplot_data]
boxes2.set_ylabel("Average " + str(metric_label[metric.index(met)]))
boxes2.set_xlabel(str(key))
f.savefig("/autofs/space/erebus_001/users/data/scores/new2/plots/motionBetween_avg_"+str(key),dpi=199)
#f.savefig("/space/erebus/1/users/vsiless/QA_plots//motionBetween_avg_"+str(key),dpi=199)
if str(str(key))=='Anhedonia':
boxes1.set_ylim(min(trans_box_min_list)-.25,max(trans_box_max_list)+.25)
boxes2.set_ylim(min(rotat_box_min_list)-.25,max(rotat_box_max_list)+.25)
#print(trans_boxplot_data[0])
#print(trans_boxplot_data[1])
boxes1.boxplot(trans_boxplot_data,labels=['no anhedonia','anhedonia'])
boxes2.boxplot(rotat_boxplot_data,labels=['no anhedonia','anhedonia'])
#g.savefig("/space/erebus/1/users/vsiless/QA_plots//motion_Between_avg_Anhedonia"+"_boxplot",dpi=199)
g.savefig("/autofs/space/erebus_001/users/data/scores/new2/plots/motion_Between_avg_Anhedonia"+"_boxplot",dpi=199)
def snr_measures(premade_output = None):
studies = {'Diffusion1':'dMRI_AP1.nii.gz','Diffusion2':'dMRI_PA1.nii.gz','Diffusion3':'dMRI_AP2.nii.gz','Diffusion4':'dMRI_PA2.nii.gz','Rest1':'fMRI_rest1_AP.nii.gz', 'Rest2':'fMRI_rest2_PA.nii.gz', 'Rest3':'fMRI_rest3_AP.nii.gz', 'Rest4':'fMRI_rest4_PA.nii.gz', 'Gambling1':'tfMRI_gambling1_AP.nii.gz', 'Gambling2':'tfMRI_gambling2_PA.nii.gz','FaceMatching1':'tfMRI_faceMatching1_AP.nii.gz', 'FaceMatching2':'tfMRI_faceMatching2_PA.nii.gz', 'Conflict1':'tfMRI_conflict1_AP.nii.gz', 'Conflict2':'tfMRI_conflict2_PA.nii.gz', 'Conflict3':'tfMRI_conflict3_AP.nii.gz', 'Conflict4':'tfMRI_conflict4_PA.nii.gz', 'T2':'T2.nii.gz','T1':'T1.nii.gz'}
scans=['Diffusion1','Diffusion2','Diffusion3','Diffusion4','Rest1','Rest2','Rest3','Rest4','Gambling1','Gambling2','FaceMatching1','FaceMatching2','Conflict1','Conflict2','Conflict3','Conflict4','T1','T2']
diffusion=[ [0,98], [98,196],[196,295],[295,394]]
if premade_output == None:
snr_output=csv.writer(open('/autofs/space/erebus_001/users/data/scores/new2/snr_avg_output_wComposites_rjj020419.csv','w+'))
snr_output.writerow(['subject','avg_snr','questionnaire','questionnaire_score'])
subjects, sex, scores = utils.loadBANDA140(outliers)
#print(subjects)
for key, score in scores.items():
f, axarr = plt.subplots(1,1,figsize=(23,14))
#f.tight_layout()
f.subplots_adjust(left=.03, bottom=.06, right=.97, top=.95, wspace=.18, hspace=.30)
axarr.set_title(str(key))
#for calculating axis ranges
ymin_value = 0
ymax_value = 0
xmin_value = 0
xmax_value = 0
x_values = []
y_values = []
#for s in subjects:
for s_index,s in enumerate(subjects):
print (s)
if premade_output ==None:
snr_accumulate = 0
for scan in scans:
roi="/space/erebus/1/users/data/preprocess/"+s+"/snr/WMROI5001_2"+studies[scan]
image="/space/erebus/1/users/data/preprocess/"+s+"/"+studies[scan]
mean2="/space/erebus/1/users/data/preprocess/"+s+"/snr/mean"+studies[scan]
std2="/space/erebus/1/users/data/preprocess/"+s+"/snr/std"+studies[scan]
snr2="/space/erebus/1/users/data/preprocess/"+s+"/snr/snr"+studies[scan]
#ROI= nib.load("/space/erebus/1/users/data/preprocess/"+s+"/snr/WMROI4010_2"+studies[scan]).get_data()
#image = nib.load("/space/erebus/1/users/data/preprocess/"+s+"/"+studies[scan]).get_data()
snr=0
if os.path.isfile(image) :
#os.popen("mri_concat --i "+image+ " --mean --o "+mean2).read()
#os.popen("mri_concat --i "+image+ " --std --o "+std2).read()
if "T1" in scan or "T2" in scan:
mean=os.popen("fslstats "+image+" -k "+roi+" -m").read()
std=os.popen("fslstats "+image+" -k "+roi+" -s").read()
snr = float(mean.split("\n")[0])/float(std.split("\n")[0])
#print( snr)
elif os.path.isfile(std2):
#os.popen("fscalc "+mean2 +" div "+std2 +" --o " +snr2).read()
res=os.popen("fslstats "+snr2+" -k "+roi+" -m").read()
snr= float(res.split("\n")[0])
snr_accumulate+= snr
avg_snr = snr_accumulate/len(scans)
x = float(score[s_index])
y = avg_snr
snr_output.writerow([s,y,key,x])
else:
snr_accumulate = 0
for scan in scans:
with open('/space/erebus/1/users/data/scores/snr_output_master.csv', 'r') as output:
output_reader = csv.DictReader(output, delimiter = ",")
for row in iter(output_reader):
skip = 0
if str(row['subject'])==s:
print (row['subject'],s,key,scan)
if str(row['questionnaire'])==key and str(row['scan'])==scan:
print (key,scan)
snr_accumulate += float(row['score'])
if scans.index(scan) == 0: x = float(row['questionnaire_score'])
break
else:
if skip > 0:skip+= 70
else: skip+=69
for i in range(skip):
next(iter(output_reader))
avg_snr = snr_accumulate/len(scans)
y = avg_snr
axarr.plot(x, y, "o",markersize=5, c='m')
x_values.append(x)
y_values.append(y)
if s_index == 0: ymin_value = y
ymin_value = min(ymin_value,y)
ymax_value = max(ymax_value,y)
if s_index == 0: xmin_value = x
xmin_value = min(xmin_value,x)
xmax_value = max(xmax_value,x)
if key == 'Anhedonia':
x_1 = [g for g in x_values if g>=3]
x_2 = [g for g in x_values if g<3]
#regression lines
(sl,b) = polyfit(x_1,y_values[-len(x_1):], 1)
yp = polyval([sl,b],x_1)
axarr.plot(x_1,yp,'-',color='g')
#regression lines
(sl,b) = polyfit(x_2,y_values[:len(x_2)], 1)
yp = polyval([sl,b],x_2)
axarr.plot(x_2,yp,'-',color='b')
else:
#regression lines
(sl,b) = polyfit(x_values,y_values, 1)
yp = polyval([sl,b],x_values)
axarr.plot(x_values,yp,'-',color='r')
#GLM
X=np.array([])
if key != 'Anhedonia':
X = np.ones(len(x_values))
X =np.stack((X, x_values), axis=-1)
cval = np.hstack((0, -1))
else:
for x in x_values:
if x>3:
X=np.append(X,[[1,0]])
else:
X=np.append(X,[[0,1]])
cval = np.hstack((1, -1))
X= np.array(X).reshape(len(y_values),2)
Y=np.array(y_values )
model = GeneralLinearModel(X)
model.fit(Y)
z_vals = model.contrast(cval).p_value() # z-transformed statistics
print(key, z_vals)
axarr.set_ylabel("Average SNR")
axarr.set_xlabel(str(key))
axarr.set_xlim(xmin_value-(np.median(x_values)*.30),xmax_value+(np.median(x_values)*.30))
axarr.set_ylim(ymin_value-(np.median(y_values)*.30),ymax_value+(np.median(y_values)*.30))
f.savefig("/autofs/space/erebus_001/users/data/scores/new2/plots/snr_avg_"+str(key),dpi=199)
def motionWithin_measures():
studies = {'Diffusion1': 'dMRI_topup_eddy.nii.gz.eddy_parameters','Diffusion2': 'dMRI_topup_eddy.nii.gz.eddy_parameters','Diffusion3': 'dMRI_topup_eddy.nii.gz.eddy_parameters','Diffusion4':
'dMRI_topup_eddy.nii.gz.eddy_parameters','Rest1': 'fMRI_rest1_AP_motion.nii.gz.par','Rest2': 'fMRI_rest2_PA_motion.nii.gz.par','Rest3': 'fMRI_rest3_AP_motion.nii.gz.par','Rest4':
'fMRI_rest4_PA_motion.nii.gz.par', 'Gambling1' : 'tfMRI_gambling1_AP_motion.nii.gz.par','Gambling2' : 'tfMRI_gambling2_PA_motion.nii.gz.par','FaceMatching1' :
'tfMRI_faceMatching1_AP_motion.nii.gz.par','FaceMatching2' :
'tfMRI_faceMatching2_PA_motion.nii.gz.par','Conflict1':'tfMRI_conflict1_AP_motion.nii.gz.par','Conflict2':'tfMRI_conflict2_PA_motion.nii.gz.par','Conflict3':'tfMRI_conflict3_AP_motion.nii.gz.par',
'Conflict4':'tfMRI_conflict4_PA_motion.nii.gz.par', 'T1':'motion/T1_motion.nii.gz.par','T2':'motion/T2_motion.nii.gz.par'}
scans=['T1'] #'Diffusion1','Diffusion2','Diffusion3','Diffusion4'] #,'Rest1','Rest2','Rest3','Rest4','Gambling1','Gambling2','FaceMatching1','FaceMatching2','Conflict1','Conflict2','Conflict3','Conflict4','T1','T2']
#scans.append("T1 all vnavs - no reacq")
#scans.append("T2 all vnavs - no reacq")
#scans=['T2']
diffusion=[ [0,98], [98,196],[196,295],[295,394]]
metric = [getTranslation, getRotation] #, getTranslationAbsolute, getRotationAbsolute]
metric_label=["Translation (mm/s)" , "Rotation ($\degree/s$)", "Absolute translation per second", "Absolute rotation per second"]
indexPerDiffusion = {'Diffusion1':[0,98], 'Diffusion2':[98,196], 'Diffusion3':[196,295],'Diffusion4':[295,394]}
fmri_order = {'Rest1':0,'Rest2':1,'Rest3':2,'Rest4':3,'Gambling1':4,'Gambling2':5, 'FaceMatching1':6,'FaceMatching2':7, 'Conflict1':8,'Conflict2':9,'Conflict3':10,'Conflict4':11}
subjects, sex, scores = utils.loadBANDA140(outliers)
motionWithin_output=csv.writer(open('/space/erebus/1/users/data/scores/new2/test_rjj091219.csv','w+'))
motionWithin_output.writerow(['subject','rot_trans','avg_score','questionnaire','questionnaire_score'])
for key, score in scores.items():
print (scans)
f, axarr = plt.subplots(1, 2,figsize=(23,14))
f.subplots_adjust(left=.03, bottom=.06, right=.97, top=.95, wspace=.18, hspace=.28)
trans_boxplot_data = [[],[]]
rotat_boxplot_data = [[],[]]
anhedonia = 0
anhedonia_no = 0
#box plot measures
g, (boxes1,boxes2) = plt.subplots(nrows=1,ncols=2,figsize=(15,14))
for i,met in enumerate(metric):
axarr[i].set_title(str(met)[13:18])
if str(met)[13:18]=='Trans': boxes1.set_title(str(met)[13:18])
elif str(met)[13:18]=='Rotat': boxes2.set_title(str(met)[13:18])
#for calculating axis ranges
ymin_value = 0
ymax_value = 0
xmin_value = 0
xmax_value = 0
x_values = []
y_values = []
#for s in subjects:
for s_index,s in enumerate(subjects):
print(s)
t_accumulate = 0
for name in scans:
fileN = studies[name.split()[0]]
column,acq_time = getFileData(fileN)
if "no vnavs" in name:
if "T1" in name :
index=[0,166]
else:
index=[0,111]
else:
index=[0,1000]
if "dMRI" in fileN:
index =diffusion[i]
filePath = getFilePath(fileN,s)
#print(i,j)
#print(name)
if filePath != None :
#print (name)
if "T1" == name:
ind = readVNavsScoreFiles(s, "T1")
elif "T2" == name:
ind = readVNavsScoreFiles(s, "T2")
#print(s, ind)
else:
ind = range(index[0], index[1])
t= met(filePath,column,np.sort(ind),acq_time)
t_accumulate += t
avg_t = t_accumulate/len(scans)
if s_index == 0: ymin_value = avg_t
ymin_value = min(ymin_value,avg_t)
ymax_value = max(ymax_value,avg_t)
if s_index == 0: xmin_value = float(score[s_index])
xmin_value = min(xmin_value,float(score[s_index]))
xmax_value = max(xmax_value,float(score[s_index]))
if str(str(key))=='Anhedonia':
if str(met)[13:18]=='Trans':
if float(score[s_index])<3:
trans_boxplot_data[0].append(avg_t)
anhedonia_no += 1
else:
trans_boxplot_data[1].append(avg_t)
anhedonia += 1
if str(met)[13:18]=='Rotat':
if float(score[s_index])<3: rotat_boxplot_data[0].append(avg_t)
else: rotat_boxplot_data[1].append(avg_t)
motionWithin_output.writerow([s,metric_label[metric.index(met)],avg_t,key,float(score[s_index])])
axarr[i].plot(float(score[s_index]),avg_t,"o",markersize=5, color='m')
x_values.append(float(score[s_index]))
y_values.append(avg_t)
#regression lines
(sl,b) = polyfit(x_values,y_values,1)
yp = polyval([sl,b],x_values)
axarr[i].plot(x_values,yp,'-',color='r')
#GLM
X=np.array([])
if key != 'Anhedonia':
X = np.ones(len(x_values))
X =np.stack((X, x_values), axis=-1)
cval = np.hstack((0, -1))
else:
for x in x_values:
if x>=3:
X=np.append(X,[[1,0]])
else:
X=np.append(X,[[0,1]])
cval = np.hstack((1, -1))
X= np.array(X).reshape(len(y_values),2)
Y=np.array(y_values )
model = GeneralLinearModel(X)
model.fit(Y)
z_vals = model.contrast(cval).p_value() # z-transformed statistics
print(key, z_vals)
axarr[i].set_ylabel("Average" + str(metric_label[metric.index(met)]))
axarr[i].set_xlabel(str(key))
axarr[i].set_xlim(xmin_value-(np.median(x_values)*.30),xmax_value+(np.median(x_values)*.30))
axarr[i].set_ylim(ymin_value-(np.median(y_values)*.30),ymax_value+(np.median(y_values)*.30))
if str(key)=='Anhedonia':
if i==0:
trans_box_max_list = [ max(a) for a in trans_boxplot_data]
trans_box_min_list = [ min(a) for a in trans_boxplot_data]
boxes1.set_ylabel("Average " + str(metric_label[metric.index(met)]))
boxes1.set_xlabel(str(key))
elif i==1:
rotat_box_max_list = [ max(a) for a in rotat_boxplot_data]
rotat_box_min_list = [ min(a) for a in rotat_boxplot_data]
boxes2.set_ylabel("Average " + str(metric_label[metric.index(met)]))
boxes2.set_xlabel(str(key))
#f.savefig("/space/erebus/1/users/data/comparisonPlots_1_88/plots/motion_snr/motionWithin_avg_"+str(key),dpi=199)
f.savefig("/autofs/space/erebus_001/users/data/scores/new2/plots/test_09122019_motion_Within_avg_"+str(key),dpi=199)
if str(str(key))=='Anhedonia':
boxes1.set_ylim(0,max(trans_box_max_list)+.25)
boxes2.set_ylim(0,max(rotat_box_max_list)+.25)
#print(trans_boxplot_data[0])
#print(trans_boxplot_data[1])
boxes1.boxplot(trans_boxplot_data,labels=['no anhedonia','anhedonia'])
boxes2.boxplot(rotat_boxplot_data,labels=['no anhedonia','anhedonia'])
g.savefig("/autofs/space/erebus_001/users/data/scores/new2/plots/test_09112019_motion_Within_avg_Anhedonia"+"_boxplot",dpi=199)
#g.savefig("/space/erebus/1/users/vsiless/QA_plots//motion_Within_avg_Anhedonia"+"_boxplot",dpi=199)
print(anhedonia)
print(anhedonia_no)