def test_nifti_maps_masker_report_displayed_maps_errors(
niftimapsmasker_inputs, displayed_maps):
"""Tests that a TypeError is raised when the argument `displayed_maps`
of `generate_report()` is not valid.
"""
masker = NiftiMapsMasker(**niftimapsmasker_inputs)
masker.fit()
with pytest.raises(TypeError, match=("Parameter ``displayed_maps``")):
masker.generate_report(displayed_maps)
def test_nifti_maps_masker_report_maps_number_errors(niftimapsmasker_inputs,
displayed_maps):
"""Tests that a ValueError is raised when the argument `displayed_maps`
contains invalid map numbers.
"""
masker = NiftiMapsMasker(**niftimapsmasker_inputs)
masker.fit()
with pytest.raises(ValueError,
match="Report cannot display the following maps"):
masker.generate_report(displayed_maps)
def test_nifti_maps_masker_report_image_in_fit(niftimapsmasker_inputs):
""""""
masker = NiftiMapsMasker(**niftimapsmasker_inputs)
image, _ = generate_random_img((13, 11, 12), affine=np.eye(4), length=3)
masker.fit(image)
html = masker.generate_report(2)
assert masker._report_content['report_id'] == 0
assert masker._report_content['number_of_maps'] == 9
assert masker._report_content['warning_message'] is None
assert html.body.count("<img") == 2
def test_nifti_maps_masker_report_integer_and_all_displayed_maps(
niftimapsmasker_inputs, displayed_maps):
"""Tests NiftiMapsMasker reporting with no image provided to fit
and displayed_maps provided as an integer or as 'all'.
"""
masker = NiftiMapsMasker(**niftimapsmasker_inputs)
masker.fit()
expected_n_maps = 9 if displayed_maps == 'all' else min(9, displayed_maps)
if displayed_maps != 'all' and displayed_maps > 9:
with pytest.warns(UserWarning, match="masker only has 9 maps."):
html = masker.generate_report(displayed_maps)
else:
html = masker.generate_report(displayed_maps)
assert masker._report_content['report_id'] == 0
assert masker._report_content['number_of_maps'] == 9
assert (masker._report_content['displayed_maps'] == list(
range(expected_n_maps)))
msg = ("No image provided to fit in NiftiMapsMasker. "
"Plotting only spatial maps for reporting.")
assert masker._report_content['warning_message'] == msg
assert html.body.count("<img") == expected_n_maps
def test_nifti_maps_masker_report_list_and_arrays_maps_number(
niftimapsmasker_inputs, displayed_maps):
"""Tests report generation for NiftiMapsMasker with displayed_maps
passed as a list of a Numpy arrays.
"""
masker = NiftiMapsMasker(**niftimapsmasker_inputs)
masker.fit()
html = masker.generate_report(displayed_maps)
assert masker._report_content['report_id'] == 0
assert masker._report_content['number_of_maps'] == 9
assert (masker._report_content['displayed_maps'] == list(displayed_maps))
msg = ("No image provided to fit in NiftiMapsMasker. "
"Plotting only spatial maps for reporting.")
assert masker._report_content['warning_message'] == msg
assert html.body.count("<img") == len(displayed_maps)
# Extract the time series
# ------------------------
from nilearn.input_data import NiftiMapsMasker
masker = NiftiMapsMasker(maps_img=atlas_filename, standardize=True,
memory='nilearn_cache', verbose=5)
masker.fit(data.func[0])
time_series = masker.transform(data.func[0],
confounds=data.confounds)
############################################################################
# We can generate an HTML report and visualize the components of the
# :class:`~nilearn.input_data.NiftiMapsMasker`.
# You can pass the indices of the spatial maps you want to include in the
# report in the order you want them to appear.
# Here, we only include maps 2, 6, 7, 16, and 21 in the report:
report = masker.generate_report(displayed_maps=[2, 6, 7, 16, 21])
report
############################################################################
# `time_series` is now a 2D matrix, of shape (number of time points x
# number of regions)
print(time_series.shape)
############################################################################
# Build and display a correlation matrix
# ---------------------------------------
from nilearn.connectome import ConnectivityMeasure
correlation_measure = ConnectivityMeasure(kind='correlation')
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
# Display the correlation matrix
