def test_save_vols():
# setup
n_scans = 10
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# create 4D film
film = create_random_image(ndim=4, n_scans=n_scans)
threeD_vols = nibabel.four_to_three(film)
# save vols manually
film_filename = os.path.join(output_dir, "film.nii.gz")
threeD_vols_filenames = [os.path.join(output_dir, "fMETHODS-%06i" % i) for i in range(len(threeD_vols))]
# check saving seperate 3D vols
for stuff in [film, threeD_vols]:
if isinstance(stuff, list):
basenames = [os.path.basename(x) for x in threeD_vols_filenames]
else:
basenames = os.path.basename(film_filename)
for concat in [False, True]:
for bn in [None, basenames]:
saved_vols_filenames = save_vols(stuff, output_dir, ext=".nii.gz", concat=concat, basenames=bn)
if not concat and isinstance(stuff, list):
assert_true(isinstance(saved_vols_filenames, list))
assert_equal(len(saved_vols_filenames), n_scans)
if not bn is None:
assert_equal(os.path.basename(saved_vols_filenames[7]), "fMETHODS-000007.nii.gz")
else:
assert_true(isinstance(saved_vols_filenames, basestring))
assert_true(saved_vols_filenames.endswith(".nii.gz"), msg=saved_vols_filenames)
assert_true(is_4D(check_niimg_4d(saved_vols_filenames)))
def load_vols(niimgs):
"""Loads a nifti image (or a bail of) into a list qof 3D volumes.
Parameters
----------
niimgs: 3 or 4D Niimg-like object
If niimgs is an iterable, checks if data is really 4D. Then,
considering that it is a list of niimg and load them one by one.
If niimg is a string, consider it as a path to Nifti image and
call nibabel.load on it. If it is an object, check if get_data
and get_affine methods are present, raise an Exception otherwise.
Returns
-------
niimgs_: list of nifti image objects
The loaded volumes.
"""
# try loading 4d
try:
niimgs = list(check_niimg_4d(niimgs, return_iterator=True))
except TypeError:
# probably not 4d
niimgs = [check_niimg(niimgs)]
except ValueError:
# probably inconsisten affines
pass
try:
# try loading volumes one-by-one
if isinstance(niimgs, _basestring):
niimgs = [niimgs]
return [check_niimg(niimg, ensure_ndim=3) for niimg in niimgs]
except TypeError:
pass
# collect the loaded volumes into a list
if is_niimg(niimgs):
# should be 3d, squash 4th dimension otherwise
if niimgs.shape[-1] == 1:
return [
nibabel.Nifti1Image(niimgs.get_data()[:, :, :, 0],
niimgs.get_affine())
]
else:
return list(iter_img(niimgs))
else:
niimgs = list(niimgs)
if len(niimgs) == 1:
niimgs = niimgs[0]
return list(iter_img(niimgs))
def load_vols(niimgs):
"""Loads a nifti image (or a bail of) into a list qof 3D volumes.
Parameters
----------
niimgs: 3 or 4D Niimg-like object
If niimgs is an iterable, checks if data is really 4D. Then,
considering that it is a list of niimg and load them one by one.
If niimg is a string, consider it as a path to Nifti image and
call nibabel.load on it. If it is an object, check if get_data
and get_affine methods are present, raise an Exception otherwise.
Returns
-------
niimgs_: list of nifti image objects
The loaded volumes.
"""
# try loading 4d
try:
niimgs = list(check_niimg_4d(niimgs, return_iterator=True))
except TypeError:
# probably not 4d
niimgs = [check_niimg(niimgs)]
except ValueError:
# probably inconsisten affines
pass
try:
# try loading volumes one-by-one
if isinstance(niimgs, _basestring): niimgs = [niimgs]
return [check_niimg(niimg, ensure_ndim=3) for niimg in niimgs]
except TypeError:
pass
# collect the loaded volumes into a list
if is_niimg(niimgs):
# should be 3d, squash 4th dimension otherwise
if niimgs.shape[-1] == 1:
return [nibabel.Nifti1Image(niimgs.get_data()[:, :, :, 0],
niimgs.get_affine())]
else:
return list(iter_img(niimgs))
else:
niimgs = list(niimgs)
if len(niimgs) == 1: niimgs = niimgs[0]
return list(iter_img(niimgs))
def test_save_vols():
# setup
n_scans = 10
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# create 4D film
film = create_random_image(ndim=4, n_scans=n_scans)
threeD_vols = nibabel.four_to_three(film)
# save vols manually
film_filename = os.path.join(output_dir, 'film.nii.gz')
threeD_vols_filenames = [os.path.join(output_dir, 'fMETHODS-%06i' % i)
for i in range(len(threeD_vols))]
# check saving seperate 3D vols
for stuff in [film, threeD_vols]:
if isinstance(stuff, list):
basenames = [os.path.basename(x)
for x in threeD_vols_filenames]
else:
basenames = os.path.basename(film_filename)
for concat in [False, True]:
for bn in [None, basenames]:
saved_vols_filenames = save_vols(stuff,
output_dir,
ext='.nii.gz',
concat=concat,
basenames=bn
)
if not concat and isinstance(stuff, list):
assert_true(isinstance(
saved_vols_filenames, list))
assert_equal(len(saved_vols_filenames),
n_scans)
if not bn is None:
assert_equal(os.path.basename(saved_vols_filenames[7]),
'fMETHODS-000007.nii.gz')
else:
assert_true(isinstance(saved_vols_filenames, basestring))
assert_true(saved_vols_filenames.endswith('.nii.gz'),
msg=saved_vols_filenames)
assert_true(is_4D(check_niimg_4d(
saved_vols_filenames)))
def time_slice_diffs(img):
''' Time-point to time-point differences over volumes and slices
We think of the passed array as an image.
The last dimension is assumed to be time.
Parameters
----------
img: 4D Niimg-like
the input (4D) image
Returns
-------
results : dict
``T`` is the number of time points (``arr.shape[time_axis]``)
``S`` is the number of slices (``arr.shape[slice_axis]``)
``v`` is the shape of a volume (``rollimg(arr, time_axis)[0].shape``)
``d2[t]`` is the volume of squared differences between voxels at
time point ``t`` and time point ``t+1``
`results` has keys:
* 'volume_mean_diff2' : (T-1,) array
array containing the mean (over voxels in volume) of the
squared difference from one time point to the next
* 'slice_mean_diff2' : (T-1, S) array
giving the mean (over voxels in slice) of the squared difference
from one time point to the next, one value per slice, per
timepoint
* 'volume_means' : (T,) array
mean over voxels for each volume ``vol[t] for t in 0:T``
* 'slice_diff2_max_vol' : v[:] array
volume, of same shape as input time point volumes, where each slice
is is the slice from ``d2[t]`` for t in 0:T-1, that has the largest
variance across ``t``. Thus each slice in the volume may well result
from a different difference time point.
* 'diff2_mean_vol`` : v[:] array
volume with the mean of ``d2[t]`` across t for t in 0:T-1.
'''
img = check_niimg_4d(img)
shape = img.shape
T = shape[-1]
S = shape[-2] # presumably the slice axis -- to be reconsidered ?
# loop over time points to save memory
# initialize the results
slice_squared_differences = np.empty((T - 1, S))
vol_mean = np.empty((T,))
diff_mean_vol = np.zeros(shape[:3])
slice_diff_max_vol = np.zeros(shape[:3])
slice_diff_max = np.zeros(S)
arr = img.get_data() # inefficient ??
last_vol = arr[..., 0]
vol_mean[0] = np.nanmean(last_vol)
# loop over scans: increment statistics
for vol_index in range(0, T - 1):
current_vol = arr[..., vol_index + 1] # shape vol_shape
vol_mean[vol_index + 1] = np.nanmean(current_vol)
squared_diff = (current_vol - last_vol) ** 2
mask = np.isfinite(squared_diff)
diff_mean_vol[mask] += squared_diff[mask]
slice_squared_differences[vol_index] = np.nanmean(
np.nanmean(squared_diff, 0), 0)
# check whether we have found a highest-diff slice
larger_diff = slice_squared_differences[vol_index] > slice_diff_max
if any(larger_diff):
slice_diff_max[larger_diff] =\
slice_squared_differences[vol_index][larger_diff]
slice_diff_max_vol[..., larger_diff] =\
squared_diff[..., larger_diff]
last_vol = current_vol
vol_squared_differences = np.nanmean(slice_squared_differences, 1)
diff_mean_vol /= (T - 1)
# Remove remaining Nans
# Nans may legitimally remain in slice_squared_differences
slice_squared_differences[np.isnan(slice_squared_differences)] = 0
# and also in slice_diff_max_vol
slice_diff_max_vol[np.isnan(slice_diff_max_vol)] = 0
# Return the outputs as images
affine = img.get_affine()
diff2_mean_vol = nib.Nifti1Image(diff_mean_vol, affine)
slice_diff2_max_vol = nib.Nifti1Image(slice_diff_max_vol, affine)
return {'volume_mean_diff2': vol_squared_differences,
'slice_mean_diff2': slice_squared_differences,
'volume_means': vol_mean,
'diff2_mean_vol': diff2_mean_vol,
'slice_diff2_max_vol': slice_diff2_max_vol,
'session_length': T}
def time_slice_diffs(img):
''' Time-point to time-point differences over volumes and slices
We think of the passed array as an image.
The last dimension is assumed to be time.
Parameters
----------
img: 4D Niimg-like
the input (4D) image
Returns
-------
results : dict
``T`` is the number of time points (``arr.shape[time_axis]``)
``S`` is the number of slices (``arr.shape[slice_axis]``)
``v`` is the shape of a volume (``rollimg(arr, time_axis)[0].shape``)
``d2[t]`` is the volume of squared differences between voxels at
time point ``t`` and time point ``t+1``
`results` has keys:
* 'volume_mean_diff2' : (T-1,) array
array containing the mean (over voxels in volume) of the
squared difference from one time point to the next
* 'slice_mean_diff2' : (T-1, S) array
giving the mean (over voxels in slice) of the squared difference
from one time point to the next, one value per slice, per
timepoint
* 'volume_means' : (T,) array
mean over voxels for each volume ``vol[t] for t in 0:T``
* 'slice_diff2_max_vol' : v[:] array
volume, of same shape as input time point volumes, where each slice
is is the slice from ``d2[t]`` for t in 0:T-1, that has the largest
variance across ``t``. Thus each slice in the volume may well result
from a different difference time point.
* 'diff2_mean_vol`` : v[:] array
volume with the mean of ``d2[t]`` across t for t in 0:T-1.
'''
img = check_niimg_4d(img)
shape = img.shape
T = shape[-1]
S = shape[-2] # presumably the slice axis -- to be reconsidered ?
# loop over time points to save memory
# initialize the results
slice_squared_differences = np.empty((T - 1, S))
vol_mean = np.empty((T,))
diff_mean_vol = np.zeros(shape[:3])
slice_diff_max_vol = np.zeros(shape[:3])
slice_diff_max = np.zeros(S)
arr = img.get_data() # inefficient ??
last_vol = arr[..., 0]
vol_mean[0] = last_vol.mean()
# loop over scans: increment statistics
for vol_index in range(0, T - 1):
current_vol = arr[..., vol_index + 1] # shape vol_shape
vol_mean[vol_index + 1] = current_vol.mean()
squared_diff = (current_vol - last_vol) ** 2
diff_mean_vol += squared_diff
slice_squared_differences[vol_index] = squared_diff.mean(0).mean(0)
# check whether we have found a highest-diff slice
larger_diff = slice_squared_differences[vol_index] > slice_diff_max
if any(larger_diff):
slice_diff_max[larger_diff] =\
slice_squared_differences[vol_index][larger_diff]
slice_diff_max_vol[..., larger_diff] =\
squared_diff[..., larger_diff]
last_vol = current_vol
vol_squared_differences = slice_squared_differences.mean(1)
diff_mean_vol /= (T - 1)
# Return the outputs as images
affine = img.get_affine()
diff2_mean_vol = nib.Nifti1Image(diff_mean_vol, affine)
slice_diff2_max_vol = nib.Nifti1Image(slice_diff_max_vol, affine)
return {'volume_mean_diff2': vol_squared_differences,
'slice_mean_diff2': slice_squared_differences,
'volume_means': vol_mean,
'diff2_mean_vol': diff2_mean_vol,
'slice_diff2_max_vol': slice_diff2_max_vol,
'session_length': T}