interpolation='nearest',
cmap=pl.cm.gray)
# Third subplot: axial view
pl.subplot(1, 3, 3)
pl.axis('off')
pl.title('Axial')
pl.imshow(np.rot90(mean_img[:, :, 32]),
interpolation='nearest',
cmap=pl.cm.gray)
# Extracting a brain mask ###################################################
# Simple computation of a mask from the fMRI data
from nisl.masking import compute_epi_mask
mask = compute_epi_mask(mean_img)
# We create a new figure
pl.figure()
# A plot the axial view of the mask to compare with the axial
# view of the raw data displayed previously
pl.imshow(np.rot90(mask[:, :, 32]), interpolation='nearest')
# Applying the mask #########################################################
# Applying the mask is just a simple array manipulation
masked_data = fmri_data[mask]
# masked_data is now a voxel x time matrix. We can plot the first 10
# lines: they correspond to time-series of 10 voxels on the side of the
# brain
pl.title('Sagittal')
pl.imshow(np.rot90(mean_img[15, :, :]), interpolation='nearest',
cmap=pl.cm.gray)
# Third subplot: axial view
pl.subplot(1, 3, 3)
pl.axis('off')
pl.title('Axial')
pl.imshow(np.rot90(mean_img[:, :, 32]), interpolation='nearest',
cmap=pl.cm.gray)
# Extracting a brain mask ###################################################
# Simple computation of a mask from the fMRI data
from nisl.masking import compute_epi_mask
mask = compute_epi_mask(mean_img)
# We create a new figure
pl.figure()
# A plot the axial view of the mask to compare with the axial
# view of the raw data displayed previously
pl.imshow(np.rot90(mask[:, :, 32]), interpolation='nearest')
# Applying the mask #########################################################
# Applying the mask is just a simple array manipulation
masked_data = fmri_data[mask]
# masked_data is now a voxel x time matrix. We can plot the first 10
# lines: they correspond to time-series of 10 voxels on the side of the
# brain
interpolation='nearest',
cmap=pl.cm.gray)
# Third subplot: axial view
pl.subplot(1, 3, 3)
pl.axis('off')
pl.title('Axial')
pl.imshow(np.rot90(mean_img[:, :, 32]),
interpolation='nearest',
cmap=pl.cm.gray)
# Extracting a brain mask ###################################################
# Simple computation of a mask from the fMRI data
from nisl.masking import compute_epi_mask
mask_img = compute_epi_mask(nifti_img)
mask_data = mask_img.get_data().astype(bool)
# We create a new figure
pl.figure()
# A plot the axial view of the mask to compare with the axial
# view of the raw data displayed previously
pl.imshow(np.rot90(mask_data[:, :, 32]), interpolation='nearest')
# Applying the mask #########################################################
from nisl.masking import apply_mask
masked_data = apply_mask(nifti_img, mask_img)
# masked_data shape is (instant number, voxel number). We can plot the first 10
# lines: they correspond to timeseries of 10 voxels on the side of the
