def test_plot_roi_contours():
display = plot_roi(None)
data = np.zeros((91, 109, 91))
x, y, z = -52, 10, 22
x_map, y_map, z_map = coord_transform(x, y, z, np.linalg.inv(mni_affine))
data[int(x_map) - 5:int(x_map) + 5,
int(y_map) - 3:int(y_map) + 3,
int(z_map) - 10:int(z_map) + 10] = 1
img = nibabel.Nifti1Image(data, mni_affine)
plot_roi(img, cmap='RdBu', alpha=0.1, view_type='contours', linewidths=2.)
plt.close()
def test_plotting_functions_with_cmaps():
img = load_mni152_template()
cmaps = ['Paired', 'Set1', 'Set2', 'Set3']
for cmap in cmaps:
plot_roi(img, cmap=cmap, colorbar=True)
plot_stat_map(img, cmap=cmap, colorbar=True)
plot_glass_brain(img, cmap=cmap, colorbar=True)
if LooseVersion(matplotlib.__version__) >= LooseVersion('2.0.0'):
plot_stat_map(img, cmap='viridis', colorbar=True)
plt.close()
def test_plotting_functions_with_cmaps():
img = load_mni152_template()
# some cmaps such as 'viridis' (the new default in 2.0), 'magma', 'plasma',
# and 'inferno' are not supported for older matplotlib version from < 1.5
cmaps = ['Paired', 'Set1', 'Set2', 'Set3']
for cmap in cmaps:
plot_roi(img, cmap=cmap, colorbar=True)
plot_stat_map(img, cmap=cmap, colorbar=True)
plot_glass_brain(img, cmap=cmap, colorbar=True)
if LooseVersion(matplotlib.__version__) >= LooseVersion('2.0.0'):
plot_stat_map(img, cmap='viridis', colorbar=True)
plt.close()
def test_plotting_functions_with_cmaps():
img = load_mni152_template()
# some cmaps such as 'viridis' (the new default in 2.0), 'magma', 'plasma',
# and 'inferno' are not supported for older matplotlib version from < 1.5
cmaps = ['Paired', 'Set1', 'Set2', 'Set3']
for cmap in cmaps:
plot_roi(img, cmap=cmap, colorbar=True)
plot_stat_map(img, cmap=cmap, colorbar=True)
plot_glass_brain(img, cmap=cmap, colorbar=True)
if LooseVersion(matplotlib.__version__) >= LooseVersion('2.0.0'):
plot_stat_map(img, cmap='viridis', colorbar=True)
plt.close()
def test_plotting_functions_with_nans_in_bg_img():
bg_img = _generate_img()
bg_data = bg_img.get_data()
bg_data[6, 5, 1] = np.nan
bg_data[1, 5, 2] = np.nan
bg_data[1, 3, 2] = np.nan
bg_data[6, 5, 2] = np.inf
bg_img = nibabel.Nifti1Image(bg_data, mni_affine)
plot_anat(bg_img)
# test with plot_roi passing background image which contains nans values
# in it
roi_img = _generate_img()
plot_roi(roi_img=roi_img, bg_img=bg_img)
stat_map_img = _generate_img()
plot_stat_map(stat_map_img=stat_map_img, bg_img=bg_img)
plt.close()
def demo_plot_roi(**kwargs):
""" Demo plotting an ROI
"""
mni_affine = MNI152TEMPLATE.get_affine()
data = np.zeros((91, 109, 91))
# Color a asymetric rectangle around Broca area:
x, y, z = -52, 10, 22
x_map, y_map, z_map = coord_transform(x, y, z, np.linalg.inv(mni_affine))
data[int(x_map) - 5 : int(x_map) + 5, int(y_map) - 3 : int(y_map) + 3, int(z_map) - 10 : int(z_map) + 10] = 1
img = nibabel.Nifti1Image(data, mni_affine)
return plot_roi(img, title="Broca's area", **kwargs)
def demo_plot_roi(**kwargs):
""" Demo plotting an ROI
"""
mni_affine = MNI152TEMPLATE.get_affine()
data = np.zeros((91, 109, 91))
# Color a asymetric rectangle around Broca area:
x, y, z = -52, 10, 22
x_map, y_map, z_map = coord_transform(x, y, z, np.linalg.inv(mni_affine))
data[int(x_map) - 5:int(x_map) + 5,
int(y_map) - 3:int(y_map) + 3,
int(z_map) - 10:int(z_map) + 10] = 1
img = nibabel.Nifti1Image(data, mni_affine)
return plot_roi(img, title="Broca's area", **kwargs)
### Visualization #############################################################
import matplotlib.pyplot as plt
# Compute the mean EPI: we do the mean along the axis 3, which is time
mean_haxby = mean_img(haxby_files.func)
plot_epi(mean_haxby)
### Extracting a brain mask ###################################################
# Simple computation of a mask from the fMRI data
from nilearn.masking import compute_epi_mask
mask_img = compute_epi_mask(haxby_files.func[0])
plot_roi(mask_img, mean_haxby)
### Applying the mask #########################################################
from nilearn.masking import apply_mask
masked_data = apply_mask(haxby_files.func[0], mask_img)
# masked_data shape is (timepoints, voxels). We can plot the first 150
# timepoints from two voxels
plt.figure(figsize=(7, 5))
plt.plot(masked_data[:2, :150].T)
plt.xlabel('Time [TRs]', fontsize=16)
plt.ylabel('Intensity', fontsize=16)
plt.xlim(0, 150)
plt.subplots_adjust(bottom=.12, top=.95, right=.95, left=.12)
### Show result ###############################################################
# Unmask data
# Avoid 0 label
labels = ward.labels_ + 1
labels_img = nifti_masker.inverse_transform(labels)
from nilearn.image import mean_img
import matplotlib.pyplot as plt
mean_func_img = mean_img(func_filename)
# common cut coordinates for all plots
first_plot = plot_roi(labels_img,
mean_func_img,
title="Ward parcellation",
display_mode='xz')
# labels_img is a Nifti1Image object, it can be saved to file with the
# following code:
labels_img.to_filename('parcellation.nii')
# Display the original data
plot_epi(nifti_masker.inverse_transform(fmri_masked[0]),
cut_coords=first_plot.cut_coords,
title='Original (%i voxels)' % fmri_masked.shape[1],
display_mode='xz')
# A reduced data can be create by taking the parcel-level average:
# Note that, as many objects in the scikit-learn, the ward object exposes
# a transform method that modifies input features. Here it reduces their
# dimension
# print basic information on the dataset
print('First functional nifti image (4D) is located at: %s' %
miyawaki_dataset.func[0]) # 4D data
miyawaki_filename = miyawaki_dataset.func[0]
miyawaki_mean_img = image.mean_img(miyawaki_filename)
# This time, we can use the NiftiMasker without changing the default mask
# strategy, as the data has already been masked, and thus lies on a
# homogeneous background
masker = NiftiMasker()
masker.fit(miyawaki_filename)
plot_roi(masker.mask_img_, miyawaki_mean_img,
title="Mask from already masked data")
###############################################################################
# From raw EPI data
# Load NYU resting-state dataset
nyu_dataset = datasets.fetch_nyu_rest(n_subjects=1)
nyu_filename = nyu_dataset.func[0]
nyu_img = nibabel.load(nyu_filename)
# Restrict nyu to 100 frames to speed up computation
from nilearn.image import index_img
nyu_img = index_img(nyu_img, slice(0, 100))
# To display the background
print "Ward agglomeration 2000 clusters: %.2fs" % (time.time() - start)
### Show result ###############################################################
# Unmask data
# Avoid 0 label
labels = ward.labels_ + 1
labels_img = nifti_masker.inverse_transform(labels)
from nilearn.image import mean_img
import matplotlib.pyplot as plt
mean_func_img = mean_img(dataset.func[0])
# common cut coordinates for all plots
first_plot = plot_roi(labels_img, mean_func_img, title="Ward parcellation",
display_mode='xz')
# labels_img is a Nifti1Image object, it can be saved to file with the
# following code:
labels_img.to_filename('parcellation.nii')
# Display the original data
plot_epi(nifti_masker.inverse_transform(fmri_masked[0]),
cut_coords=first_plot.cut_coords,
title='Original (%i voxels)' % fmri_masked.shape[1],
display_mode='xz')
# A reduced data can be create by taking the parcel-level average:
# Note that, as many objects in the scikit-learn, the ward object exposes
# a transform method that modifies input features. Here it reduces their
# dimension
import matplotlib.pyplot as plt
# Compute the mean EPI: we do the mean along the axis 3, which is time
func_filename = haxby_dataset.func[0]
mean_haxby = mean_img(func_filename)
plot_epi(mean_haxby)
### Extracting a brain mask ###################################################
# Simple computation of a mask from the fMRI data
from nilearn.masking import compute_epi_mask
mask_img = compute_epi_mask(func_filename)
plot_roi(mask_img, mean_haxby)
### Applying the mask #########################################################
from nilearn.masking import apply_mask
masked_data = apply_mask(func_filename, mask_img)
# masked_data shape is (timepoints, voxels). We can plot the first 150
# timepoints from two voxels
plt.figure(figsize=(7, 5))
plt.plot(masked_data[:2, :150].T)
plt.xlabel('Time [TRs]', fontsize=16)
plt.ylabel('Intensity', fontsize=16)
plt.xlim(0, 150)
plt.subplots_adjust(bottom=.12, top=.95, right=.95, left=.12)