Exemplo n.º 1
0
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)
Exemplo n.º 2
0
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)
Exemplo n.º 3
0
### 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
Exemplo n.º 4
0
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')
Exemplo n.º 6
0
# 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