def test_dtype():
data = np.zeros((9, 9, 9), dtype=np.float64)
data[2:-2, 2:-2, 2:-2] = 10
img = Nifti1Image(data, np.eye(4))
masker = MultiNiftiMasker(dtype='auto')
masker.fit([[img]])
masked_img = masker.transform([[img]])
assert (masked_img[0].dtype == np.float32)
def test_auto_mask():
# This mostly a smoke test
data = np.zeros((9, 9, 9))
data[2:-2, 2:-2, 2:-2] = 10
img = Nifti1Image(data, np.eye(4))
masker = MultiNiftiMasker(mask_args=dict(opening=0))
# Check that if we have not fit the masker we get a intelligible
# error
pytest.raises(ValueError, masker.transform, [[
img,
]])
# Check error return due to bad data format
pytest.raises(ValueError, masker.fit, img)
# Smoke test the fit
masker.fit([[img]])
# Test mask intersection
data2 = np.zeros((9, 9, 9))
data2[1:-3, 1:-3, 1:-3] = 10
img2 = Nifti1Image(data2, np.eye(4))
masker.fit([[img, img2]])
assert_array_equal(get_data(masker.mask_img_), np.logical_or(data, data2))
# Smoke test the transform
masker.transform([[
img,
]])
# It should also work with a 3D image
masker.transform(img)
# check exception when transform() called without prior fit()
masker2 = MultiNiftiMasker(mask_img=img)
with pytest.raises(ValueError, match='has not been fitted. '):
masker2.transform(img2)
# training data starts after the first 12 files
fmri_random_runs_filenames = dataset.func[12:]
stimuli_random_runs_filenames = dataset.label[12:]
##############################################################################
# We can use :func:`nilearn.maskers.MultiNiftiMasker` to load the fMRI
# data, clean and mask it.
import numpy as np
from nilearn.maskers import MultiNiftiMasker
masker = MultiNiftiMasker(mask_img=dataset.mask,
detrend=True,
standardize=True)
masker.fit()
fmri_data = masker.transform(fmri_random_runs_filenames)
# shape of the binary (i.e. black and wihte values) image in pixels
stimulus_shape = (10, 10)
# We load the visual stimuli from csv files
stimuli = []
for stimulus_run in stimuli_random_runs_filenames:
stimuli.append(
np.reshape(np.loadtxt(stimulus_run, dtype=int, delimiter=','),
(-1, ) + stimulus_shape,
order='F'))
##############################################################################
# Let's take a look at some of these binary images:
############################################################################
# Then we prepare and mask the data
# ----------------------------------
import numpy as np
from nilearn.maskers import MultiNiftiMasker
sys.stderr.write("Preprocessing data...")
t0 = time.time()
# Load and mask fMRI data
masker = MultiNiftiMasker(mask_img=miyawaki_dataset.mask,
detrend=True,
standardize=False)
masker.fit()
X_train = masker.transform(X_random_filenames)
X_test = masker.transform(X_figure_filenames)
# We load the visual stimuli from csv files
y_train = []
for y in y_random_filenames:
y_train.append(
np.reshape(np.loadtxt(y, dtype=int, delimiter=','), (-1, ) + y_shape,
order='F'))
y_test = []
for y in y_figure_filenames:
y_test.append(
np.reshape(np.loadtxt(y, dtype=int, delimiter=','), (-1, ) + y_shape,
order='F'))