def design_matrix(subject, run, TR = 2.5):
data = bold_data(subject,run)
vol_shape, n_trs = data.shape[:-1], data.shape[-1]
tr_times = np.arange(0,30,TR)
hrf_at_trs = hrf(tr_times)
col = 0
X = np.ones((n_trs,14))
#Smoothed and masked data
mean_data = np.mean(data,axis=-1)
masked, mask = median_otsu(mean_data,2,1)
# smooth_data = gaussian_filter(data,[2,2,2,0])
# Y = smooth_data[mask].T
#omitted smoothing for now
Y = data[mask].T
#Adding onsets to design matrix
for i in list_cond_file(subject,run):
neural_prediction = events2neural_fixed(i, TR, n_trs)
convolved = convolve(neural_prediction, hrf_at_trs)
X[:,col] = convolved
col = col+1
##PCA
Y_demeaned = Y - np.mean(Y,axis=1).reshape([-1,1])
unscaled_cov = Y_demeaned.dot(Y_demeaned.T)
U, S, V = npl.svd(unscaled_cov)
X[:,8] = U[:,0]
X[:,9:11] = U[:,6:8]
linear_drift = np.linspace(-1,1,n_trs)
X[:,11] = linear_drift
quadratic_drift = linear_drift ** 2
quadratic_drift -= np.mean(quadratic_drift)
X[:,12]= quadratic_drift
betas = npl.pinv(X).dot(Y)
betas_vols = np.zeros(vol_shape+(14,))
betas_vols[mask,:] = betas.T
projections = U.T.dot(Y_demeaned)
projection_vols = np.zeros(data.shape)
projection_vols[mask,:] = projections.T
return X, Y, betas_vols, mask, U, Y_demeaned, mean_data, projection_vols
def design_matrix(subject, run, TR=2.5):
data = bold_data(subject, run)
vol_shape, n_trs = data.shape[:-1], data.shape[-1]
tr_times = np.arange(0, 30, TR)
hrf_at_trs = hrf(tr_times)
col = 0
X = np.ones((n_trs, 14))
#Smoothed and masked data
mean_data = np.mean(data, axis=-1)
masked, mask = median_otsu(mean_data, 2, 1)
# smooth_data = gaussian_filter(data,[2,2,2,0])
# Y = smooth_data[mask].T
#omitted smoothing for now
Y = data[mask].T
#Adding onsets to design matrix
for i in list_cond_file(subject, run):
neural_prediction = events2neural_fixed(i, TR, n_trs)
convolved = convolve(neural_prediction, hrf_at_trs)
X[:, col] = convolved
col = col + 1
##PCA
Y_demeaned = Y - np.mean(Y, axis=1).reshape([-1, 1])
unscaled_cov = Y_demeaned.dot(Y_demeaned.T)
U, S, V = npl.svd(unscaled_cov)
X[:, 8] = U[:, 0]
X[:, 9:11] = U[:, 6:8]
linear_drift = np.linspace(-1, 1, n_trs)
X[:, 11] = linear_drift
quadratic_drift = linear_drift**2
quadratic_drift -= np.mean(quadratic_drift)
X[:, 12] = quadratic_drift
betas = npl.pinv(X).dot(Y)
betas_vols = np.zeros(vol_shape + (14, ))
betas_vols[mask, :] = betas.T
projections = U.T.dot(Y_demeaned)
projection_vols = np.zeros(data.shape)
projection_vols[mask, :] = projections.T
return X, Y, betas_vols, mask, U, Y_demeaned, mean_data, projection_vols
from convolve import *
import pyfits
filename = "sub600.fits"
psf = pyfits.open(filename)[0].data.copy().T
psf /= psf.sum()
# filename='sub1000.fits'
# img = pyfits.open(filename)[0].data.copy().T
img = psf
im = convolve(img, psf)
pyfits.PrimaryHDU(im.T).writeto("cov_psf_psf.fits", clobber=True)
# TR is 2,5 second
TR = 2.5
tr_times = np.arange(0, 30, TR)
# The number of the voxel is 121
n_vols = 121
all_tr_times = np.arange(121) * TR
for i in list_every_cond('sub001', 'task001_run001'):
neural = events2neural(cond_path('sub001', 'task001_run001', i), TR, n_vols)
convolved = convolve(neural, hrf(tr_times))
plt.plot(all_tr_times, neural)
plt.plot(all_tr_times, convolved)
plt.close()
# There are 8 categories, and 121 features each categories.
X = np.ones((n_vols, 8 + 1))
#There are 8 categories, so the data matrix has 9 colomns.
#The order of the column is house, scrambledpix, cat, shoe, bottle, scissors, chair, face.
# Let the first column starts from index 0
col = 0
# create data matrix
hrf_at_trs = hrf(tr_times)
X = np.ones((n_trs,14))
X_np = np.ones((n_trs,14))
mean_data = np.mean(data,axis=-1)
masked, mask = median_otsu(mean_data,2,1)
Y = data[mask].T
col = 0
pred = 0
#Adding onsets to design matrix
for i in list_cond_file(subject,run):
neural_prediction = events2neural_fixed(i, TR, n_trs)
convolved = convolve(neural_prediction, hrf_at_trs)
X[:,col] = convolved
X_np[:,pred] = neural_prediction
col = col + 1
pred = pred + 1
plt.plot(all_tr_times ,X_np[:,:8])
plt.savefig('../../images/block.png')
plt.close()
plt.plot(all_tr_times ,X_np[:,:8])
plt.plot(all_tr_times, X[:,:8])
plt.savefig('../../images/block_and_hdr.png')
plt.close()
#II. Design
hrf_at_trs = hrf(tr_times)
X = np.ones((n_trs,14))
X_np = np.ones((n_trs,14))
mean_data = np.mean(data,axis=-1)
masked, mask = median_otsu(mean_data,2,1)
Y = data[mask].T
col = 0
pred = 0
#Adding onsets to design matrix
for i in list_cond_file(subject,run):
neural_prediction = events2neural_fixed(i, TR, n_trs)
convolved = convolve(neural_prediction, hrf_at_trs)
X[:,col] = convolved
X_np[:,pred] = neural_prediction
col = col + 1
pred = pred + 1
plt.plot(all_tr_times ,X_np[:,:8])
plt.savefig('block.png')
plt.close()
plt.plot(all_tr_times ,X_np[:,:8])
plt.plot(all_tr_times, X[:,:8])
plt.savefig('block_and_hdr.png')
plt.close()
#II. Design
