def test_plot_stat_map_with_nans():
img = _generate_img()
data = img.get_data()
data[6, 5, 1] = np.nan
data[1, 5, 2] = np.nan
data[1, 3, 2] = np.nan
data[6, 5, 2] = np.inf
img = nibabel.Nifti1Image(data, mni_affine)
plot_epi(img)
plot_stat_map(img)
plot_glass_brain(img)
def test_plot_stat_map_with_nans(testdata_3d):
img = testdata_3d['img']
data = get_data(img)
data[6, 5, 1] = np.nan
data[1, 5, 2] = np.nan
data[1, 3, 2] = np.nan
data[6, 5, 2] = np.inf
img = nibabel.Nifti1Image(data, mni_affine)
plot_epi(img)
plot_stat_map(img)
plot_glass_brain(img)
### Fetch data ################################################################ from nilearn import datasets from nilearn.image.image import mean_img from nilearn.plotting.img_plotting import plot_epi, plot_roi haxby_files = datasets.fetch_haxby(n_subjects=1) ### 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
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
fmri_reduced = ward.transform(fmri_masked)
# Display the corresponding data compressed using the parcellation
fmri_compressed = ward.inverse_transform(fmri_reduced)
compressed_img = nifti_masker.inverse_transform(fmri_compressed[0])
plot_epi(compressed_img,
cut_coords=first_plot.cut_coords,
title='Compressed representation (2000 parcels)',
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
fmri_reduced = ward.transform(fmri_masked)
# Display the corresponding data compressed using the parcellation
fmri_compressed = ward.inverse_transform(fmri_reduced)
compressed_img = nifti_masker.inverse_transform(fmri_compressed[0])
plot_epi(compressed_img, cut_coords=first_plot.cut_coords,
title='Compressed representation (2000 parcels)',
display_mode='xz')
# print basic information on the dataset
print('First anatomical nifti image (3D) located is at: %s' %
haxby_dataset.anat[0])
print('First functional nifti image (4D) is located at: %s' %
haxby_dataset.func[0])
### Visualization #############################################################
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
