#START DOING GLM
for j in range(data.shape[2]):
data_slice = data[:, :, j, :]
mask_slice = mask_data[:, :, j]
data_slice = data_slice.reshape((-1, int(num_TR)))
mask_slice = np.ravel(mask_slice)
data_slice = data_slice[mask_slice == 1]
# all conditions in 1 roof (cond_all)
X = np.ones((n_vols, 15))
X[:, 1] = hrf_matrix_all[:, j] # 1 more
X[:, 2] = np.linspace(-1, 1, num=X.shape[0]) #drift
X[:, 3:9] = fourier_creation(X.shape[0], 3)[:, 1:] # six more
X[:, 9:] = pca_addition
# all conditions seperate (cond1, cond2, cond3)
X_cond = np.ones((n_vols, 17))
X_cond[:, 1] = hrf_matrix_1[:, j] # 1 more
X_cond[:, 2] = hrf_matrix_2[:, j] # 1 more
X_cond[:, 3] = hrf_matrix_3[:, j] # 1 more
X_cond[:, 4] = np.linspace(-1, 1, num=X.shape[0]) #drift # one
X_cond[:, 5:11] = fourier_creation(X.shape[0], 3)[:, 1:] # six more
X_cond[:, 11:] = pca_addition
#START CREATING MODELS
###################
# MODEL 1 #
#START DOING GLM
for j in range(data.shape[2]):
data_slice = data[:,:,j,:]
mask_slice = mask_data[:,:,j]
data_slice = data_slice.reshape((-1, int(num_TR)))
mask_slice = np.ravel(mask_slice)
data_slice = data_slice[mask_slice == 1]
# all conditions in 1 roof (cond_all)
X = np.ones((n_vols,15))
X[:, 1] = hrf_matrix_all[:, j] # 1 more
X[:, 2] = np.linspace(-1, 1, num = X.shape[0]) #drift
X[:, 3:9] = fourier_creation(X.shape[0], 3)[:, 1:] # six more
X[:, 9:] = pca_addition
# all conditions seperate (cond1, cond2, cond3)
X_cond = np.ones((n_vols,17))
X_cond[:, 1] = hrf_matrix_1[:, j] # 1 more
X_cond[:, 2] = hrf_matrix_2[:, j] # 1 more
X_cond[:, 3] = hrf_matrix_3[:, j] # 1 more
X_cond[:, 4] = np.linspace(-1, 1, num = X.shape[0]) #drift # one
X_cond[:, 5:11] = fourier_creation(X.shape[0], 3)[:, 1:] # six more
X_cond[:, 11:] = pca_addition
#START CREATING MODELS
###################
mask_data = make_mask(np.ones(data.shape[:-1]), mask_data, fit = True)
mask_data = mask_data != 0
mask_data = mask_data.astype(int)
#Run per slice in order to correct for time
for j in range(data.shape[2]):
data_slice = data[:, :, j, :]
#Create design matrix
X = np.ones((n_vols, 9))
X[:, 1] = convolve[:, j]
X[:, 2] = np.linspace(-1, 1, num = X.shape[0]) #drift
X[:, 3:] = fourier_creation(n_vols, 3)[:, 1:]
beta, t, df, p = t_stat_mult_regression(data_slice, X)
#take only first coefficient
t = t[1, :]
p = p[1, :]
MRSS, fitted, residuals = glm_diagnostics(beta, X, data_slice)
beta = beta[:, 1]
#insert into the proper slice
beta_final[:, :, j] = beta.reshape(data_slice.shape[:-1])
t_final[:, :, j] = t.reshape(data_slice.shape[:-1])
img = nib.load(smooth_data+ i +"_bold_smoothed.nii")
data = img.get_data().astype(float)
n_vols = data.shape[-1]
convolve = np.loadtxt(hrf_data+i+"_hrf.txt")
residual_final = np.zeros((data.shape))
t_final = np.zeros((data.shape[:-1]))
data_slice = data[:,:,j,:]
X = np.ones((n_vols,7))
X[:,1] = convolve[:,j]
X[:,2]=np.linspace(-1,1,num=X.shape[0]) #drift
X[:,3:]=fourier_creation(X.shape[0],2)[:,1:]
beta,t,df,p = t_stat_mult_regression(data_slice, X)
MRSS, fitted, residuals = glm_diagnostics(beta, X, data_slice)
plt.scatter(fitted[30,40,:],residuals[30,40,:])
plt.title("Subject 001, voxel: [30,40,15]")
plt.xlabel("Fitted Values")
plt.ylabel("Residuals")
plt.savefig(location_of_images+'Fitted_v_Residuals.png')
plt.close()
img = nib.load(smooth_data + i + "_bold_smoothed.nii")
data = img.get_data()
n_vols = data.shape[-1]
convolve = np.loadtxt(hrf_data + i + "_hrf.txt")
residual_final = np.zeros((data.shape))
t_final = np.zeros((data.shape[:-1]))
for j in range(data.shape[2]):
data_slice = data[:, :, j, :]
X = np.ones((n_vols, 6))
X[:, 1] = convolve[:, j]
X[:, 2] = np.linspace(-1, 1, num=X.shape[0]) #drift
X[:, 3:] = fourier_creation(X.shape[0], 3)[:, 1:]
beta, t, df, p = t_stat_mult_regression(data_slice, X)
t = t[1, :]
MRSS, fitted, residuals = glm_diagnostics(beta, X, data_slice)
t_final[:, :, j] = t.reshape(data_slice.shape[:-1])
residual_final[:, :, j, :] = residuals.reshape(data_slice.shape)
np.save("../data/glm/t_stat/" + i + "_tstat.npy", t_final)
np.save("../data/glm/residual/" + i + "_residual.npy", residual_final)
sys.stdout.write("-")
t_final = np.zeros((data.shape[:-1])) data_slice = data[:, :, j, :] #Prepare mask for PCA mask = nib.load(path_to_data + i + '/anatomy/inplane001_brain_mask.nii.gz') mask_data = mask.get_data() mask_data = make_mask(np.ones(data.shape[:-1]), mask_data, fit=True) mask_data = mask_data != 0 mask_data = mask_data.astype(int) #Create design matrix X = np.ones((n_vols, 9)) X[:, 1] = convolve[:, j] X[:, 2] = np.linspace(-1, 1, num=X.shape[0]) #drift X[:, 3:] = fourier_creation(n_vols, 3)[:, 1:] beta, t, df, p = t_stat_mult_regression(data_slice, X) MRSS, fitted, residuals = glm_diagnostics(beta, X, data_slice) ########################### # Fitted VS Residual Plot # ########################### plt.scatter(fitted[30, 40, :], residuals[30, 40, :]) min_max = (np.min(fitted[30, 40, :]), np.max(fitted[30, 40, :])) plt.plot([min_max[0], min_max[1]], [0, 0], color="k") plt.xlim(min_max[0], min_max[1])
#Run GLM Per slice
for j in range(data.shape[2]):
data_slice = data[:, :, j, :]
mask_slice = mask_data[:, :, j]
data_slice = data_slice.reshape((-1, num_TR))
mask_slice = np.ravel(mask_slice)
data_slice = data_slice[mask_slice == 1]
# all conditions in 1 roof (cond_all)
X = np.ones((n_vols, 13))
X[:, 1] = hrf_matrix_all[:, j] # 1 more
X[:, 2] = np.linspace(-1, 1, num=X.shape[0]) #drift # one
X[:, 3:7] = fourier_creation(X.shape[0], 2)[:, 1:] # four more
X[:, 7:] = pca_addition
# all conditions seperate (cond1,cond2,cond3)
X_cond = np.ones((n_vols, 15))
X_cond[:, 1] = hrf_matrix_1[:, j]
X_cond[:, 2] = hrf_matrix_2[:, j]
X_cond[:, 3] = hrf_matrix_3[:, j]
X_cond[:, 4] = np.linspace(-1, 1, num=X.shape[0]) #drift # one
X_cond[:, 5:9] = fourier_creation(X.shape[0], 2)[:, 1:] # four more
X_cond[:, 9:] = pca_addition
#START CREATING MODELS
###################
# MODEL 1 #
#START DOING GLM
for j in range(data.shape[2]):
data_slice = data[:,:,j,:]
mask_slice = mask_data[:,:,j]
data_slice = data_slice.reshape((-1,num_TR))
mask_slice = np.ravel(mask_slice)
data_slice = data_slice[mask_slice==1]
# all conditions in 1 roof (cond_all)
X = np.ones((n_vols,13))
X[:,1] = hrf_matrix_all[:,j] # 1 more
X[:,2] = np.linspace(-1,1,num=X.shape[0]) #drift # one
X[:,3:7] = fourier_creation(X.shape[0],2)[:,1:] # four more
X[:,7:] = pca_addition
# all conditions seperate (cond1,cond2,cond3)
X_cond = np.ones((n_vols,15))
X_cond[:,1] = hrf_matrix_1[:,j] # 1 more
X_cond[:,2] = hrf_matrix_2[:,j] # 1 more
X_cond[:,3] = hrf_matrix_3[:,j] # 1 more
X_cond[:,4] = np.linspace(-1,1,num=X.shape[0]) #drift # one
X_cond[:,5:9] = fourier_creation(X.shape[0],2)[:,1:] # four more
X_cond[:,9:] = pca_addition
#START CREATING MODELS
###################
