def fit(self, X, Y):
"""Fit data X and Y and learn transformation to map X to Y
Parameters
----------
X: Niimg-like object
Source data.
Y: Niimg-like object
Target data
Returns
-------
self
"""
self.masker_ = check_embedded_nifti_masker(self)
self.masker_.n_jobs = self.n_jobs
if self.masker_.mask_img is None:
self.masker_.fit([X])
else:
self.masker_.fit()
if type(self.clustering) == nib.nifti1.Nifti1Image or os.path.isfile(
self.clustering):
# check that clustering provided fills the mask, if not, reduce the mask
if 0 in self.masker_.transform(self.clustering):
reduced_mask = _intersect_clustering_mask(
self.clustering, self.masker_.mask_img)
self.mask = reduced_mask
self.masker_ = check_embedded_nifti_masker(self)
self.masker_.n_jobs = self.n_jobs
self.masker_.fit()
warnings.warn(
"Mask used was bigger than clustering provided. " +
"Its intersection with the clustering was used instead.")
if isinstance(X, (list, np.ndarray)):
X_ = concat_imgs(X)
else:
X_ = load_img(X)
if isinstance(X, (list, np.ndarray)):
Y_ = concat_imgs(Y)
else:
Y_ = load_img(Y)
self.fit_, self.labels_ = [], []
rs = ShuffleSplit(n_splits=self.n_bags, test_size=.8, random_state=0)
outputs = Parallel(
n_jobs=self.n_jobs, prefer="threads", verbose=self.verbose)(
delayed(fit_one_parcellation)
(X_, Y_, self.alignment_method, self.masker_, self.n_pieces,
self.clustering, clustering_index, self.n_jobs, self.verbose)
for clustering_index, _ in rs.split(range(X_.shape[-1])))
# change split
self.labels_ = [output[0] for output in outputs]
self.fit_ = [output[1] for output in outputs]
return self
def average_image_pairs(image_pairs,
image_paths,
rotated_bvecs,
bvals,
confounds_tsvs,
raw_concatenated_files,
verbose=False):
"""Create 4D series of averaged images, gradients, and confounds"""
averaged_images = []
new_bvecs = []
confounds = pd.concat(
[pd.read_csv(fname, delimiter='\t') for fname in confounds_tsvs])
merged_confounds = []
merged_bvals = []
# Load the raw concatenated images for qc
raw_concatenated_img = concat_imgs(raw_concatenated_files)
raw_averaged_images = []
confounds1_rename = {col: col + "_1" for col in confounds.columns}
confounds2_rename = {col: col + "_2" for col in confounds.columns}
for index1, index2 in image_pairs:
confounds1 = confounds.iloc[index1].copy().rename(confounds1_rename)
confounds2 = confounds.iloc[index2].copy().rename(confounds2_rename)
confounds_both = confounds1.append(confounds2)
averaged_images.append(
math_img('(a+b)/2', a=image_paths[index1], b=image_paths[index2]))
raw_averaged_images.append(
math_img('(a[..., %d] + a[..., %d]) / 2' % (index1, index2),
a=raw_concatenated_img))
new_bval = (bvals[index1] + bvals[index2]) / 2.
merged_bvals.append(new_bval)
rotated1 = rotated_bvecs[:, index1]
rotated2 = rotated_bvecs[:, index2]
new_bvec, bvec_error = average_bvec(rotated1, rotated2)
new_bvecs.append(new_bvec)
confounds_both['vec_averaging_error'] = bvec_error
confounds_both['rotated_grad_x_1'] = rotated1[0]
confounds_both['rotated_grad_y_1'] = rotated1[1]
confounds_both['rotated_grad_z_1'] = rotated1[2]
confounds_both['rotated_grad_x_2'] = rotated2[0]
confounds_both['rotated_grad_y_2'] = rotated2[1]
confounds_both['rotated_grad_z_2'] = rotated2[2]
confounds_both['mean_grad_x'] = new_bvec[0]
confounds_both['mean_grad_y'] = new_bvec[1]
confounds_both['mean_grad_z'] = new_bvec[2]
confounds_both['mean_b'] = new_bval
merged_confounds.append(confounds_both)
if verbose:
print('%d: %d [%.4fdeg error]\n\t%d (%.4f %.4f %.4f)' %
(index1, index2, bvec_error, new_bval, new_bvec[0],
new_bvec[1], new_bvec[2]))
averaged_confounds = pd.DataFrame(merged_confounds)
return concat_imgs(averaged_images), concat_imgs(raw_averaged_images), \
np.array(merged_bvals), np.array(new_bvecs), averaged_confounds
def main(args):
# Set analysis directories
root = '/net/synapse/nt/users/bmacintosh_lab/nluciw/'
# Root directory of data.
data_dir = root + 'data/EnF/sourcedata/'
# Output directory.
output_dir = root + 'outputs/perf_covar/' + args.output_dir
# Create output if does not exist.
if not os.path.exists(os.path.dirname(output_dir)):
os.makedirs(os.path.dirname(output_dir))
# Save command line
with open(output_dir + 'commandline_args.txt', 'w') as f:
f.write('\n'.join(sys.argv[1:]))
# Load 4d nifti objects for both groups
bd_data = fetch_data(data_dir,
args.nifti_name,
metadata=args.metadata,
subject_group=('BD', ))
hc_data = fetch_data(data_dir,
args.nifti_name,
metadata=args.metadata,
subject_group=('HC', ))
hc_vols = concat_imgs(hc_data.imgs)
bd_vols = concat_imgs(bd_data.imgs)
# Construct or load parcellator object using the atlas we specify on
# the command line.
parcellator = NiftiLabelsMasker(labels_img=args.atlas,
mask_img=data_dir +
'masks/cbf_80p_aal_merge_mni.nii.gz',
standardize=False,
strategy='mean')
parcellator.fit()
# Do the parcellation and correlation for both groups.
hc_covar, bd_covar =\
covariance.parcellate_and_correlate([hc_vols,bd_vols],
output_dir,
parcellator,
prefix = args.output_prefix,
detrend=False#,
# pve_gm_imgs=[concat_imgs(hc_data.struc_imgs),
# concat_imgs(bd_data.struc_imgs)]
)
print(len(bd_data.imgs), len(hc_data.imgs))
difference = statistics.compute_difference(bd_covar[0], hc_covar[0],
len(bd_data.imgs),
len(hc_data.imgs))
cors = np.stack((bd_covar[0], hc_covar[0], difference))
np.save(output_dir + args.output_prefix + 'cors', cors)
def __init__(self,
echo_times,
MPRAGE_tr=None,
invtimesAB=None,
flipangleABdegree=None,
nZslices=None,
FLASH_tr=None,
sequence='normal',
inversion_efficiency=0.96,
B0=7,
inv1=None,
inv1ph=None,
inv2=None,
inv2ph=None,
B1_fieldmap=None):
if type(inv2) is list:
inv2 = image.concat_imgs(inv2)
if type(inv2ph) is list:
inv2ph = image.concat_imgs(inv2ph)
self.t2starw_echoes = inv2
self.inv2_echo_times = np.array(echo_times)
self.n_echoes = len(echo_times)
if inv2ph is not None:
self.t2starw_echoes_phase = inv2ph
if self.t2starw_echoes.shape[-1] != self.n_echoes:
raise ValueError('Length of echo_times should correspond to the number of echoes'\
'in INV2')
inv2 = image.index_img(self.t2starw_echoes, 0)
inv2ph = image.index_img(self.t2starw_echoes_phase, 0)
self._s0 = None
self._t2starmap = None
self._t2starw = None
super(MEMP2RAGE, self).__init__(MPRAGE_tr=MPRAGE_tr,
invtimesAB=invtimesAB,
flipangleABdegree=flipangleABdegree,
nZslices=nZslices,
FLASH_tr=FLASH_tr,
sequence=sequence,
inversion_efficiency=inversion_efficiency,
B0=B0,
inv1=inv1,
inv1ph=inv1ph,
inv2=inv2,
inv2ph=inv2ph,
B1_fieldmap=B1_fieldmap)
def transform(self, param_type='z_score'):
if not self._fit_status:
raise NotImplementedError(
'LSS not yet fit. Please run .fit() first')
param_maps = []
list_ = []
for model in self.models:
if not isinstance(model.img, str):
raise Exception('{} not a string'.format(model.img))
# ensure that we get event of LSS using a lookup
ev = model.design.columns.get_loc(model._event_of_interest)
param_maps.append(model.extract_params(param_type, contrast_ix=ev))
# get trial info for the map
event = model.events.loc[model.event_index]
list_.append({
'src_img': model.img,
'trial_type': event['trial_type'],
'onset': event['onset']
})
param_index = pd.DataFrame(list_)
return concat_imgs(param_maps), param_index
def create_parcel_atlas(parcel_list):
"""
Create a 3D Nifti1Image atlas parcellation of consecutive integer intensities from an input list of ROI's.
Parameters
----------
parcel_list : list
List of 3D boolean numpy arrays or binarized Nifti1Images corresponding to ROI masks.
Returns
-------
net_parcels_map_nifti : Nifti1Image
A nibabel-based nifti image consisting of a 3D array with integer voxel intensities corresponding to ROI
membership.
parcel_list_exp : list
List of 3D boolean numpy arrays or binarized Nifti1Images corresponding to ROI masks, prepended with a
background image of zeros.
"""
from nilearn.image import new_img_like, concat_imgs
parcel_background = new_img_like(
parcel_list[0], np.zeros(parcel_list[0].shape, dtype=bool))
parcel_list_exp = [parcel_background] + parcel_list
parcellation = concat_imgs(parcel_list_exp).get_fdata()
index_vec = np.array(range(len(parcel_list_exp))) + 1
net_parcels_sum = np.sum(index_vec * parcellation, axis=3)
net_parcels_map_nifti = nib.Nifti1Image(net_parcels_sum,
affine=parcel_list[0].affine)
return net_parcels_map_nifti, parcel_list_exp
def transform(self, param_type='z_score'):
if not self._fit_status:
raise NotImplementedError(
'LSA not yet fit. Please run .fit() first')
param_maps = []
list_ = []
for model in self.models:
# iterate only through trial_types
trial_reg_names = model.events['trial_type'].tolist()
if len(np.unique(trial_reg_names)) != len(trial_reg_names):
raise Exception('Trial regressor names are not unique')
for ev in trial_reg_names:
reg = model.design.columns.get_loc(ev)
param_maps.append(
model.extract_params(param_type, contrast_ix=reg))
trial_type, onset = model.design.columns[reg].split('___')
list_.append({
'src_img': model.img,
'trial_type': trial_type,
'onset': onset
})
param_index = pd.DataFrame(list_)
return concat_imgs(param_maps), param_index
def _check_inputs(self, X, y):
from nibabel.nifti1 import Nifti1Image
from nilearn.image import load_img, concat_imgs
if type(X) == list:
# First, load images
X = [load_img(img) for img in X]
# check that all elements of the list have dimension 3 (should be 3D images)
dims_X = [img.ndim != 3 for img in X]
if np.any(dims_X):
raise print(
"List of images provided, so they all should be 3D")
X = concat_imgs(X)
elif type(X) == Nifti1Image:
if X.ndim != 4:
raise print("One Nifti image, so it should be 4D")
n_obs = X.shape[3]
if n_obs < 3:
print(
"check the results, with observations < 3 the correlations"
" will NaN or always 1")
y = np.asarray(y)
if y.ndim > 1:
raise print("Dependent variable y should be unidimensional")
return X, y
def process_img(stat_img, cluster_extent, voxel_thresh=1.96):
"""
Parameters
----------
stat_img : Niimg_like object
Thresholded statistical map image
cluster_extent : int
Minimum number of voxels required to consider a cluster
voxel_thresh : int, optional
Threshold to apply to `stat_img`. If a negative number is provided a
percentile threshold is used instead, where the percentile is
determined by the equation `100 - voxel_thresh`. Default: 1.96
Returns
-------
cluster_img : Nifti1Image
4D image of brain regions, where each volume is a distinct cluster
"""
# get input data image
stat_img = image.index_img(check_niimg(stat_img, atleast_4d=True), 0)
# threshold image
if voxel_thresh < 0:
voxel_thresh = '{}%'.format(100 + voxel_thresh)
else:
# ensure that threshold is not greater than most extreme value in image
if voxel_thresh > np.abs(stat_img.get_data()).max():
empty = np.zeros(stat_img.shape + (1,))
return image.new_img_like(stat_img, empty)
thresh_img = image.threshold_img(stat_img, threshold=voxel_thresh)
# extract clusters
min_region_size = cluster_extent * np.prod(thresh_img.header.get_zooms())
clusters = []
for sign in ['pos', 'neg']:
# keep only data of given sign
data = thresh_img.get_data().copy()
data[(data < 0) if sign == 'pos' else (data > 0)] = 0
# Do nothing if data array contains only zeros
if np.any(data):
try:
clusters += [connected_regions(
image.new_img_like(thresh_img, data),
min_region_size=min_region_size,
extract_type='connected_components')[0]]
except TypeError: # for no clusters
pass
# Return empty image if no clusters were found
if len(clusters) == 0:
return image.new_img_like(thresh_img, np.zeros(data.shape + (1,)))
# Reorder clusters by their size
clust_img = image.concat_imgs(clusters)
cluster_size = (clust_img.get_data() != 0).sum(axis=(0, 1, 2))
new_order = np.argsort(cluster_size)[::-1]
clust_img_ordered = image.index_img(clust_img, new_order)
return clust_img_ordered
def gen_network_parcels(parlistfile, network, labels, dir_path):
from nilearn.image import new_img_like, concat_imgs
bna_img = nib.load(parlistfile)
bna_data = np.round(bna_img.get_data(),1)
##Get an array of unique parcels
bna_data_for_coords_uniq = np.unique(bna_data)
##Number of parcels:
par_max = len(bna_data_for_coords_uniq) - 1
bna_data = bna_data.astype('int16')
img_stack = []
for idx in range(1, par_max+1):
roi_img = bna_data == bna_data_for_coords_uniq[idx].astype('int16')
roi_img = roi_img.astype('int16')
img_stack.append(roi_img)
img_stack = np.array(img_stack).astype('int16')
img_list = []
for idy in range(par_max):
roi_img_nifti = new_img_like(bna_img, img_stack[idy])
img_list.append(roi_img_nifti)
print('\nExtracting parcels associated with ' + network + ' network locations...\n')
net_parcels = [i for j, i in enumerate(img_list) if j in labels]
bna_4D = concat_imgs(net_parcels).get_data()
index_vec = np.array(range(len(net_parcels))) + 1
net_parcels_sum = np.sum(index_vec * bna_4D, axis=3)
net_parcels_map_nifti = nib.Nifti1Image(net_parcels_sum, affine=np.eye(4))
out_path = dir_path + '/' + network + '_parcels.nii.gz'
nib.save(net_parcels_map_nifti, out_path)
return(out_path)
def spread_labels(labels_img, roi_values=None, background_label=0):
""" Spread each ROI in labels_img into a 4D image.
Parameters
----------
labels_img: 3D Niimg-like object
See http://nilearn.github.io/manipulating_images/input_output.html. Region definitions, as one image of labels.
roi_values: List of int or the values in the labels_img, optional
The values of `labels_img` that you want to use.
If None, will extract all the values except from `background_label`.
background_label: number, optional
Label used in labels_img to represent background.
Return
------
4d_labels_img: 4D Niimg-like object
"""
lbls_img = niimg.load_img(labels_img)
lbls_vol = lbls_img.get_data()
if roi_values is None:
roi_values = np.unique(lbls_vol)
roi_values = roi_values[roi_values != background_label]
atlas_roi_vols = [(lbls_vol == val) * val for val in roi_values]
atlas_roi_imgs = [
niimg.new_img_like(lbls_img, vol) for vol in atlas_roi_vols
]
# atlas_roi_imgs = [niimg.new_img_like(lbls_img, (lbls_vol == val) * val) for val in roi_values]
return niimg.concat_imgs(atlas_roi_imgs)
def concat_3D_imgs(in_files, out_file=None):
""" Use nilearn.image.concat_imgs to concat 3D volumes into one 4D volume.
If `in_files` is a list of 3D volumes the return value is the path to one 4D volume.
Else if `in_files` is a list of 4D volumes the return value is `in_files`.
Returns
-------
out_file: str
The absolute path to the output file.
"""
import nilearn.image as niimg
from nilearn._utils import check_niimg_3d
all_3D = True
for idx, img in enumerate(in_files):
try:
_ = check_niimg_3d(img)
except Exception:
all_3D = False
break
if not all_3D:
#raise AttributeError('Expected all input images to be 3D volumes, but '
# ' at least the {}th is not.'.format(idx))
return in_files
else:
return niimg.concat_imgs(in_files)
def create_parcel_atlas(parcel_list):
"""
Create a 3D Nifti1Image atlas parcellation of consecutive integer intensities from an input list of ROI's.
Parameters
----------
parcel_list : list
List of 3D boolean numpy arrays or binarized Nifti1Images corresponding to ROI masks.
Returns
-------
net_parcels_map_nifti : Nifti1Image
A nibabel-based nifti image consisting of a 3D array with integer voxel intensities corresponding to ROI
membership.
parcel_list_exp : list
List of 3D boolean numpy arrays or binarized Nifti1Images corresponding to ROI masks, prepended with a
background image of zeros.
"""
import gc
from nilearn.image import new_img_like, concat_imgs
parcel_list_exp = [new_img_like(parcel_list[0], np.zeros(parcel_list[0].shape, dtype=bool))] + parcel_list
concatted_parcels = concat_imgs(parcel_list_exp, dtype=np.float32)
parcel_list_exp = np.array(range(len(parcel_list_exp))).astype('float32')
parcel_sum = np.sum(parcel_list_exp * np.asarray(concatted_parcels.dataobj), axis=3, dtype=np.uint16)
net_parcels_map_nifti = nib.Nifti1Image(parcel_sum, affine=parcel_list[0].affine)
del concatted_parcels, parcel_sum, parcel_list
gc.collect()
return net_parcels_map_nifti, parcel_list_exp
def transform(self, seeds_img=None):
seeds_img = seeds_img or self.vt_mask_img
X = self.masker.transform(self.func_img)
self.func_img = self.masker.inverse_transform(X)
# Reordering data
print("Sorting data...")
self.class_labels, self.stim_labels, sorted_idx \
= reorder_haxby_labels(self.stim_labels, self.sessions)
imgs = []
for sess_idx, sess_id in zip(sorted_idx, np.unique(self.sessions)):
for img_idx in sess_idx:
# Convert the relative img_idx (to the session start) into
# absolute stim_idx (over all sessions)
stim_idx = np.nonzero(self.sessions == sess_id)[0][img_idx]
imgs += nibabel.four_to_three(
index_img(self.func_img, stim_idx))
self.func_img = concat_imgs(imgs)
# Average across sessions
print("Averaging data...")
self.func_img, self.img_labels = self.my_cache(average_data)(
grouping=self.grouping,
func_img=self.func_img,
ordered_class_labels=self.class_labels,
stim_labels=self.stim_labels,
sessions=self.sessions)
self.searchlight = RsaSearchlight(mask_img=self.mask_img,
seeds_img=seeds_img,
memory_params=self.memory_params,
radius=self.radius)
self.searchlight.fit()
self.similarity_comparisons, self.similarity_std, self.n_voxels = \
self.searchlight.transform(func_img=self.func_img)
def concat_3D_imgs(in_files, out_file=None):
""" Use nilearn.image.concat_imgs to concat 3D volumes into one 4D volume.
If `in_files` is a list of 3D volumes the return value is the path to one 4D volume.
Else if `in_files` is a list of 4D volumes the return value is `in_files`.
Returns
-------
out_file: str
The absolute path to the output file.
"""
import nilearn.image as niimg
from nilearn._utils import check_niimg_3d
all_3D = True
for idx, img in enumerate(in_files):
try:
_ = check_niimg_3d(img)
except Exception:
all_3D = False
break
if not all_3D:
# raise AttributeError('Expected all input images to be 3D volumes, but '
# ' at least the {}th is not.'.format(idx))
return in_files
else:
return niimg.concat_imgs(in_files)
def _check_vol_to_surf_results(img, mesh):
mni_mask = datasets.load_mni152_brain_mask()
for kind, interpolation, mask_img in itertools.product(
['ball', 'line'], ['linear', 'nearest'], [mni_mask, None]):
proj_1 = vol_to_surf(img,
mesh,
kind=kind,
interpolation=interpolation,
mask_img=mask_img)
assert_true(proj_1.ndim == 1)
img_rot = image.resample_img(img,
target_affine=rotation(
np.pi / 3., np.pi / 4.))
proj_2 = vol_to_surf(img_rot,
mesh,
kind=kind,
interpolation=interpolation,
mask_img=mask_img)
# The projection values for the rotated image should be close
# to the projection for the original image
diff = np.abs(proj_1 - proj_2) / np.abs(proj_1)
assert_true(np.mean(diff[diff < np.inf]) < .03)
img_4d = image.concat_imgs([img, img])
proj_4d = vol_to_surf(img_4d,
mesh,
kind=kind,
interpolation=interpolation,
mask_img=mask_img)
nodes, _ = surface.load_surf_mesh(mesh)
assert_array_equal(proj_4d.shape, [nodes.shape[0], 2])
assert_array_almost_equal(proj_4d[:, 0], proj_1, 3)
def transform(self, X):
"""Predict data from X
Parameters
----------
X: Niimg-like object
Source data
Returns
-------
X_transform: Niimg-like object
Predicted data
"""
if isinstance(X, (list, np.ndarray)):
X = concat_imgs(X)
X_ = self.masker_.transform(X)
X_transform = np.zeros_like(X_)
for i in range(self.n_bags):
X_transform += piecewise_transform(self.labels_[i], self.fit_[i],
X_)
X_transform /= self.n_bags
return self.masker_.inverse_transform(X_transform)
def get_coords_ball(r2):
vox_coords = np.unravel_index(r2.get_fdata().argmax(), r2.shape)
coords = image.coord_transform(*vox_coords, thr_r2.affine)
_, ball =_apply_mask_and_get_affinity([list(coords)], image.concat_imgs([r2]), 3., True)
ball = ball.toarray().reshape(r2.shape)* 100.
ball = image.new_img_like(r2, ball.astype(int))
return coords, ball
def concat_imgs(imgs):
"""
Simple wrapper for concat_imgs
:param imgs: list of nifti images or filenames
:param logger: logfile ID
:return: Nifti1Image
"""
return image.concat_imgs(imgs, dtype=np.float32)
def correlation_searchlight(A,
B,
mask=None,
radius=2,
interpolate=False,
n_jobs=1,
data_dir=None):
"""
Parameters
----------
A : list
list of (even_trials, odd_trials) tuples for the first condition (A);
each tuple represents a subject's mean fMRI data for both trials, and
even/odd_trials should be a 3D niimg or path (string) to a NIfTI file
B : list
list of (even_trials, odd_trials) tuples for the second condition (B);
formatted the same as A
mask : Niimg-like object
boolean image giving location of voxels containing usable signals
radius : int
radius of the searchlight sphere
interpolate : bool
whether or not to skip every other sphere and interpolate the results;
used to speed up the analysis
n_jobs : int
number of CPUs to split the work up between (-1 means "all CPUs")
data_dir : string
path to directory where MVPA results should be stored
Returns
-------
score_map_fpaths : list
list of paths to NIfTI files representing the MVPA scores for each
subject
"""
if not mask:
niimgs = [niimg for trials in A + B for niimg in trials]
mask = compute_epi_mask(mean_img(concat_imgs(niimgs)))
spheres = extract_spheres(get_data(mask), radius, interpolate)
_analyze_subject = partial(analyze_subject,
spheres=spheres,
interpolate=interpolate,
mask=mask,
data_dir=data_dir)
if n_jobs > 1 or n_jobs == -1:
score_map_fpaths = concurrent_exec(
_analyze_subject,
[(i, _A, _B) for i, (_A, _B) in enumerate(zip(A, B))], n_jobs)
else:
score_map_fpaths = []
for subject_id, (_A, _B) in enumerate(zip(A, B)):
score_map_fpath = _analyze_subject(subject_id, _A, _B)
score_map_fpaths.append(score_map_fpath)
return score_map_fpaths
def add_epi_fmaps_to_dwi_b0s(epi_fmaps, b0_threshold, max_per_spec,
dwi_spec_lines, dwi_imain):
"""Add additional images from EPI fieldmaps for distortion correction.
In order to fill out the maximum number of images per distortion group, images
from files in the fmap/ directory can be added to those already extracted from the
DWI series.
Examples:
---------
>>> epi_fmaps = ["/data/sub-1/fmap/sub-1_dir-AP_epi.nii.gz",
... "/data/sub-1/fmap/sub-1_dir-PA_epi.nii.gz"]
"""
# Extract b=0 images as if we were only pulling images from epi fmaps.
fmaps_4d, fmap_b0_indices, fmap_original_files = load_epi_dwi_fieldmaps(
epi_fmaps, b0_threshold)
fmap_spec_lines, fmap_imain, fmap_report, fmap_spec_map = topup_inputs_from_4d_file(
fmaps_4d,
fmap_b0_indices,
fmap_original_files,
image_source="EPI fieldmap",
max_per_spec=max_per_spec)
# Check how many are present in each group from just the dwi files
spec_counts = defaultdict(int)
for dwi_spec in dwi_spec_lines:
spec_counts[dwi_spec] += 1
# Only add as many as you need to fill out max_per_spec
fmap_indices_to_add = []
for image_num, epi_spec in enumerate(fmap_spec_lines):
if spec_counts[epi_spec] + 1 > max_per_spec:
continue
fmap_indices_to_add.append(image_num)
spec_counts[epi_spec] += 1
# No additional epi fmaps to add
if not fmap_indices_to_add:
return dwi_imain, dwi_spec_lines, \
' No Additional images from EPI fieldmaps were added because the maximum ' \
'number of images per distortion group was reached.'
# Add the epi b=0's to the dwi b=0's
topup_imain = concat_imgs(
[dwi_imain, index_img(fmap_imain, fmap_indices_to_add)],
auto_resample=True)
topup_spec_lines = dwi_spec_lines + [
fmap_spec_lines[idx] for idx in fmap_indices_to_add
]
new_report = topup_selection_to_report(fmap_indices_to_add,
fmap_original_files,
fmap_spec_map,
image_source='EPI fieldmap')
return topup_imain, topup_spec_lines, new_report
def test_fmri_inputs_for_non_parametric_inference():
# Test processing of FMRI inputs
with InTemporaryDirectory():
# prepare fake data
p, q = 80, 10
X = np.random.randn(p, q)
shapes = ((7, 8, 9, 10),)
mask, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
T = func_img.shape[-1]
des = pd.DataFrame(np.ones((T, 1)), columns=['a'])
des_fname = 'design.csv'
des.to_csv(des_fname)
# prepare correct input first level models
flm = FirstLevelModel(subject_label='01').fit(FUNCFILE,
design_matrices=des)
# prepare correct input dataframe and lists
shapes = ((7, 8, 9, 1),)
_, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
dfcols = ['subject_label', 'map_name', 'effects_map_path']
dfrows = [['01', 'a', FUNCFILE], ['02', 'a', FUNCFILE],
['03', 'a', FUNCFILE]]
niidf = pd.DataFrame(dfrows, columns=dfcols)
niimgs = [FUNCFILE, FUNCFILE, FUNCFILE]
niimg_4d = concat_imgs(niimgs)
confounds = pd.DataFrame([['01', 1], ['02', 2], ['03', 3]],
columns=['subject_label', 'conf1'])
sdes = pd.DataFrame(X[:3, :3], columns=['intercept', 'b', 'c'])
# test missing second-level contrast
# niimgs as input
assert_raises(ValueError, non_parametric_inference, niimgs, None, sdes)
assert_raises(ValueError, non_parametric_inference, niimgs, confounds,
sdes)
# 4d niimg as input
assert_raises(ValueError, non_parametric_inference, niimg_4d, None,
sdes)
# test wrong input errors
# test first level model
assert_raises(ValueError, non_parametric_inference, flm)
# test list of less than two niimgs
assert_raises(ValueError, non_parametric_inference, [FUNCFILE])
# test dataframe
assert_raises(ValueError, non_parametric_inference, niidf)
# test niimgs requirements
assert_raises(ValueError, non_parametric_inference, niimgs)
assert_raises(ValueError, non_parametric_inference, niimgs + [[]],
confounds)
assert_raises(ValueError, non_parametric_inference, [FUNCFILE])
# test other objects
assert_raises(ValueError, non_parametric_inference,
'random string object')
del X, FUNCFILE, func_img
def sbc_group(in_dir, out_dir):
"""
for each session: load sbc (z transformed) of all subjects and calculate mean & sd
"""
os.makedirs(out_dir, exist_ok=True)
niis = glob(os.path.join(in_dir, "*.nii.gz"))
sessions = list(
set(
map(lambda f: os.path.basename(f).split("_")[-1].split(".")[0],
niis)))
sessions.sort()
print("calc mean sbc for {}".format(sessions))
for ses in sessions:
niis = glob(os.path.join(in_dir, "*{}.nii.gz".format(ses)))
niis.sort()
print(niis)
from nilearn import image, plotting
out_filename_mean_nii = os.path.join(
out_dir, "sbc_1_mean_ses-{}.nii.gz".format(ses))
out_filename_sd_nii = os.path.join(
out_dir, "sbc_1_sd_ses-{}.nii.gz".format(ses))
out_filename_mean_png = os.path.join(
out_dir, "sbc_2_mean_ses-{}.png".format(ses))
out_filename_sd_png = os.path.join(out_dir,
"sbc_2_sd_ses-{}.png".format(ses))
out_filename_list = os.path.join(out_dir,
"sbc_ses-{}_scans.txt".format(ses))
mean_sbc = image.mean_img(niis)
mean_sbc.to_filename(out_filename_mean_nii)
concat_img = image.concat_imgs(niis)
sd_sbc = image.math_img("np.std(img, axis=-1)", img=concat_img)
sd_sbc.to_filename(out_filename_sd_nii)
with open(out_filename_list, "w") as fi:
fi.write("\n".join(niis))
display = plotting.plot_stat_map(
mean_sbc,
threshold=0.5,
cut_coords=(0, -52, 18),
title="mean sbc {} (fisher z)".format(ses))
display.add_markers(marker_coords=[(0, -52, 18)],
marker_color='g',
marker_size=300)
display.savefig(out_filename_mean_png)
display.close()
display = plotting.plot_stat_map(
sd_sbc,
cut_coords=(0, -52, 18),
title="sd sbc {} (fisher z)".format(ses))
display.add_markers(marker_coords=[(0, -52, 18)],
marker_color='g',
marker_size=300)
display.savefig(out_filename_sd_png)
display.close()
def test_fmri_inputs_for_non_parametric_inference():
# Test processing of FMRI inputs
with InTemporaryDirectory():
# prepare fake data
p, q = 80, 10
X = np.random.randn(p, q)
shapes = ((7, 8, 9, 10), )
mask, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
T = func_img.shape[-1]
des = pd.DataFrame(np.ones((T, 1)), columns=['a'])
des_fname = 'design.csv'
des.to_csv(des_fname)
# prepare correct input first level models
flm = FirstLevelModel(subject_label='01').fit(FUNCFILE,
design_matrices=des)
# prepare correct input dataframe and lists
shapes = ((7, 8, 9, 1), )
_, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
dfcols = ['subject_label', 'map_name', 'effects_map_path']
dfrows = [['01', 'a', FUNCFILE], ['02', 'a', FUNCFILE],
['03', 'a', FUNCFILE]]
niidf = pd.DataFrame(dfrows, columns=dfcols)
niimgs = [FUNCFILE, FUNCFILE, FUNCFILE]
niimg_4d = concat_imgs(niimgs)
confounds = pd.DataFrame([['01', 1], ['02', 2], ['03', 3]],
columns=['subject_label', 'conf1'])
sdes = pd.DataFrame(X[:3, :3], columns=['intercept', 'b', 'c'])
# test missing second-level contrast
# niimgs as input
assert_raises(ValueError, non_parametric_inference, niimgs, None, sdes)
assert_raises(ValueError, non_parametric_inference, niimgs, confounds,
sdes)
# 4d niimg as input
assert_raises(ValueError, non_parametric_inference, niimg_4d, None,
sdes)
# test wrong input errors
# test first level model
assert_raises(ValueError, non_parametric_inference, flm)
# test list of less than two niimgs
assert_raises(ValueError, non_parametric_inference, [FUNCFILE])
# test dataframe
assert_raises(ValueError, non_parametric_inference, niidf)
# test niimgs requirements
assert_raises(ValueError, non_parametric_inference, niimgs)
assert_raises(ValueError, non_parametric_inference, niimgs + [[]],
confounds)
assert_raises(ValueError, non_parametric_inference, [FUNCFILE])
# test other objects
assert_raises(ValueError, non_parametric_inference,
'random string object')
del X, FUNCFILE, func_img
def concat_imgs(in_files, out_file=None):
""" Use nilearn.image.concat_imgs to concat images of up to 4 dimensions.
Returns
-------
out_file: str
The absolute path to the output file.
"""
import nilearn.image as niimg
return niimg.concat_imgs(in_files)
def create_parcel_atlas(parcel_list):
from nilearn.image import new_img_like, concat_imgs
parcel_background = new_img_like(parcel_list[0], np.zeros(parcel_list[0].shape, dtype=bool))
parcel_list_exp = [parcel_background] + parcel_list
parcellation = concat_imgs(parcel_list_exp).get_data()
index_vec = np.array(range(len(parcel_list_exp))) + 1
net_parcels_sum = np.sum(index_vec * parcellation, axis=3)
net_parcels_map_nifti = nib.Nifti1Image(net_parcels_sum, affine=parcel_list[0].affine)
return(net_parcels_map_nifti, parcel_list_exp)
def _process_inputs(self):
""" validate and process inputs into useful form.
Returns a list of nilearn maskers and the list of corresponding label
names."""
import nilearn.input_data as nl
import nilearn.image as nli
label_data = nli.concat_imgs(self.inputs.label_files)
maskers = []
# determine form of label files, choose appropriate nilearn masker
if np.amax(label_data.dataobj) > 1: # 3d label file
n_labels = np.amax(label_data.dataobj)
maskers.append(nl.NiftiLabelsMasker(label_data))
else: # 4d labels
n_labels = label_data.shape[3]
if self.inputs.incl_shared_variance: # independent computation
for img in nli.iter_img(label_data):
maskers.append(
nl.NiftiMapsMasker(self._4d(img.dataobj, img.affine))
)
else: # one computation fitting all
maskers.append(nl.NiftiMapsMasker(label_data))
# check label list size
if not np.isclose(int(n_labels), n_labels):
raise ValueError(
"The label files {} contain invalid value {}. Check input.".format(
self.inputs.label_files, n_labels
)
)
if len(self.inputs.class_labels) != n_labels:
raise ValueError(
"The length of class_labels {} does not "
"match the number of regions {} found in "
"label_files {}".format(
self.inputs.class_labels, n_labels, self.inputs.label_files
)
)
if self.inputs.include_global:
global_label_data = label_data.dataobj.sum(axis=3) # sum across all regions
global_label_data = (
np.rint(global_label_data).astype(int).clip(0, 1)
) # binarize
global_label_data = self._4d(global_label_data, label_data.affine)
global_masker = nl.NiftiLabelsMasker(
global_label_data, detrend=self.inputs.detrend
)
maskers.insert(0, global_masker)
self.inputs.class_labels.insert(0, "GlobalSignal")
for masker in maskers:
masker.set_params(detrend=self.inputs.detrend)
return maskers
def concat_imgs(in_files, out_file=None):
""" Use nilearn.image.concat_imgs to concat images of up to 4 dimensions.
Returns
-------
out_file: str
The absolute path to the output file.
"""
import nilearn.image as niimg
return niimg.concat_imgs(in_files)
def _concat_copes(cope_file, output_dir):
"""Concatenate COPE images and save as a file"""
from nilearn.image import concat_imgs
copes = []
for i in cope_file:
copes.append(i)
copes_concat = concat_imgs(copes, auto_resample=True)
copes_concat.to_filename(output_dir)
return output_dir
def join_files(in_files):
import os.path as op
from nilearn.image import concat_imgs
from nipype.utils.filemanip import split_filename
_, base, _ = split_filename(in_files[0])
base = '_'.join(base.split('_')[:-1])
out_file = op.abspath(base + '.nii.gz')
img = concat_imgs(in_files)
img.to_filename(out_file)
return out_file
def subject_data(sub):
sessions = np.zeros(360)
sessions[:90] = 1
sessions[90:180] = 2
sessions[180:270] = 3
sessions[270:] = 4
return image.smooth_img(
image.clean_img(image.concat_imgs(
src.format(sub=sub) for src in NIFTI_SRC),
sessions=sessions), SMOOTHING)
def load_epi_dwi_fieldmaps(fmap_list, b0_threshold):
"""Creates a 4D image of b=0s from a list of input images.
Parameters:
-----------
fmap_list: list
List of paths to epi fieldmap images
b0_threshold: int
Maximum b value for an image to be considered a b=0
Returns:
--------
concatenated_images: spatial image
The b=0 volumes concatenated into a 4D image
b0_indices: list
List of the indices in the concatenated images that contain usable images
original_files: list
List of the original files where each b=0 image came from.
"""
# Add in the rpe data, if it exists
b0_indices = []
original_files = []
image_series = []
for fmap_file in fmap_list:
pth, fname, _ = split_filename(fmap_file)
potential_bval_file = op.join(pth, fname) + ".bval"
starting_index = len(original_files)
fmap_img = load_img(fmap_file)
image_series.append(fmap_img)
num_images = 1 if fmap_img.ndim == 3 else fmap_img.shape[3]
original_files += [fmap_file] * num_images
# Which images are b=0 images?
if op.exists(potential_bval_file):
bvals = np.loadtxt(potential_bval_file)
too_large = np.flatnonzero(bvals > b0_threshold)
too_large_values = bvals[too_large]
if too_large.size:
LOGGER.warning(
"Excluding volumes %s from the %s because b=%s is greater than %d",
str(too_large), fmap_file, str(too_large_values),
b0_threshold)
_b0_indices = np.flatnonzero(bvals < b0_threshold) + starting_index
else:
_b0_indices = np.arange(num_images) + starting_index
b0_indices += _b0_indices.tolist()
concatenated_images = concat_imgs(image_series, auto_resample=True)
return concatenated_images, b0_indices, original_files
def test_generate_report_from_4d(self):
''' if the in_file was 4d, it should be able to produce the same report
anyway (using arbitrary volume) '''
# makeshift 4d in_file
mni_file = MNI_2MM
mni_4d = image.concat_imgs([mni_file, mni_file, mni_file])
mni_4d_file = os.path.join(os.getcwd(), 'mni_4d.nii.gz')
nb.save(mni_4d, mni_4d_file)
_smoke_test_report(
BETRPT(in_file=mni_4d_file, generate_report=True, mask=True),
'testBET4d.html')
def _process_inputs(self):
''' validate and process inputs into useful form.
Returns a list of nilearn maskers and the list of corresponding label
names.'''
import nilearn.input_data as nl
import nilearn.image as nli
label_data = nli.concat_imgs(self.inputs.label_files)
maskers = []
# determine form of label files, choose appropriate nilearn masker
if np.amax(label_data.get_data()) > 1: # 3d label file
n_labels = np.amax(label_data.get_data())
maskers.append(nl.NiftiLabelsMasker(label_data))
else: # 4d labels
n_labels = label_data.get_data().shape[3]
if self.inputs.incl_shared_variance: # independent computation
for img in nli.iter_img(label_data):
maskers.append(
nl.NiftiMapsMasker(
self._4d(img.get_data(), img.affine)))
else: # one computation fitting all
maskers.append(nl.NiftiMapsMasker(label_data))
# check label list size
if not np.isclose(int(n_labels), n_labels):
raise ValueError(
'The label files {} contain invalid value {}. Check input.'
.format(self.inputs.label_files, n_labels))
if len(self.inputs.class_labels) != n_labels:
raise ValueError('The length of class_labels {} does not '
'match the number of regions {} found in '
'label_files {}'.format(self.inputs.class_labels,
n_labels,
self.inputs.label_files))
if self.inputs.include_global:
global_label_data = label_data.get_data().sum(
axis=3) # sum across all regions
global_label_data = np.rint(global_label_data).astype(int).clip(
0, 1) # binarize
global_label_data = self._4d(global_label_data, label_data.affine)
global_masker = nl.NiftiLabelsMasker(
global_label_data, detrend=self.inputs.detrend)
maskers.insert(0, global_masker)
self.inputs.class_labels.insert(0, 'GlobalSignal')
for masker in maskers:
masker.set_params(detrend=self.inputs.detrend)
return maskers
def _run_interface(self, runtime):
self._results['out_file'] = genfname(
self.inputs.in_files[0], suffix='merged')
new_nii = concat_imgs(self.inputs.in_files, dtype=self.inputs.dtype)
if isdefined(self.inputs.header_source):
src_hdr = nb.load(self.inputs.header_source).header
new_nii.header.set_xyzt_units(t=src_hdr.get_xyzt_units()[-1])
new_nii.header.set_zooms(list(new_nii.header.get_zooms()[:3]) +
[src_hdr.get_zooms()[3]])
new_nii.to_filename(self._results['out_file'])
return runtime
def _run_interface(self, runtime):
ext = '.nii.gz' if self.inputs.compress else '.nii'
self._results['out_file'] = fname_presuffix(
self.inputs.in_files[0], suffix='_merged' + ext, newpath=runtime.cwd, use_ext=False)
new_nii = concat_imgs(self.inputs.in_files, dtype=self.inputs.dtype)
if isdefined(self.inputs.header_source):
src_hdr = nb.load(self.inputs.header_source).header
new_nii.header.set_xyzt_units(t=src_hdr.get_xyzt_units()[-1])
new_nii.header.set_zooms(list(new_nii.header.get_zooms()[:3]) +
[src_hdr.get_zooms()[3]])
new_nii.to_filename(self._results['out_file'])
return runtime
def _check_vol_to_surf_results(img, mesh):
mni_mask = datasets.load_mni152_brain_mask()
for kind, interpolation, mask_img in itertools.product(
['ball', 'line'], ['linear', 'nearest'], [mni_mask, None]):
proj_1 = vol_to_surf(
img, mesh, kind=kind, interpolation=interpolation,
mask_img=mask_img)
assert_true(proj_1.ndim == 1)
img_rot = image.resample_img(
img, target_affine=rotation(np.pi / 3., np.pi / 4.))
proj_2 = vol_to_surf(
img_rot, mesh, kind=kind, interpolation=interpolation,
mask_img=mask_img)
# The projection values for the rotated image should be close
# to the projection for the original image
diff = np.abs(proj_1 - proj_2) / np.abs(proj_1)
assert_true(np.mean(diff[diff < np.inf]) < .03)
img_4d = image.concat_imgs([img, img])
proj_4d = vol_to_surf(
img_4d, mesh, kind=kind, interpolation=interpolation,
mask_img=mask_img)
nodes, _ = surface.load_surf_mesh(mesh)
assert_array_equal(proj_4d.shape, [nodes.shape[0], 2])
assert_array_almost_equal(proj_4d[:, 0], proj_1, 3)
def spatclust(img, min_cluster_size, threshold=None, index=None, mask=None):
"""
Spatially clusters `img`
Parameters
----------
img : str or img_like
Image file or object to be clustered
min_cluster_size : int
Minimum cluster size (in voxels)
threshold : float, optional
Whether to threshold `img` before clustering
index : array_like, optional
Whether to extract volumes from `img` for clustering
mask : (S,) array_like, optional
Boolean array for masking resultant data array
Returns
-------
clustered : :obj:`numpy.ndarray`
Boolean array of clustered (and thresholded) `img` data
"""
# we need a 4D image for `niimg.iter_img`, below
img = niimg.copy_img(check_niimg(img, atleast_4d=True))
# temporarily set voxel sizes to 1mm isotropic so that `min_cluster_size`
# represents the minimum number of voxels we want to be in a cluster,
# rather than the minimum size of the desired clusters in mm^3
if not np.all(np.abs(np.diag(img.affine)) == 1):
img.set_sform(np.sign(img.affine))
# grab desired volumes from provided image
if index is not None:
if not isinstance(index, list):
index = [index]
img = niimg.index_img(img, index)
# threshold image
if threshold is not None:
img = niimg.threshold_img(img, float(threshold))
clout = []
for subbrick in niimg.iter_img(img):
# `min_region_size` is not inclusive (as in AFNI's `3dmerge`)
# subtract one voxel to ensure we aren't hitting this thresholding issue
try:
clsts = connected_regions(subbrick,
min_region_size=int(min_cluster_size) - 1,
smoothing_fwhm=None,
extract_type='connected_components')[0]
# if no clusters are detected we get a TypeError; create a blank 4D
# image object as a placeholder instead
except TypeError:
clsts = niimg.new_img_like(subbrick,
np.zeros(subbrick.shape + (1,)))
# if multiple clusters detected, collapse into one volume
clout += [niimg.math_img('np.sum(a, axis=-1)', a=clsts)]
# convert back to data array and make boolean
clustered = utils.load_image(niimg.concat_imgs(clout).get_data()) != 0
# if mask provided, mask output
if mask is not None:
clustered = clustered[mask]
return clustered
zmap_filenames.append('/home/jmuraskin/Projects/CCD/working_v1/seed-to-voxel/%s/%s/%s_%s.nii.gz' % (fc,secondlevel_folder_names[fb],fc,subj))
mask_filename='/home/jmuraskin/Projects/CCD/working_v1/seg_probabilities/grey_matter_mask-20-percent.nii.gz'
from scipy.stats import zscore
#load phenotypic data
phenoFile='/home/jmuraskin/Projects/CCD/Pheno/narsad+vt_new.csv'
pheno=read_csv(phenoFile)
pheno=pheno.set_index('participant')
ages=zscore(pheno.loc[goodsubj]['V1_DEM_001'])
mf=zscore(pheno.loc[goodsubj]['V1_DEM_002'])
motionTest=read_csv('/home/jmuraskin/Projects/CCD/CCD-scripts/analysis/CCD_meanFD.csv')
meanFD=zscore(motionTest[motionTest.FB=='FEEDBACK'][motionTest.Subject_ID.isin(goodsubj)]['train_meanFD'])
imgs=image.concat_imgs(zmap_filenames)
clean_imgs=image.clean_img(imgs,confounds=[ages,mf,meanFD],detrend=False,standardize=True)
from nilearn.decoding import SpaceNetRegressor
decoder = SpaceNetRegressor(mask=mask_filename, penalty="tv-l1",
eps=1e-1, # prefer large alphas
memory="nilearn_cache",n_jobs=30)
decoder.fit(clean_imgs, behavioral_target)
# grab the LR and RL phase encoding rest images from one subject
rs_files1 = glob.glob('/Volumes/TRESOR/neurospin/Volumes/DANILO2/neurospin/population/HCP/S500-1/*/MNINonLinear/Results/rfMRI_REST1_LR/rfMRI_REST1_LR.nii.gz')
for i_file, rs_file in enumerate(rs_files1):
sub_id = int(rs_file.split('/')[10])
print('Doing %i/%i: %s...' % (i_file + 1, len(rs_files1), rs_file))
if len(glob.glob('%i_regs_prec*' % sub_id)) > 0:
print('Skipped...')
continue
# may take a while ! -> unpacks 2 1TB gz archives
# timeit on MBP: 1 loops, best of 1: 2min 13s
if isinstance(rs_file, list):
all_sub_rs_maps = concat_imgs(rs_file)
else:
all_sub_rs_maps = nib.load(rs_file)
cur_shape = all_sub_rs_maps.get_data().shape
size_in_GB = all_sub_rs_maps.get_data().nbytes / 1e9
print('% rs images: %.2f GB' % (cur_shape[-1], size_in_GB))
###############################################################################
# dump network projections
###############################################################################
# retrieve network projections
from nilearn import datasets as ds
smith_pkg = ds.fetch_atlas_smith_2009()
icas_path = smith_pkg['rsn20']
def test_fmri_inputs():
# Test processing of FMRI inputs
with InTemporaryDirectory():
# prepare fake data
p, q = 80, 10
X = np.random.randn(p, q)
shapes = ((7, 8, 9, 10),)
mask, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
T = func_img.shape[-1]
des = pd.DataFrame(np.ones((T, 1)), columns=['a'])
des_fname = 'design.csv'
des.to_csv(des_fname)
# prepare correct input first level models
flm = FirstLevelModel(subject_label='01').fit(FUNCFILE,
design_matrices=des)
flms = [flm, flm, flm]
# prepare correct input dataframe and lists
shapes = ((7, 8, 9, 1),)
_, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
dfcols = ['subject_label', 'map_name', 'effects_map_path']
dfrows = [['01', 'a', FUNCFILE], ['02', 'a', FUNCFILE],
['03', 'a', FUNCFILE]]
niidf = pd.DataFrame(dfrows, columns=dfcols)
niimgs = [FUNCFILE, FUNCFILE, FUNCFILE]
niimg_4d = concat_imgs(niimgs)
confounds = pd.DataFrame([['01', 1], ['02', 2], ['03', 3]],
columns=['subject_label', 'conf1'])
sdes = pd.DataFrame(X[:3, :3], columns=['intercept', 'b', 'c'])
# smoke tests with correct input
# First level models as input
SecondLevelModel(mask_img=mask).fit(flms)
SecondLevelModel().fit(flms)
# Note : the following one creates a singular design matrix
SecondLevelModel().fit(flms, confounds)
SecondLevelModel().fit(flms, None, sdes)
# dataframes as input
SecondLevelModel().fit(niidf)
SecondLevelModel().fit(niidf, confounds)
SecondLevelModel().fit(niidf, confounds, sdes)
SecondLevelModel().fit(niidf, None, sdes)
# niimgs as input
SecondLevelModel().fit(niimgs, None, sdes)
# 4d niimg as input
SecondLevelModel().fit(niimg_4d, None, sdes)
# test wrong input errors
# test first level model requirements
assert_raises(ValueError, SecondLevelModel().fit, flm)
assert_raises(ValueError, SecondLevelModel().fit, [flm])
# test dataframe requirements
assert_raises(ValueError, SecondLevelModel().fit,
niidf['subject_label'])
# test niimgs requirements
assert_raises(ValueError, SecondLevelModel().fit, niimgs)
assert_raises(ValueError, SecondLevelModel().fit, niimgs + [[]],
confounds)
# test first_level_conditions, confounds, and design
assert_raises(ValueError, SecondLevelModel().fit, flms, ['', []])
assert_raises(ValueError, SecondLevelModel().fit, flms, [])
assert_raises(ValueError, SecondLevelModel().fit, flms,
confounds['conf1'])
assert_raises(ValueError, SecondLevelModel().fit, flms,
None, [])
subject_data = fetch_spm_multimodal_fmri()
#########################################################################
# Timing and design matrix parameter specification
tr = 2. # repetition time, in seconds
slice_time_ref = 0. # we will sample the design matrix at the beggining of each acquisition
drift_model = 'Cosine' # We use a discrete cosin transform to model signal drifts.
period_cut = 128. # The cutoff for the drift model is 1/128 Hz.
hrf_model = 'spm + derivative' # The hemodunamic response finction is the SPM canonical one
#########################################################################
# Resample the images.
#
# This is achieved by the concat_imgs function of Nilearn.
from nilearn.image import concat_imgs, resample_img, mean_img
fmri_img = [concat_imgs(subject_data.func1, auto_resample=True),
concat_imgs(subject_data.func2, auto_resample=True)]
affine, shape = fmri_img[0].affine, fmri_img[0].shape
print('Resampling the second image (this takes time)...')
fmri_img[1] = resample_img(fmri_img[1], affine, shape[:3])
#########################################################################
# Create mean image for display
mean_image = mean_img(fmri_img)
#########################################################################
# Make design matrices
import numpy as np
import pandas as pd
from nistats.design_matrix import make_first_level_design_matrix
design_matrices = []
subject_data = fetch_spm_auditory() print(subject_data.func) # print the list of names of functional images ############################################################################### # We can display the first functional image and the subject's anatomy: from nilearn.plotting import plot_stat_map, plot_anat, plot_img, show plot_img(subject_data.func[0]) plot_anat(subject_data.anat) ############################################################################### # Next, we concatenate all the 3D EPI image into a single 4D image, # then we average them in order to create a background # image that will be used to display the activations: from nilearn.image import concat_imgs, mean_img fmri_img = concat_imgs(subject_data.func) mean_img = mean_img(fmri_img) ############################################################################### # Specifying the experimental paradigm # ------------------------------------ # # We must now provide a description of the experiment, that is, define the # timing of the auditory stimulation and rest periods. This is typically # provided in an events.tsv file. The path of this file is # provided in the dataset. import pandas as pd events = pd.read_table(subject_data['events']) print(events) ###############################################################################
