#import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
import smooth_gaussian
#possibly put in os function to specify path later
#this file was more of a test, but since can easily adapt it into a more useful
#script i left it in utils
img = nib.load("../../data/ds005/sub001/BOLD/task001_run001/bold.nii.gz")
data = img.get_data()
#just plot a random slice to see if works
spatial2 = smooth_gaussian.smooth_spatial(data, fwhm = 5)[:, :, 16, 50]
spatial3 = smooth_gaussian.smooth_spatial(data,fwhm = 4)[:, :, 16, 50]
spatial4 = smooth_gaussian.smooth_spatial(data,fwhm = 3)[:, :, 16, 50]
f, (ax1, ax2, ax3, ax4) = plt.subplots(4, sharex = True, sharey = True)
ax1.imshow(data[:, :, 16, 50], cmap = 'gray')
ax1.set_title('Original')
ax2.imshow(spatial2, cmap = 'gray')
ax2.set_title('fwhm = 5 mm')
ax3.imshow(spatial3, cmap = 'gray')
ax3.set_title('fwhm = 4 mm')
ax4.imshow(spatial4, cmap = 'gray')
ax4.set_title('fwhm = 3mm')
import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../utils/functions')
import smooth_gaussian
import os
os.chdir("../../data")
plot_number = 1
fig = plt.figure(figsize = (50, 70))
for subject in range(1, 17):
for run in range(1, 4):
path = 'ds005/sub0' + str(subject).zfill(2) + '/BOLD/task001_run00' + str(run) + '/bold.nii.gz'
img = nib.load(path)
data = img.get_data()
data = smooth_gaussian.smooth_spatial(data)
mean_data = np.mean(data, axis = -1)
axis = fig.add_subplot(16, 3, plot_number )
axis.hist(np.ravel(mean_data), bins = 100)
axis.set_title('Subject ' + str(subject) + ', Run ' + str(run))
plot_number += 1
#fig.tight_layout()
fig.savefig('../paper/figures/mean_hists.png')
#import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
import smooth_gaussian
#possibly put in os function to specify path later
#this file was more of a test, but since can easily adapt it into a more useful
#script i left it in utils
img = nib.load("../../data/ds005/sub001/BOLD/task001_run001/bold.nii.gz")
data = img.get_data()
#just plot a random slice to see if works
spatial2 = smooth_gaussian.smooth_spatial(data, fwhm=5)[:, :, 16, 50]
spatial3 = smooth_gaussian.smooth_spatial(data, fwhm=4)[:, :, 16, 50]
spatial4 = smooth_gaussian.smooth_spatial(data, fwhm=3)[:, :, 16, 50]
f, (ax1, ax2, ax3, ax4) = plt.subplots(4, sharex=True, sharey=True)
ax1.imshow(data[:, :, 16, 50], cmap='gray')
ax1.set_title('Original')
ax2.imshow(spatial2, cmap='gray')
ax2.set_title('fwhm = 5 mm')
ax3.imshow(spatial3, cmap='gray')
ax3.set_title('fwhm = 4 mm')
ax4.imshow(spatial4, cmap='gray')
ax4.set_title('fwhm = 3mm')
plt.savefig("smoothed_images.png")
plt.close()
import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../utils')
import smooth_gaussian
import os
os.chdir("../../data")
plot_number = 1
fig = plt.figure(figsize = (50, 70))
for subject in range(1, 17):
for run in range(1, 4):
path = 'ds005/sub0' + str(subject).zfill(2) + '/BOLD/task001_run00' + str(run) + '/bold.nii.gz'
img = nib.load(path)
data = img.get_data()
data = smooth_gaussian.smooth_spatial(data)
mean_data = np.mean(data, axis = -1)
axis = fig.add_subplot(16, 3, plot_number )
axis.hist(np.ravel(mean_data), bins = 100)
axis.set_title('Subject ' + str(subject) + ', Run ' + str(run))
plot_number += 1
#fig.tight_layout()
fig.savefig('../code/scripts/mean_hists.png')
fd_out = json.load(open("../results/texts/fdOutliers.txt"))
# Threshold cutoff from histogram observation
threshold = 7500
for i in range(1,17):
# first three dimension for data shape is 91, 109, 91.
# create array to store the combined dataset of three runs
data_full = np.empty([91, 109, 91, 0])
gain_full = np.empty([0,])
loss_full = np.empty([0,])
for j in range(1,4):
boldname='ds005/sub0'+str(i).zfill(2)+'/model/model001/task001_run00'+`j`+'.feat/filtered_func_data_mni.nii.gz'
img=nib.load(boldname)
data=img.get_data()
data=smooth_spatial(data)
run = j
behav_cond = 'ds005/sub0'+str(i).zfill(2)+'/behav/task001_run00'+`j`+'/behavdata.txt'
task_cond1 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond001.txt'
task_cond2 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond002.txt'
task_cond3 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond003.txt'
task_cond4 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond004.txt'
parameters = merge_cond(behav_cond, task_cond1, task_cond2, task_cond3, task_cond4)
neural_prediction = events2neural_extend(parameters,TR, n_vols)
gain, loss, linear_dr, quad_dr = getRegressor(TR, n_vols, hrf_at_trs, neural_prediction, standard = True)
data, gain, loss, linear_dr, quad_dr = deleteOutliers(data, gain, loss, i, run, dvars_out, fd_out)
data_full = np.concatenate((data_full,data),axis=3)
gain_full = np.concatenate((gain_full,gain),axis=0)
loss_full = np.concatenate((loss_full,loss),axis=0)
# mea=calcMRSS(data_full, gain_full, loss_full, None, None, threshold)
X, Y, beta=calcBeta(data_full, gain_full, loss_full, None, None, threshold)
threshold = 400
for i in range(1,17):
# first three dimension for data shape is 64, 64, 34.
# create array to store the combined dataset of three runs
data_full = np.empty([64, 64, 34, 0])
gain_full = np.empty([0,])
loss_full = np.empty([0,])
linear_full = np.empty([0,])
quad_full = np.empty([0,])
for j in range(1,4):
direct='ds005/sub0'+str(i).zfill(2)+'/BOLD/task001_run00'+`j`+'/'
boldname = direct+'bold.nii.gz'
img=nib.load(boldname)
data=img.get_data()
data=smooth_spatial(data)
run = j
behav_cond = 'ds005/sub0'+str(i).zfill(2)+'/behav/task001_run00'+`j`+'/behavdata.txt'
task_cond1 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond001.txt'
task_cond2 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond002.txt'
task_cond3 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond003.txt'
task_cond4 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond004.txt'
parameters = merge_cond(behav_cond, task_cond1, task_cond2, task_cond3, task_cond4)
neural_prediction = events2neural_extend(parameters,TR, n_vols)
gain, loss, linear_dr, quad_dr = getRegressor(TR, n_vols, hrf_at_trs, neural_prediction)
data, gain, loss, linear_dr, quad_dr = deleteOutliers(data, gain, loss, linear_dr, quad_dr, i, run, dvars_out, fd_out)
data_full = np.concatenate((data_full,data),axis=3)
gain_full = np.concatenate((gain_full,gain),axis=0)
loss_full = np.concatenate((loss_full,loss),axis=0)
linear_full = np.concatenate((linear_full,linear_dr),axis=0)
quad_full = np.concatenate((quad_full,quad_dr),axis=0)
