def report_slm_oasis(): # pragma: no cover
n_subjects = 5 # more subjects requires more memory
oasis_dataset = nilearn.datasets.fetch_oasis_vbm(n_subjects=n_subjects)
# Resample the images, since this mask has a different resolution
mask_img = resample_to_img(
nilearn.datasets.fetch_icbm152_brain_gm_mask(),
oasis_dataset.gray_matter_maps[0],
interpolation='nearest',
)
design_matrix = _make_design_matrix_slm_oasis(oasis_dataset, n_subjects)
second_level_model = SecondLevelModel(smoothing_fwhm=2.0,
mask_img=mask_img)
second_level_model.fit(oasis_dataset.gray_matter_maps,
design_matrix=design_matrix)
contrast = [[1, 0, 0], [0, 1, 0]]
report = make_glm_report(
model=second_level_model,
contrasts=contrast,
bg_img=nilearn.datasets.fetch_icbm152_2009()['t1'],
height_control=None,
)
output_filename = 'generated_report_slm_oasis.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
def test_high_level_glm_with_paths():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# Ordinary Least Squares case
model = SecondLevelModel(mask_img=mask)
# asking for contrast before model fit gives error
with pytest.raises(ValueError):
model.compute_contrast([])
# fit model
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
c1 = np.eye(len(model.design_matrix_.columns))[0]
z_image = model.compute_contrast(c1, output_type='z_score')
assert isinstance(z_image, Nifti1Image)
assert_array_equal(z_image.affine, load(mask).affine)
# try with target_shape
target_shape = (10, 10, 10)
target_affine = np.eye(4)
target_affine[0, 3] = 1
model = SecondLevelModel(mask_img=mask,
target_shape=target_shape,
target_affine=target_affine)
z_image = model.fit(Y, design_matrix=X).compute_contrast(c1)
assert_array_equal(z_image.shape, target_shape)
assert_array_equal(z_image.affine, target_affine)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del Y, FUNCFILE, func_img, model
def test_second_level_voxelwise_attribute(attribute):
"""Smoke test for voxelwise attributes for SecondLevelModel."""
shapes = ((7, 8, 9, 1), )
mask, fmri_data, _ = generate_fake_fmri_data_and_design(shapes)
model = SecondLevelModel(mask_img=mask, minimize_memory=False)
Y = fmri_data * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model.fit(Y, design_matrix=X)
model.compute_contrast()
getattr(model, attribute)
def test_second_level_residuals():
"""Tests residuals computation for SecondLevelModel."""
shapes = ((7, 8, 9, 1), )
mask, fmri_data, _ = generate_fake_fmri_data_and_design(shapes)
model = SecondLevelModel(mask_img=mask, minimize_memory=False)
Y = fmri_data * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model.fit(Y, design_matrix=X)
model.compute_contrast()
assert isinstance(model.residuals, Nifti1Image)
assert model.residuals.shape == (7, 8, 9, 4)
mean_residuals = model.masker_.transform(model.residuals).mean(0)
assert_array_almost_equal(mean_residuals, 0)
def test_second_level_voxelwise_attribute_errors(attribute):
"""Tests that an error is raised when trying to access
voxelwise attributes before fitting the model, before
computing a contrast, and when not setting
``minimize_memory`` to ``True``.
"""
shapes = ((7, 8, 9, 1), )
mask, fmri_data, _ = generate_fake_fmri_data_and_design(shapes)
model = SecondLevelModel(mask_img=mask, minimize_memory=False)
with pytest.raises(ValueError, match="The model has no results."):
getattr(model, attribute)
Y = fmri_data * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model.fit(Y, design_matrix=X)
with pytest.raises(ValueError, match="The model has no results."):
getattr(model, attribute)
with pytest.raises(ValueError, match="attribute must be one of"):
model._get_voxelwise_model_attribute("foo", True)
model = SecondLevelModel(mask_img=mask, minimize_memory=True)
model.fit(Y, design_matrix=X)
model.compute_contrast()
with pytest.raises(ValueError, match="To access voxelwise attributes"):
getattr(model, attribute)
def test_slm_reporting():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
model = SecondLevelModel()
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
c1 = np.eye(len(model.design_matrix_.columns))[0]
report_slm = glmr.make_glm_report(model, c1)
# catches & raises UnicodeEncodeError in HTMLDocument.get_iframe()
report_iframe = report_slm.get_iframe()
# So flake8 doesn't complain about not using variable (F841)
report_iframe
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del Y, FUNCFILE, func_img, model
def test_second_level_contrast_computation_with_memory_caching():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# Ordinary Least Squares case
model = SecondLevelModel(mask_img=mask, memory='nilearn_cache')
# fit model
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
ncol = len(model.design_matrix_.columns)
c1 = np.eye(ncol)[0, :]
# test memory caching for compute_contrast
model.compute_contrast(c1, output_type='z_score')
# or simply pass nothing
model.compute_contrast()
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del func_img, FUNCFILE, model, X, Y
def test_second_level_glm_computation():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# Ordinary Least Squares case
model = SecondLevelModel(mask_img=mask)
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
model.compute_contrast()
labels1 = model.labels_
results1 = model.results_
labels2, results2 = run_glm(model.masker_.transform(Y), X.values,
'ols')
assert_almost_equal(labels1, labels2, decimal=1)
assert len(results1) == len(results2)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del func_img, FUNCFILE, model, X, Y
############################################################################
# Estimate second level model
# ---------------------------
# We define the input maps and the design matrix for the second level model
# and fit it.
import pandas as pd
second_level_input = data['cmaps']
design_matrix = pd.DataFrame([1] * len(second_level_input),
columns=['intercept'])
############################################################################
# Model specification and fit.
from nilearn.glm.second_level import SecondLevelModel
second_level_model = SecondLevelModel(smoothing_fwhm=8.0)
second_level_model = second_level_model.fit(second_level_input,
design_matrix=design_matrix)
##########################################################################
# To estimate the :term:`contrast` is very simple. We can just provide the column
# name of the design matrix.
z_map = second_level_model.compute_contrast(output_type='z_score')
###########################################################################
# We threshold the second level contrast at uncorrected p < 0.001 and plot it.
from scipy.stats import norm
p_val = 0.001
p001_unc = norm.isf(p_val)
display = plotting.plot_glass_brain(
z_map, threshold=p001_unc, colorbar=True, display_mode='z', plot_abs=False,
title='group left-right button press (unc p<0.001)')
plotting.show()
def test_second_level_contrast_computation():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# Ordinary Least Squares case
model = SecondLevelModel(mask_img=mask)
# asking for contrast before model fit gives error
with pytest.raises(ValueError):
model.compute_contrast('intercept')
# fit model
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
ncol = len(model.design_matrix_.columns)
c1, cnull = np.eye(ncol)[0, :], np.zeros(ncol)
# smoke test for different contrasts in fixed effects
model.compute_contrast(c1)
z_image = model.compute_contrast(c1, output_type='z_score')
stat_image = model.compute_contrast(c1, output_type='stat')
p_image = model.compute_contrast(c1, output_type='p_value')
effect_image = model.compute_contrast(c1, output_type='effect_size')
variance_image = \
model.compute_contrast(c1, output_type='effect_variance')
# Test output_type='all', and verify images are equivalent
all_images = model.compute_contrast(c1, output_type='all')
assert_array_equal(get_data(all_images['z_score']), get_data(z_image))
assert_array_equal(get_data(all_images['stat']), get_data(stat_image))
assert_array_equal(get_data(all_images['p_value']), get_data(p_image))
assert_array_equal(get_data(all_images['effect_size']),
get_data(effect_image))
assert_array_equal(get_data(all_images['effect_variance']),
get_data(variance_image))
# formula should work (passing variable name directly)
model.compute_contrast('intercept')
# or simply pass nothing
model.compute_contrast()
# passing null contrast should give back a value error
with pytest.raises(ValueError):
model.compute_contrast(cnull)
# passing wrong parameters
with pytest.raises(ValueError):
model.compute_contrast([])
with pytest.raises(ValueError):
model.compute_contrast(c1, None, '')
with pytest.raises(ValueError):
model.compute_contrast(c1, None, [])
with pytest.raises(ValueError):
model.compute_contrast(c1, None, None, '')
# check that passing no explicit contrast when the design
# matrix has more than one columns raises an error
rng = np.random.RandomState(42)
X = pd.DataFrame(rng.uniform(size=(4, 2)), columns=["r1", "r2"])
model = model.fit(Y, design_matrix=X)
with pytest.raises(ValueError):
model.compute_contrast(None)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del func_img, FUNCFILE, model, X, Y
#############################################################################
# Let's plot the design matrix.
from nilearn.plotting import plot_design_matrix
ax = plot_design_matrix(design_matrix)
ax.set_title('Second level design matrix', fontsize=12)
ax.set_ylabel('maps')
##########################################################################
# Next, we specify and fit the second-level model when loading the data and
# also smooth a little bit to improve statistical behavior.
from nilearn.glm.second_level import SecondLevelModel
second_level_model = SecondLevelModel(smoothing_fwhm=2.0, mask_img=mask_img)
second_level_model.fit(gray_matter_map_filenames,
design_matrix=design_matrix)
##########################################################################
# Estimating the contrast is very simple. We can just provide the column
# name of the design matrix.
z_map = second_level_model.compute_contrast(second_level_contrast=[1, 0, 0],
output_type='z_score')
###########################################################################
# We threshold the second level contrast at uncorrected p < 0.001 and plot it.
from nilearn import plotting
from nilearn.glm import threshold_stats_img
_, threshold = threshold_stats_img(
z_map, alpha=.05, height_control='fdr')
print('The FDR=.05-corrected threshold is: %.3g' % threshold)
plotting.show()
#########################################################################
# Second level model estimation
# -----------------------------
# We just have to provide the list of fitted FirstLevelModel objects
# to the SecondLevelModel object for estimation. We can do this because
# all subjects share a similar design matrix (same variables reflected in
# column names).
from nilearn.glm.second_level import SecondLevelModel
second_level_input = models
#########################################################################
# Note that we apply a smoothing of 8mm.
second_level_model = SecondLevelModel(smoothing_fwhm=8.0)
second_level_model = second_level_model.fit(second_level_input)
#########################################################################
# Computing contrasts at the second level is as simple as at the first level.
# Since we are not providing confounders we are performing a one-sample test
# at the second level with the images determined by the specified first level
# contrast.
zmap = second_level_model.compute_contrast(
first_level_contrast='language-string')
#########################################################################
# The group level contrast reveals a left lateralized fronto-temporal
# language network.
plotting.plot_glass_brain(zmap,
colorbar=True,
threshold=p001_unc,
############################################################################
# Estimate second level model
# ---------------------------
# We define the input maps and the design matrix for the second level model
# and fit it.
import pandas as pd
design_matrix = pd.DataFrame(np.hstack((tested_var, np.ones_like(tested_var))),
columns=['fluency', 'intercept'])
###########################################################################
# Fit of the second-level model
from nilearn.glm.second_level import SecondLevelModel
model = SecondLevelModel(smoothing_fwhm=5.0)
model.fit(contrast_map_filenames, design_matrix=design_matrix)
##########################################################################
# To estimate the contrast is very simple. We can just provide the column
# name of the design matrix.
z_map = model.compute_contrast('fluency', output_type='z_score')
###########################################################################
# We compute the fdr-corrected p = 0.05 threshold for these data
from nilearn.glm import threshold_stats_img
_, threshold = threshold_stats_img(z_map, alpha=.05, height_control='fdr')
###########################################################################
# Let us plot the second level contrast at the computed thresholds
from nilearn import plotting