def test_ISFC():
curr_dir = os.path.dirname(__file__)
mask_fname = os.path.join(curr_dir, 'mask.nii.gz')
mask = io.load_boolean_mask(mask_fname)
fnames = [
os.path.join(curr_dir, 'subj1.nii.gz'),
os.path.join(curr_dir, 'subj2.nii.gz')
]
masked_images = image.mask_images(io.load_images(fnames), mask)
D = image.MaskedMultiSubjectData.from_masked_images(
masked_images, len(fnames))
assert D.shape == (4, 5, 2), "Loaded data has incorrect shape"
ISFC = brainiak.isfc.isfc(D)
ground_truth = \
[[1, 1, 0, -1],
[1, 1, 0, -1],
[0, 0, 1, 0],
[-1, -1, 0, 1]]
assert np.isclose(ISFC, ground_truth).all(), \
"Calculated ISFC does not match ground truth"
def test_ISFC():
curr_dir = os.path.dirname(__file__)
mask_fname = os.path.join(curr_dir, 'mask.nii.gz')
mask = io.load_boolean_mask(mask_fname)
fnames = [os.path.join(curr_dir, 'subj1.nii.gz'),
os.path.join(curr_dir, 'subj2.nii.gz')]
masked_images = image.mask_images(io.load_images(fnames), mask)
D = image.MaskedMultiSubjectData.from_masked_images(masked_images,
len(fnames))
assert D.shape == (4, 5, 2), "Loaded data has incorrect shape"
(ISFC, p) = brainiak.isfc.isfc(D, return_p=True, num_perm=100,
two_sided=True, random_state=0)
ground_truth = \
[[1, 1, 0, -1],
[1, 1, 0, -1],
[0, 0, 1, 0],
[-1, -1, 0, 1]]
ground_truth_p = 1 - np.abs(ground_truth)
assert np.isclose(ISFC, ground_truth).all(), \
"Calculated ISFC does not match ground truth"
assert np.isclose(p, ground_truth_p).all(), \
"Calculated p values do not match ground truth"
def test_ISFC():
curr_dir = os.path.dirname(__file__)
mask_fname = os.path.join(curr_dir, 'mask.nii.gz')
mask = io.load_boolean_mask(mask_fname)
fnames = [
os.path.join(curr_dir, 'subj1.nii.gz'),
os.path.join(curr_dir, 'subj2.nii.gz')
]
masked_images = image.mask_images(io.load_images(fnames), mask)
D = image.MaskedMultiSubjectData.from_masked_images(
masked_images, len(fnames))
assert D.shape == (4, 5, 2), "Loaded data has incorrect shape"
(ISFC, p) = brainiak.isfc.isfc(D,
return_p=True,
num_perm=100,
two_sided=True,
random_state=0)
ground_truth = \
[[1, 1, 0, -1],
[1, 1, 0, -1],
[0, 0, 1, 0],
[-1, -1, 0, 1]]
ground_truth_p = 1 - np.abs(ground_truth)
assert np.isclose(ISFC, ground_truth).all(), \
"Calculated ISFC does not match ground truth"
assert np.isclose(p, ground_truth_p).all(), \
"Calculated p values do not match ground truth"
def test_load_images_data_shape(
image_paths: Iterable[Path],
expected_image_data_shape: Sequence[int],
expected_n_subjects: int
) -> None:
for i, image in enumerate(io.load_images(image_paths)):
assert image.get_data().shape == (64, 64, 26, 10)
assert i + 1 == expected_n_subjects
def boldfiles_to_arrays(boldfiles, z_score=True):
"""
Load bold data into list of arrays (bc that's what brainiak wants)
"""
# create generator that returns nibabel nifti instances
nibs_gen = load_images(boldfiles)
# get numpy array data from each those instances (might take a while)
bold_arrays_list = [
np.reshape(nib_instance.get_fdata(),
(1327104, 256)) # TODO: don't hard code this
for nib_instance in nibs_gen
]
if z_score:
zscored = []
for bold_array in bold_arrays_list:
zs = stats.zscore(bold_array, axis=1, ddof=1)
zs = np.nan_to_num(zs)
zscored.append(zs)
# TODO: put z-scoring in preprocessing pipeline later
bold_arrays_list = zscored
return bold_arrays_list
def test_ISFC():
curr_dir = os.path.dirname(__file__)
mask_fname = os.path.join(curr_dir, 'mask.nii.gz')
mask = io.load_boolean_mask(mask_fname)
fnames = [os.path.join(curr_dir, 'subj1.nii.gz'),
os.path.join(curr_dir, 'subj2.nii.gz')]
masked_images = image.mask_images(io.load_images(fnames), mask)
D = image.MaskedMultiSubjectData.from_masked_images(masked_images,
len(fnames))
assert D.shape == (4, 5, 2), "Loaded data has incorrect shape"
ISFC = brainiak.isfc.isfc(D)
ground_truth = \
[[1, 1, 0, -1],
[1, 1, 0, -1],
[0, 0, 1, 0],
[-1, -1, 0, 1]]
assert np.isclose(ISFC, ground_truth).all(), \
"Calculated ISFC does not match ground truth"
from brainiak import image, io
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import fcluster, linkage
curr_dir = dirname(abspath("__file__"))
mask_fn = join(curr_dir,'avg152T1_gray_3mm.nii.gz')
func_fns = [join(curr_dir,
'sub-{0:03d}-task-intact1.nii.gz'.format(sub))
for sub in np.arange(1, 6)]
print('Loading data from {0} subjects...'.format(len(func_fns)))
mask_image = io.load_boolean_mask(mask_fn, lambda x: x > 50)
masked_images = image.mask_images(io.load_images(func_fns),
mask_image)
coords = np.where(mask_image)
data = image.MaskedMultiSubjectData.from_masked_images(masked_images,
len(func_fns))
print('Calculating mean ISC on {0} voxels'.format(data.shape[1]))
iscs = isc(data, pairwise=False, summary_statistic='mean')
iscs = np.nan_to_num(iscs)
print('Writing ISC map to file...')
nii_template = nib.load(mask_fn)
isc_vol = np.zeros(nii_template.shape)
isc_vol[coords] = iscs
isc_image = nib.Nifti1Image(isc_vol, nii_template.affine,
nii_template.header)
from brainiak import image, io
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import fcluster, linkage
curr_dir = dirname(abspath("__file__"))
mask_fn = join(curr_dir, 'avg152T1_gray_3mm.nii.gz')
func_fns = [
join(curr_dir, 'sub-{0:03d}-task-intact1.nii.gz'.format(sub))
for sub in np.arange(1, 6)
]
print('Loading data from {0} subjects...'.format(len(func_fns)))
mask_image = io.load_boolean_mask(mask_fn, lambda x: x > 50)
masked_images = image.mask_images(io.load_images(func_fns), mask_image)
coords = np.where(mask_image)
data = image.MaskedMultiSubjectData.from_masked_images(masked_images,
len(func_fns))
print('Calculating mean ISC on {0} voxels'.format(data.shape[1]))
iscs = isc(data, pairwise=False, summary_statistic='mean')
iscs = np.nan_to_num(iscs)
print('Writing ISC map to file...')
nii_template = nib.load(mask_fn)
isc_vol = np.zeros(nii_template.shape)
isc_vol[coords] = iscs
isc_image = nib.Nifti1Image(isc_vol, nii_template.affine, nii_template.header)
nib.save(isc_image, 'example_isc.nii.gz')
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import fcluster, linkage
import sys
import os
curr_dir = os.path.dirname(__file__)
brain_fname = os.path.join(curr_dir,'avg152T1_gray_3mm.nii.gz')
fnames = [os.path.join(curr_dir,
'sub-0' + format(subj, '02') + '-task-intact1.nii.gz') for
subj in np.arange(1, 5)]
print('Loading data from ', len(fnames), ' subjects...')
brain_mask = io.load_boolean_mask(brain_fname, lambda x: x > 50)
masked_images = image.mask_images(io.load_images(fnames), brain_mask)
coords = np.where(brain_mask)
D = image.MaskedMultiSubjectData.from_masked_images(masked_images, len(fnames))
print('Calculating ISC on ', D.shape[0], ' voxels')
ISC = brainiak.isfc.isc(D)
ISC[np.isnan(ISC)] = 0
print('Writing ISC map to file...')
brain_nii = nib.load(brain_fname)
ISC_vol = np.zeros(brain_nii.shape)
ISC_vol[coords] = ISC
ISC_nifti = nib.Nifti1Image(ISC_vol, brain_nii.affine, brain_nii.header)
nib.save(ISC_nifti, 'ISC.nii.gz')
ISC_mask = ISC > 0.2
test_sub = nib.load(test_sub) # Test sub is defined in the SR_utils.py
test_fit = brain_masker.fit(test_sub)
affine_mat = test_sub.affine
dimsize = test_sub.header.get_zooms()
# load in the functional data for the comparison group (which SRM will learn features on)
fnames = {}
images = {}
masked_images = {}
bold_base = {}
n_subjs_base = {}
fnames = get_file_names(data_dir_, which_base.lower())
images = io.load_images(fnames)
masked_images = image.mask_images(images, brain_mask)
# Concatenate all of the masked images across participants
bold_base = image.MaskedMultiSubjectData.from_masked_images(
masked_images, len(fnames))
# Convert nans into zeros
bold_base[np.isnan(bold_base)] = 0
# reshape the data
bold_base = np.transpose(bold_base, [1, 0, 2])
n_subjs_base = np.shape(bold_base)[-1]
print("")
print(f'Base data loaded: {which_base}\t shape: {np.shape(bold_base)}')
import sys
import os
curr_dir = os.path.dirname(__file__)
brain_fname = os.path.join(curr_dir, 'avg152T1_gray_3mm.nii.gz')
fnames = [
os.path.join(curr_dir,
'sub-0' + format(subj, '02') + '-task-intact1.nii.gz')
for subj in np.arange(1, 5)
]
print('Loading data from ', len(fnames), ' subjects...')
brain_mask = io.load_boolean_mask(brain_fname, lambda x: x > 50)
masked_images = image.mask_images(io.load_images(fnames), brain_mask)
coords = np.where(brain_mask)
D = image.MaskedMultiSubjectData.from_masked_images(masked_images, len(fnames))
print('Calculating ISC on ', D.shape[0], ' voxels')
ISC = brainiak.isfc.isc(D)
ISC[np.isnan(ISC)] = 0
print('Writing ISC map to file...')
brain_nii = nib.load(brain_fname)
ISC_vol = np.zeros(brain_nii.shape)
ISC_vol[coords] = ISC
ISC_nifti = nib.Nifti1Image(ISC_vol, brain_nii.affine, brain_nii.header)
nib.save(ISC_nifti, 'ISC.nii.gz')
ISC_mask = ISC > 0.2
