def test_overlaid_report(data_img_3d):
pytest.importorskip('matplotlib')
masker = NiftiMasker(target_affine=np.eye(3) * 8)
html = masker.generate_report()
assert "Please `fit` the object" in str(html)
masker.fit(data_img_3d)
html = masker.generate_report()
assert '<div class="overlay">' in str(html)
def test_4d_reports(mask):
# Dummy 4D data
data = np.zeros((10, 10, 10, 3), dtype=int)
data[..., 0] = 1
data[..., 1] = 2
data[..., 2] = 3
data_img_4d = Nifti1Image(data, np.eye(4))
# test .fit method
masker = NiftiMasker(mask_strategy='epi')
masker.fit(data_img_4d)
assert masker._report_content['warning_message'] is None
html = masker.generate_report()
_check_html(html)
# test .fit_transform method
masker = NiftiMasker(mask_img=mask, standardize=True)
masker.fit_transform(data_img_4d)
assert masker._report_content['warning_message'] is None
html = masker.generate_report()
_check_html(html)
def _gen_report():
""" Generate an empty HTMLReport for testing """
data = np.zeros((9, 9, 9))
data[3:-3, 3:-3, 3:-3] = 10
data_img_3d = Nifti1Image(data, np.eye(4))
# turn off reporting
mask = NiftiMasker()
mask.fit(data_img_3d)
report = mask.generate_report()
return report
miyawaki_filename = miyawaki_dataset.func[0]
miyawaki_mean_img = image.mean_img(miyawaki_filename)
plot_epi(miyawaki_mean_img, title='Mean EPI image')
###############################################################################
# A NiftiMasker with the default strategy
masker = NiftiMasker()
masker.fit(miyawaki_filename)
# Plot the generated mask using the mask_img_ attribute
plot_roi(masker.mask_img_,
miyawaki_mean_img,
title="Mask from already masked data")
###############################################################################
# Plot the generated mask using the .generate_report method
report = masker.generate_report()
report
###############################################################################
# Computing a mask from raw EPI data
###############################################################################
#
# From raw EPI data, there is no uniform background, and a different
# strategy is necessary
# Load movie watching based brain development fmri dataset
dataset = datasets.fetch_development_fmri(n_subjects=1)
epi_filename = dataset.func[0]
# Restrict to 100 frames to speed up computation
from nilearn.image import index_img
########################################################################### # Visualize the mask using the plot_roi method from nilearn.plotting import plot_roi from nilearn.image.image import mean_img # calculate mean image for the background mean_func_img = mean_img(func_filename) plot_roi(mask_img, mean_func_img, display_mode='y', cut_coords=4, title="Mask") ########################################################################### # Visualize the mask using the 'generate_report' method # This report can be displayed in a Jupyter Notebook, # opened in-browser using the .open_in_browser() method, # or saved to a file using the .save_as_html(output_filepath) method. report = nifti_masker.generate_report() report ########################################################################### # Preprocess data with the NiftiMasker nifti_masker.fit(func_filename) fmri_masked = nifti_masker.transform(func_filename) # fmri_masked is now a 2D matrix, (n_voxels x n_time_points) ########################################################################### # Run an algorithm from sklearn.decomposition import FastICA n_components = 10 ica = FastICA(n_components=n_components, random_state=42) components_masked = ica.fit_transform(fmri_masked.T).T
the 4D data and compress rearrange them into 2D.
For example, if we have a group of voxels we think
are in the Amygdala, the masker will extract them from
the time series of 3D images (4D series) and arrange them
to be a data matrix with the voxel values as rows and
the columns as time points, for example.'''
mask_filename = haxby_data.mask_vt[0]
plotting.plot_roi(mask_filename, bg_img=haxby_data.anat[0], cmap='Paired')
'''What we have done here is plotted the voxels that we
have applied the mask to agains the backdrop of the
actual brain volume'''
from nilearn.input_data import NiftiMasker
masker = NiftiMasker(mask_img=mask_filename, standardize=True)
fmri_masked = masker.fit_transform(fmri_filename)
masker.generate_report()
print(fmri_masked)
print(fmri_masked.shape)
'''as you can see, the object fmri_masked is 1452x464,
which is the number of time points by the number of voxels.'''
'''Now why don't we do a little bit of visualization.
What does the time series for some random voxels look like?'''
plt.plot(fmri_masked[200:400, 45:52])
plt.title('Time Series for Some Random Masked Voxels')
plt.xlabel('Time')
plt.ylabel('BOLD signal')
plt.tight_layout()
'''Pretty cool, huh? Now we're doing science!'''
'''Now let's circle back around to that behavioral
data. Now that we have arranged the brain data in
a 2D, this will make it play nicer with the behavioral
