plt.close()
# show the design matrix graphically:
plt.imshow(dct_design_mat, aspect=0.1, cmap="gray", interpolation="nearest")
plt.savefig("../../../data/design_matrix/dct_design_mat.png")
plt.close()
######### we take the mean volume (over time), and do a histogram of the values
mean_vol = np.mean(data, axis=-1)
# mask out the outer-brain noise using mean volumes over time.
in_brain_mask = mean_vol > 8000
# We can use this 3D mask to index into our 4D dataset.
# This selects all the voxel time-courses for voxels within the brain
# (as defined by the mask)
y = linear_modeling.smoothing(data, in_brain_mask)
######### Lastly, do t test on betas:
X = dct_design_mat
np.savetxt("../../../data/design_matrix/full_dct_design_mat.txt", X)
beta, errors, MRSS, df = linear_modeling.beta_est(y, X)
print("The mean MRSS across all voxels in mixed design is " + str(np.mean(MRSS)))
np.savetxt("../../../data/beta/" + f1 + "_betas_hat_full_dct.txt", beta, newline="\r\n")
# Visualizing betas for the middle slice
# First reshape
b_vols = np.zeros(vol_shape + (beta.shape[0],))
b_vols[in_brain_mask, :] = beta.T
# Then plot them on the same plot with uniform scale
design = np.ones((len(convo), 4))
design[:, 1] = start
design[:, 2] = end
design[:, 3] = convo
# reshape data to 2D
vol_shape, n_time = data.shape[:-1], data.shape[-1]
# shape_2d = (n_time, np.product(vol_shape)) # (133, 902629)
# Smoothing raw data set
mean_data = np.mean(data, -1)
plt.figure(2)
plt.hist(np.ravel(mean_data), bins=100)
line = plt.axvline(8000, ls='--', color = 'red')
mask = mean_data > 8000
smooth_data = linear_modeling.smoothing(data, mask)
# Block linear regression
betas_hat, s2, df = linear_modeling.beta_est(smooth_data, design) #(4, 194287)
np.savetxt('../../../data/beta/' + f2 + '_betas_hat_block.txt', betas_hat.T, newline='\r\n')
# Filling back to raw data shape
beta_vols = np.zeros(vol_shape + (betas_hat.shape[0],)) #(91, 109, 91, 4)
beta_vols[mask] = betas_hat.T
# set regions outside mask as missing with np.nan
mean_data[~mask] = np.nan
beta_vols[~mask] = np.nan
# T-test on null hypothesis, assume only input variance of beta3 [0,0,0,1]
t_value, p_value = linear_modeling.t_stat(design, [0,1,1,1], betas_hat, s2, df) #(1, 194287) (1, 194287)
linear_drift = np.linspace(-1, 1, n_trs) design_mat[:, 6] = linear_drift quadratic_drift = linear_drift**2 quadratic_drift -= np.mean(quadratic_drift) design_mat[:, 7] = quadratic_drift ############## we take the mean volume (over time) mean_vol = np.mean(data, axis=-1) # mask out the outer-brain noise using mean volumes over time. in_brain_mask = mean_vol > 8000 # We can use this 3D mask to index into our 4D dataset. # This selects all the voxel time-courses for voxels within the brain # (as defined by the mask) ############## Spatially smoothing the raw data y = linear_modeling.smoothing(data, in_brain_mask) ############## Lastly, do t test on betas: X = design_mat ############## Get RSS from full model _, _, MRSS, df = linear_modeling.beta_est(y, X) RSS = MRSS * df ############## Test beta1 + beta4 + beta5 = 0 (block design) index1 = np.array([0, 3, 4]) X_1 = np.delete(X, index1, axis=1) _, _, MRSS1, df1 = linear_modeling.beta_est(y, X_1) RSS1 = MRSS1 * df1 ############## Test beta2 + beta3 = 0 (event related design)