def plotSNRAvg():
snr_perScan = dict()
subjects, subjects_lbl = utils.getBandaLabels(outliers)
with open(
'/autofs/space/erebus_001/users/data/scores/new2/snr_avg_output_rjj020419.csv',
'r') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in spamreader:
try:
s = row[0]
scan = row[1][:-1]
snr = float(row[2])
if snr > 0:
if not scan in snr_perScan:
snr_perScan[scan] = [[], [], []]
if s in subjects_lbl['control']:
snr_perScan[scan][0].append(snr)
elif s in subjects_lbl['anx']:
snr_perScan[scan][1].append(snr)
elif s in subjects_lbl['dep']:
snr_perScan[scan][2].append(snr)
except:
print(row)
f, axarr = plt.subplots(1, 6, figsize=(23, 5))
f.subplots_adjust(left=.03,
bottom=.06,
right=.97,
top=.95,
wspace=.18,
hspace=.30)
plotted = 0
for scanName, values in snr_perScan.items():
print(scanName)
axarr[plotted].set_title(scanName)
for q in range(len(values)):
axarr[plotted].violinplot(values[q], [q],
points=20,
widths=0.3,
showmeans=True) #,showmedians=True)
if scanName == 'T':
axarr[plotted].set_ylim((0, 15))
else:
axarr[plotted].set_ylim((5, 30))
axarr[plotted].set_xticks([0, 1, 2],
["Controls", "Anxious", "Depressed"])
#f.savefig("/space/erebus/1/users/vsiless/QA_plots/snr",dpi=199)
plotted += 1
for ax in axarr.flatten():
ax.set_xticks([0, 1, 2])
ax.set_xticklabels(["Controls", "Anxious", "Depressed"])
f.savefig(
"/autofs/space/erebus_001/users/data/scores/new2/plots/snrAvg_140",
dpi=199)
def snr():
output = csv.writer(
open(
'/autofs/space/erebus_001/users/data/scores/new2/snr_avg_output_vivs.csv',
'w+'))
output.writerow(['subject', 'scan', 'score'])
subjects, subjects_lbl = utils.getBandaLabels(outliers)
for scan in scans:
print(scan)
for s_index, s in enumerate(subjects_lbl['allsubjects']):
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()
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()
image = nib.load("/space/erebus/1/users/data/preprocess/" +
s + "/" + studies[scan]).get_data()
std = np.std(image[:20, :20, :20])
snr = float(mean.split("\n")[0]) / std
print(snr)
elif "Diffusion" in scan:
mean = os.popen("fslstats " + mean2 + " -k " + roi +
" -m").read()
image = nib.load("/space/erebus/1/users/data/preprocess/" +
s + "/" + studies[scan]).get_data()
std = np.std(image[:20, :20, :20])
snr = float(mean.split("\n")[0]) / std
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])
output.writerow([s, scan, snr])
def plotWithinScanMotion():
perScan = [dict(), dict()]
tipo = "within" #"between"
labels = ["Rotation", "Translation"]
subjects, subjects_lbl = utils.getBandaLabels(outliers)
with open(
'/space/erebus/1/users/data/scores/new2/motion_' + tipo +
'_scan_output_140.csv', 'r') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in spamreader:
try:
s = row[0]
if "Rotation" in row[1]:
ind = 0
else:
ind = 1
if "T" not in row[2]:
scan = row[2][:-1]
else:
scan = row[2]
val = float(row[3])
#except:
# val=0
if val > 0:
if not scan in perScan[ind]:
perScan[ind][scan] = [[], [], []]
if s in subjects_lbl['control']:
perScan[ind][scan][0].append(val)
elif s in subjects_lbl['anx']:
perScan[ind][scan][1].append(val)
elif s in subjects_lbl['dep']:
perScan[ind][scan][2].append(val)
except:
print(row)
f, axarr = plt.subplots(2, 9, figsize=(23, 5))
f.subplots_adjust(left=.03,
bottom=.06,
right=.97,
top=.95,
wspace=.18,
hspace=.30)
for i in range(2):
plotted = 0
for scanName, values in perScan[i].items():
print(scanName)
axarr[i][plotted].set_title(scanName)
for q in range(len(values)):
axarr[i][plotted].violinplot(
values[q], [q], points=20, widths=0.3,
showmeans=True) #,showmedians=True)
axarr[i][plotted].set_ylim((0, 1))
axarr[i][plotted].set_ylabel(labels[i])
axarr[i][plotted].set_xticks([0, 1, 2],
["Controls", "Anxious", "Depressed"])
# f.savefig("/space/erebus/1/users/vsiless/QA_plots/"+tipo,dpi=199)
plotted += 1
for ax in axarr.flatten():
ax.set_xticks([0, 1, 2, 3])
ax.set_xticklabels(
["Controls", "Anxious", "Comorbid", "Depressed"])
# plt.show()
f.savefig("/autofs/space/erebus_001/users/data/scores/new2/plots/" + tipo +
"_withinScanMotion",
dpi=199)
def plotLTAMovement():
subjects, subjects_lbl = utils.getBandaLabels(outliers)
fg, axarr = plt.subplots(1, 2)
files = [
'dMRI_AP1_2_T1.lta_diff', 'dMRI_PA1_2_dMRI_AP1.lta_diff',
'fMRI_rest1_AP_2_dMRI_PA1.lta_diff',
'fMRI_rest2_PA_2_fMRI_rest1_AP.lta_diff',
'dMRI_AP2_2_fMRI_rest2_PA.lta_diff', 'dMRI_PA2_2_dMRI_AP2.lta_diff',
'fMRI_rest3_AP_2_dMRI_PA2.lta_diff',
'fMRI_rest4_PA_2_fMRI_rest3_AP.lta_diff',
'tfMRI_gambling1_AP_2_fMRI_rest4_PA.lta_diff',
'tfMRI_gambling2_PA_2_tfMRI_gambling1_AP.lta_diff',
'tfMRI_faceMatching1_AP_2_tfMRI_gambling2_PA.lta_diff',
'tfMRI_faceMatching2_PA_2_tfMRI_faceMatching1_AP.lta_diff',
'tfMRI_conflict1_AP_2_tfMRI_faceMatching2_PA.lta_diff',
'tfMRI_conflict2_PA_2_tfMRI_conflict1_AP.lta_diff',
'tfMRI_conflict3_AP_2_tfMRI_conflict2_PA.lta_diff',
'tfMRI_conflict4_PA_2_tfMRI_conflict3_AP.lta_diff',
'T2_2_tfMRI_conflict4_PA.lta_diff'
]
labels = [
'T1', 'dMRI1', 'dMRI2', 'fMRI1', 'fMR2', 'dMRI3', 'dMRI4', 'fMRI3',
'fMRI4', 'gambling1', 'gambling2', 'faceMatching1', 'faceMatchin2',
'conflict1', 'conflict2', 'conflict3', 'conflict4', 'T2'
]
for s in subjects:
angles = []
trans = []
for f in files:
lta_diff = open("/space/erebus/1/users/data/preprocess/" + s +
"/motion/" + f)
for line in lta_diff:
if "RotAngle" in line:
angles.append((float(line.split()[2]) * 180 / 3.15) % 180)
if "AbsTrans" in line:
trans.append(line.split()[2])
axarr[0].plot(angles, label=s)
axarr[0].set_xlabel('scans')
axarr[0].set_ylabel('angle')
#axarr[0].get_xticklabels().set_rotation(90)
#plt.setp(axarr, xticks=range(len(labels)), xticklabels=labels)
axarr[1].plot(trans, label=s)
axarr[1].set_xlabel('scans')
axarr[1].set_ylabel('translation (mm)')
labels2 = []
for i in range(1, len(labels)):
labels2.append(labels[i] + "-" + labels[i - 1])
matplotlib.pyplot.sca(axarr[1])
plt.xticks(rotation=90)
#axarr[1].get_xticklabels().set_rotation(90)
axarr[1].legend(ncol=3)
#plt.setp(axarr, xticks=range(len(labels)), xticklabels=labels)
plt.setp(axarr, xticks=range(len(labels)), xticklabels=labels2)
matplotlib.pyplot.sca(axarr[0])
plt.xticks(rotation=90)
# plt.show()
fg.savefig(
"/autofs/space/erebus_001/users/data/scores/new2/plots/motion_LTA_movement_patvscontrol",
dpi=199)
def plotBetweenScanMotion():
#output=csv.writer(open('/space/erebus/1/users/data/scores/motion_between_scan_output.csv','w+'))
#output.writerow(['subject','rot_trans','scan_type','score'])
output = csv.writer(
open(
'/space/erebus/1/users/data/scores/_motion_between_scan_FD_output_140.csv',
'w+'))
output.writerow(['subject', 'rot_trans', 'scan_type', 'score'])
scans.append("T1 all vnavs - no reacq")
scans.append("T2 all vnavs - no reacq")
# outliers=["BANDA141","BANDA142","BANDA143"]
subjects, subjects_lbl = utils.getBandaLabels(outliers)
for m in metric:
fg, axarr = plt.subplots(3, 4)
fg.subplots_adjust(left=.03,
bottom=.06,
right=.97,
top=.95,
wspace=.18,
hspace=.28)
num = 0
#for name1, name2 in pairsOfStudies.items():
for a in range(len(pairsOfStudies)):
name1, name2 = pairsOfStudies[a]
print(name1, name2)
i = num % 3
j = int(num / 3)
axarr[i, j].set_title(name1 + "_" + name2)
name = "%s_%s" % (name1, name2)
y_values = []
for s_index, s in enumerate(subjects_lbl['allsubjects']):
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 = m(f1, column, ind1, acq_time, 0, f2, ind2) #/acq_time
y_values.append(t)
#axarr[i,j].plot(subjects.index(s)+1, t, "o",c='C'+str(s_index%10))
output.writerow([s, m, name, t])
#color by subject type
if s in subjects_lbl['control']:
axarr[i,
j].plot(subjects_lbl['allsubjects'].index(s) + 1,
t,
"o",
c='r')
elif s in subjects_lbl['anx']:
axarr[i,
j].plot(subjects_lbl['allsubjects'].index(s) + 1,
t,
"o",
c='c')
elif s in subjects_lbl['dep']:
axarr[i,
j].plot(subjects_lbl['allsubjects'].index(s) + 1,
t,
"o",
c='y')
if j == 0:
axarr[i, j].set_ylabel(metric_label[metric.index(m)])
if i == 2:
axarr[i, j].set_xlabel('Subject')
#axarr[i,j].axvline(14.5, color='k', linestyle='--')
axarr[i, j].set_xticks(range(1, len(subjects) + 1), 20)
#axarr[i,j].set_xticklabels(np.arange(1,len(subjects)+1,20))
num += 1
#if metric.index(m)==1 :
if max(y_values) < 10:
axarr[i, j].set_ylim(-.1, max(y_values) + 1)
control_patch = mpatches.Patch(color='r', label='control')
anxious_patch = mpatches.Patch(color='c', label='anxious')
depressed_patch = mpatches.Patch(color='y', label='depressed')
axarr[i, j].legend(
handles=[control_patch, anxious_patch, depressed_patch])
plt.show()
def plotWithinScanMotion():
output = csv.writer(
open(
'/space/erebus/1/users/data/scores/motion_within_scan_FD_output_140.csv',
'w+'))
output.writerow(['subject', 'rot_trans', 'scan_type', 'score'])
scans.append("T1 all vnavs - no reacq")
scans.append("T2 all vnavs - no reacq")
# outliers=["BANDA141","BANDA142","BANDA143"]
subjects, subjects_lbl = utils.getBandaLabels(outliers)
for m in metric:
fg, axarr = plt.subplots(4, 5)
fg.subplots_adjust(left=.03,
bottom=.06,
right=.97,
top=.95,
wspace=.18,
hspace=.28)
num = 0
for name in scans:
fileN = studies[name.split()[0]]
i = num % 4
j = int(num / 4)
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]
axarr[i, j].set_title(name)
#for s in subjects:
for s_index, s in enumerate(subjects_lbl['allsubjects']):
f = getFilePath(fileN, s)
if f != None:
print(name)
if "T1" == name:
ind = readVNavsScoreFiles(s, "T1")
#print(np.sort(ind) )
elif "T2" == name:
ind = readVNavsScoreFiles(s, "T2")
#print(np.sort(ind))
else:
#print("hola")
ind = range(index[0], index[1])
t = m(f, column, np.sort(ind), acq_time)
#axarr[i,j].plot(subjects.index(s)+1, t, "o",c='C'+str(s_index%10))
output.writerow([s, m, name, t])
#color by subject type
if s in subjects_lbl['control']:
axarr[i,
j].plot(subjects_lbl['allsubjects'].index(s) + 1,
t,
"o",
c='r')
elif s in subjects_lbl['anx']:
axarr[i,
j].plot(subjects_lbl['allsubjects'].index(s) + 1,
t,
"o",
c='c')
elif s in subjects_lbl['dep']:
axarr[i,
j].plot(subjects_lbl['allsubjects'].index(s) + 1,
t,
"o",
c='y')
if j == 0:
axarr[i, j].set_ylabel(metric_label[metric.index(m)])
if i == 3:
axarr[i, j].set_xlabel('Subject')
if metric.index(m) > 1:
axarr[i, j].set_ylim((-.1, 7))
else:
axarr[i, j].set_ylim((-.1, 1))
axarr[i, j].axvline(14.5, color='k', linestyle='--')
axarr[i, j].set_xticks(range(1, len(subjects) + 1), 20)
num += 1
control_patch = mpatches.Patch(color='r', label='control')
anxious_patch = mpatches.Patch(color='c', label='anxious')
depressed_patch = mpatches.Patch(color='y', label='depressed')
axarr[i, j].legend(
handles=[control_patch, anxious_patch, depressed_patch])
#fg.savefig("/autofs/space/erebus_001/users/data/scores/new2/plots/motion_within_scan_patvscontrol"+m,dpi=199)
plt.show()