def diagnose(args):
""" Calculate, write results from diagnostic screen
Parameters
----------
args : object
object with attributes:
* filename : str - 4D image filename
* time_axis : str - name or number of time axis in `filename`
* slice_axis : str - name or number of slice axis in `filename`
* out_path : None or str - path to which to write results
* out_fname_label : None or filename - suffix of output results files
* ncomponents : int - number of PCA components to write images for
Returns
-------
res : dict
Results of running :func:`screen` on `filename`
"""
img, time_axis, slice_axis = parse_fname_axes(args.filename,
args.time_axis,
args.slice_axis)
res = screen(img, args.ncomponents, time_axis, slice_axis)
froot, ext, addext = splitext_addext(args.filename)
fpath, fbase = psplit(froot)
fpath = fpath if args.out_path is None else args.out_path
fbase = fbase if args.out_fname_label is None else args.out_fname_label
write_screen_res(res, fpath, fbase, ext + addext)
return res
def diagnose(args):
""" Calculate, write results from diagnostic screen
Parameters
----------
args : object
object with attributes:
* filename : str - 4D image filename
* time_axis : str - name or number of time axis in `filename`
* slice_axis : str - name or number of slice axis in `filename`
* out_path : None or str - path to which to write results
* out_fname_label : None or filename - suffix of output results files
* ncomponents : int - number of PCA components to write images for
Returns
-------
res : dict
Results of running :func:`screen` on `filename`
"""
img, time_axis, slice_axis = parse_fname_axes(args.filename,
args.time_axis,
args.slice_axis)
res = screen(img, args.ncomponents, time_axis, slice_axis)
froot, ext, addext = splitext_addext(args.filename)
fpath, fbase = psplit(froot)
fpath = fpath if args.out_path is None else args.out_path
fbase = fbase if args.out_fname_label is None else args.out_fname_label
write_screen_res(res, fpath, fbase, ext + addext)
return res
def get_dtype(data):
"""
Determines neuroimaging format of `data`
Parameters
----------
data : list-of-str or str or img_like
Data to determine format of
Returns
-------
dtype : {'NIFTI', 'GIFTI', 'OTHER'} str
Format of input data
"""
if isinstance(data, list):
dtypes = np.unique([get_dtype(d) for d in data])
if dtypes.size > 1:
raise ValueError('Provided data detected to have varying formats: '
'{}'.format(dtypes))
return dtypes[0]
elif isinstance(data, str):
dtype = splitext_addext(data)[1]
else: # img_like?
if not hasattr(data, 'valid_exts'):
raise TypeError('Input data format cannot be detected.')
dtype = data.valid_exts[0]
return FORMATS.get(dtype, 'OTHER')
def get_dtype(data):
"""
Determines neuroimaging format of `data`
Parameters
----------
data : :obj:`list` of :obj:`str` or :obj:`str` or img_like
Data to determine format of
Returns
-------
dtype : {'NIFTI', 'OTHER'} str
Format of input data
"""
if isinstance(data, list):
dtypes = np.unique([get_dtype(d) for d in data])
if dtypes.size > 1:
raise ValueError('Provided data detected to have varying formats: '
'{}'.format(dtypes))
return dtypes[0]
elif isinstance(data, str):
dtype = splitext_addext(data)[1]
else: # img_like?
if not hasattr(data, 'valid_exts'):
raise TypeError('Input data format cannot be detected.')
dtype = data.valid_exts[0]
return FORMATS.get(dtype, 'OTHER')
def napari_get_reader(path):
"""A basic implementation of the napari_get_reader hook specification.
Parameters
----------
path : str or list of str
Path to file, or list of paths.
Returns
-------
function or None
If the path is a recognized format, return a function that accepts the
same path or list of paths, and returns a list of layer data tuples.
"""
if isinstance(path, list):
# reader plugins may be handed single path, or a list of paths.
# if it is a list, it is assumed to be an image stack...
# so we are only going to look at the first file.
path = path[0]
froot, ext, addext = splitext_addext(path)
# if we know we cannot read the file, we immediately return None.
if not ext.lower() in all_valid_exts:
return None
# otherwise we return the *function* that can read ``path``.
return reader_function
def filewrite(data, filename, ref_img, gzip=False, copy_header=True,
copy_meta=False):
"""
Writes `data` to `filename` in format of `ref_img`
If `ref_img` dtype is GIFTI, then `data` is assumed to be stacked L/R
hemispheric and will be split and saved as two files
Parameters
----------
data : (S [x T]) array_like
Data to be saved
filename : str
Filepath where data should be saved to
ref_img : str or img_like
Reference image
gzip : bool, optional
Whether to gzip output (if not specified in `filename`). Only applies
if output dtype is NIFTI. Default: False
copy_header : bool, optional
Whether to copy header from `ref_img` to new image. Default: True
copy_meta : bool, optional
Whether to copy meta from `ref_img` to new image. Only applies if
output dtype is GIFTI. Default: False
Returns
-------
name : str
Path of saved image (with added extensions, as appropriate)
"""
# get datatype and reference image for comparison
dtype = get_dtype(ref_img)
if isinstance(ref_img, list):
ref_img = ref_img[0]
# ensure that desired output type (from name) is compatible with `dtype`
root, ext, add = splitext_addext(filename)
if ext != '' and FORMATS[ext] != dtype:
raise ValueError('Cannot write {} data to {} file. Please ensure file'
'formats are compatible'.format(dtype, FORMATS[ext]))
if dtype == 'NIFTI':
out = new_nii_like(ref_img, data, copy_header=copy_header)
name = '{}.{}'.format(root, 'nii.gz' if gzip else 'nii')
out.to_filename(name)
elif dtype == 'GIFTI':
# remove possible hemispheric denotations from root
root = op.join(op.dirname(root), op.basename(root).split('.')[0])
# save hemispheres separately
for n, (hdata, hemi) in enumerate(zip(np.split(data, 2, axis=0),
['L', 'R'])):
out = new_gii_like(ref_img[n], hdata,
copy_header=copy_header,
copy_meta=copy_meta)
name = '{}.{}.func.gii'.format(root, hemi)
out.to_filename(name)
return name
def filewrite(data, filename, ref_img, gzip=False, copy_header=True,
copy_meta=False):
"""
Writes `data` to `filename` in format of `ref_img`
If `ref_img` dtype is GIFTI, then `data` is assumed to be stacked L/R
hemispheric and will be split and saved as two files
Parameters
----------
data : (S [x T]) array_like
Data to be saved
filename : str
Filepath where data should be saved to
ref_img : str or img_like
Reference image
gzip : bool, optional
Whether to gzip output (if not specified in `filename`). Only applies
if output dtype is NIFTI. Default: False
copy_header : bool, optional
Whether to copy header from `ref_img` to new image. Default: True
copy_meta : bool, optional
Whether to copy meta from `ref_img` to new image. Only applies if
output dtype is GIFTI. Default: False
Returns
-------
name : str
Path of saved image (with added extensions, as appropriate)
"""
# get datatype and reference image for comparison
dtype = get_dtype(ref_img)
if isinstance(ref_img, list):
ref_img = ref_img[0]
# ensure that desired output type (from name) is compatible with `dtype`
root, ext, add = splitext_addext(filename)
if ext != '' and FORMATS[ext] != dtype:
raise ValueError('Cannot write {} data to {} file. Please ensure file'
'formats are compatible'.format(dtype, FORMATS[ext]))
if dtype == 'NIFTI':
out = new_nii_like(ref_img, data, copy_header=copy_header)
name = '{}.{}'.format(root, 'nii.gz' if gzip else 'nii')
out.to_filename(name)
elif dtype == 'GIFTI':
# remove possible hemispheric denotations from root
root = op.join(op.dirname(root), op.basename(root).split('.')[0])
# save hemispheres separately
for n, (hdata, hemi) in enumerate(zip(np.split(data, 2, axis=0),
['L', 'R'])):
out = new_gii_like(ref_img, hdata,
copy_header=copy_header,
copy_meta=copy_meta)
name = '{}.{}.func.gii'.format(root, hemi)
out.to_filename(name)
return name
def guessed_image_type(filename):
froot, ext, trailing = splitext_addext(filename, ('.gz', '.bz2'))
lext = ext.lower()
if lext == '.img' and os.path.isfile(os.path.join(froot,'.hdr')):
return nibabel_guess(filename)
else:
return WashUImage
def tsdiffana(args):
""" Generate tsdiffana plots from command line params `args`
Parameters
----------
args : object
object with attributes
* filename : str - 4D image filename
* out_file : str - graphics file to write to instead of leaving
graphics on screen
* time_axis : str - name or number of time axis in `filename`
* slice_axis : str - name or number of slice axis in `filename`
* write_results : bool - if True, write images and plots to files
* out_path : None or str - path to which to write results
* out_fname_label : None or filename - suffix of output results files
Returns
-------
axes : Matplotlib axes
Axes on which we have done the plots.
"""
if args.out_file is not None and args.write_results:
raise ValueError("Cannot have OUT_FILE and WRITE_RESULTS options "
"together")
img, time_axis, slice_axis = parse_fname_axes(args.filename,
args.time_axis,
args.slice_axis)
results = time_slice_diffs_image(img, time_axis, slice_axis)
axes = plot_tsdiffs(results)
if args.out_file is None and not args.write_results:
# interactive mode
return axes
if args.out_file is not None:
# plot only mode
axes[0].figure.savefig(args.out_file)
return axes
# plot and images mode
froot, ext, addext = splitext_addext(args.filename)
fpath, fbase = psplit(froot)
fpath = fpath if args.out_path is None else args.out_path
fbase = fbase if args.out_fname_label is None else args.out_fname_label
axes[0].figure.savefig(pjoin(fpath, 'tsdiff_' + fbase + '.png'))
# Save image volumes
for key, prefix in (('slice_diff2_max_vol', 'dv2_max_'), ('diff2_mean_vol',
'dv2_mean_')):
fname = pjoin(fpath, prefix + fbase + ext + addext)
nipy.save_image(results[key], fname)
# Save time courses into npz
np.savez(
pjoin(fpath, 'tsdiff_' + fbase + '.npz'),
volume_means=results['volume_means'],
slice_mean_diff2=results['slice_mean_diff2'],
)
return axes
def tsdiffana(args):
""" Generate tsdiffana plots from command line params `args`
Parameters
----------
args : object
object with attributes
* filename : str - 4D image filename
* out_file : str - graphics file to write to instead of leaving
graphics on screen
* time_axis : str - name or number of time axis in `filename`
* slice_axis : str - name or number of slice axis in `filename`
* write_results : bool - if True, write images and plots to files
* out_path : None or str - path to which to write results
* out_fname_label : None or filename - suffix of output results files
Returns
-------
axes : Matplotlib axes
Axes on which we have done the plots.
"""
if args.out_file is not None and args.write_results:
raise ValueError("Cannot have OUT_FILE and WRITE_RESULTS options "
"together")
img, time_axis, slice_axis = parse_fname_axes(args.filename,
args.time_axis,
args.slice_axis)
results = time_slice_diffs_image(img, time_axis, slice_axis)
axes = plot_tsdiffs(results)
if args.out_file is None and not args.write_results:
# interactive mode
return axes
if args.out_file is not None:
# plot only mode
axes[0].figure.savefig(args.out_file)
return axes
# plot and images mode
froot, ext, addext = splitext_addext(args.filename)
fpath, fbase = psplit(froot)
fpath = fpath if args.out_path is None else args.out_path
fbase = fbase if args.out_fname_label is None else args.out_fname_label
axes[0].figure.savefig(pjoin(fpath, 'tsdiff_' + fbase + '.png'))
# Save image volumes
for key, prefix in (('slice_diff2_max_vol', 'dv2_max_'),
('diff2_mean_vol', 'dv2_mean_')):
fname = pjoin(fpath, prefix + fbase + ext + addext)
nipy.save_image(results[key], fname)
# Save time courses into npz
np.savez(pjoin(fpath, 'tsdiff_' + fbase + '.npz'),
volume_means=results['volume_means'],
slice_mean_diff2=results['slice_mean_diff2'],
)
return axes
def _list_outputs(self):
outputs = self._outputs().get()
filename = splitext_addext(os.path.basename(self.inputs.in_files[0]))[0]
out_dir = os.path.abspath('TED.{}'.format(filename))
outputs['t2star_map'] = os.path.join(out_dir, 't2sv.nii')
outputs['s0_map'] = os.path.join(out_dir, 's0v.nii')
outputs['t2star_adaptive_map'] = os.path.join(out_dir, 't2svG.nii')
outputs['s0_adaptive_map'] = os.path.join(out_dir, 's0vG.nii')
outputs['optimal_comb'] = os.path.join(out_dir, 'ts_OC.nii')
return outputs
def _list_outputs(self):
outputs = self._outputs().get()
filename = splitext_addext(os.path.basename(
self.inputs.in_files[0]))[0]
out_dir = os.path.abspath('TED.{}'.format(filename))
outputs['t2star_map'] = os.path.join(out_dir, 't2sv.nii')
outputs['s0_map'] = os.path.join(out_dir, 's0v.nii')
outputs['t2star_adaptive_map'] = os.path.join(out_dir, 't2svG.nii')
outputs['s0_adaptive_map'] = os.path.join(out_dir, 's0vG.nii')
outputs['optimal_comb'] = os.path.join(out_dir, 'ts_OC.nii')
return outputs
def filewrite(data, filename, ref_img, gzip=False, copy_header=True):
"""
Writes `data` to `filename` in format of `ref_img`
Parameters
----------
data : (S [x T]) array_like
Data to be saved
filename : :obj:`str`
Filepath where data should be saved to
ref_img : :obj:`str` or img_like
Reference image
gzip : :obj:`bool`, optional
Whether to gzip output (if not specified in `filename`). Only applies
if output dtype is NIFTI. Default: False
copy_header : :obj:`bool`, optional
Whether to copy header from `ref_img` to new image. Default: True
Returns
-------
name : :obj:`str`
Path of saved image (with added extensions, as appropriate)
"""
# get datatype and reference image for comparison
dtype = get_dtype(ref_img)
if isinstance(ref_img, list):
ref_img = ref_img[0]
# ensure that desired output type (from name) is compatible with `dtype`
root, ext, add = splitext_addext(filename)
if ext != '' and FORMATS[ext] != dtype:
raise ValueError('Cannot write {} data to {} file. Please ensure file'
'formats are compatible'.format(dtype, FORMATS[ext]))
if dtype == 'NIFTI':
out = new_nii_like(ref_img, data, copy_header=copy_header)
name = '{}.{}'.format(root, 'nii.gz' if gzip else 'nii')
out.to_filename(name)
return name
def filewrite(data, filename, ref_img, gzip=True, copy_header=True):
"""
Writes `data` to `filename` in format of `ref_img`
Parameters
----------
data : (S [x T]) array_like
Data to be saved
filename : :obj:`str`
Filepath where data should be saved to
ref_img : :obj:`str` or img_like
Reference image
gzip : :obj:`bool`, optional
Whether to gzip output (if not specified in `filename`). Only applies
if output dtype is NIFTI. Default: True
copy_header : :obj:`bool`, optional
Whether to copy header from `ref_img` to new image. Default: True
Returns
-------
name : :obj:`str`
Path of saved image (with added extensions, as appropriate)
"""
# get reference image for comparison
if isinstance(ref_img, list):
ref_img = ref_img[0]
# generate out file for saving
out = new_nii_like(ref_img, data, copy_header=copy_header)
# FIXME: we only handle writing to nifti right now
# get root of desired output file and save as nifti image
root = op.dirname(filename)
base = op.basename(filename)
base, ext, add = splitext_addext(base)
root = op.join(root, base)
name = '{}.{}'.format(root, 'nii.gz' if gzip else 'nii')
out.to_filename(name)
return name
def proc_file(infile, opts):
# figure out the output filename, and see if it exists
basefilename = splitext_addext(os.path.basename(infile))[0]
if opts.outdir is not None:
# set output path
basefilename = os.path.join(opts.outdir, basefilename)
# prep a file
if opts.compressed:
verbose('Using gzip compression')
outfilename = basefilename + '.nii.gz'
else:
outfilename = basefilename + '.nii'
if os.path.isfile(outfilename) and not opts.overwrite:
raise IOError('Output file "%s" exists, use --overwrite to '
'overwrite it' % outfilename)
# load the PAR header and data
scaling = 'dv' if opts.scaling == 'off' else opts.scaling
infile = fname_ext_ul_case(infile)
pr_img = pr.load(infile,
permit_truncated=opts.permit_truncated,
scaling=scaling,
strict_sort=opts.strict_sort)
pr_hdr = pr_img.header
affine = pr_hdr.get_affine(origin=opts.origin)
slope, intercept = pr_hdr.get_data_scaling(scaling)
if opts.scaling != 'off':
verbose('Using data scaling "%s"' % opts.scaling)
# get original scaling, and decide if we scale in-place or not
if opts.scaling == 'off':
slope = np.array([1.])
intercept = np.array([0.])
in_data = pr_img.dataobj.get_unscaled()
out_dtype = pr_hdr.get_data_dtype()
elif not np.any(np.diff(slope)) and not np.any(np.diff(intercept)):
# Single scalefactor case
slope = slope.ravel()[0]
intercept = intercept.ravel()[0]
in_data = pr_img.dataobj.get_unscaled()
out_dtype = pr_hdr.get_data_dtype()
else:
# Multi scalefactor case
slope = np.array([1.])
intercept = np.array([0.])
in_data = np.array(pr_img.dataobj)
out_dtype = np.float64
# Reorient data block to LAS+ if necessary
ornt = io_orientation(np.diag([-1, 1, 1, 1]).dot(affine))
if np.all(ornt == [[0, 1], [1, 1], [2, 1]]): # already in LAS+
t_aff = np.eye(4)
else: # Not in LAS+
t_aff = inv_ornt_aff(ornt, pr_img.shape)
affine = np.dot(affine, t_aff)
in_data = apply_orientation(in_data, ornt)
bvals, bvecs = pr_hdr.get_bvals_bvecs()
if not opts.keep_trace: # discard Philips DTI trace if present
if bvecs is not None:
bad_mask = np.logical_and(bvals != 0, (bvecs == 0).all(axis=1))
if bad_mask.sum() > 0:
pl = 's' if bad_mask.sum() != 1 else ''
verbose('Removing %s DTI trace volume%s' %
(bad_mask.sum(), pl))
good_mask = ~bad_mask
in_data = in_data[..., good_mask]
bvals = bvals[good_mask]
bvecs = bvecs[good_mask]
# Make corresponding NIfTI image
nimg = nifti1.Nifti1Image(in_data, affine, pr_hdr)
nhdr = nimg.header
nhdr.set_data_dtype(out_dtype)
nhdr.set_slope_inter(slope, intercept)
nhdr.set_sform(affine, code=1)
nhdr.set_qform(affine, code=1)
if 'parse' in opts.minmax:
# need to get the scaled data
verbose('Loading (and scaling) the data to determine value range')
if opts.minmax[0] == 'parse':
nhdr['cal_min'] = in_data.min() * slope + intercept
else:
nhdr['cal_min'] = float(opts.minmax[0])
if opts.minmax[1] == 'parse':
nhdr['cal_max'] = in_data.max() * slope + intercept
else:
nhdr['cal_max'] = float(opts.minmax[1])
# container for potential NIfTI1 header extensions
if opts.store_header:
# dump the full PAR header content into an extension
with open(infile, 'rb') as fobj: # contents must be bytes
hdr_dump = fobj.read()
dump_ext = nifti1.Nifti1Extension('comment', hdr_dump)
nhdr.extensions.append(dump_ext)
verbose('Writing %s' % outfilename)
nibabel.save(nimg, outfilename)
# write out bvals/bvecs if requested
if opts.bvs:
if bvals is None and bvecs is None:
verbose('No DTI volumes detected, bvals and bvecs not written')
elif bvecs is None:
verbose('DTI volumes detected, but no diffusion direction info was'
'found. Writing .bvals file only.')
with open(basefilename + '.bvals', 'w') as fid:
# np.savetxt could do this, but it's just a loop anyway
for val in bvals:
fid.write('%s ' % val)
fid.write('\n')
else:
verbose('Writing .bvals and .bvecs files')
# Transform bvecs with reorientation affine
orig2new = npl.inv(t_aff)
bv_reorient = from_matvec(to_matvec(orig2new)[0], [0, 0, 0])
bvecs = apply_affine(bv_reorient, bvecs)
with open(basefilename + '.bvals', 'w') as fid:
# np.savetxt could do this, but it's just a loop anyway
for val in bvals:
fid.write('%s ' % val)
fid.write('\n')
with open(basefilename + '.bvecs', 'w') as fid:
for row in bvecs.T:
for val in row:
fid.write('%s ' % val)
fid.write('\n')
# export data labels varying along the 4th dimensions if requested
if opts.vol_info:
labels = pr_img.header.get_volume_labels()
if len(labels) > 0:
vol_keys = list(labels.keys())
with open(basefilename + '.ordering.csv', 'w') as csvfile:
csvwriter = csv.writer(csvfile, delimiter=',')
csvwriter.writerow(vol_keys)
for vals in zip(*[labels[k] for k in vol_keys]):
csvwriter.writerow(vals)
# write out dwell time if requested
if opts.dwell_time:
try:
dwell_time = calculate_dwell_time(pr_hdr.get_water_fat_shift(),
pr_hdr.get_echo_train_length(),
opts.field_strength)
except MRIError:
verbose('No EPI factors, dwell time not written')
else:
verbose('Writing dwell time (%r sec) calculated assuming %sT '
'magnet' % (dwell_time, opts.field_strength))
with open(basefilename + '.dwell_time', 'w') as fid:
fid.write('%r\n' % dwell_time)
def space_time_realign(
input, tr, slice_order="descending", slice_dim=2, slice_dir=1, apply=True, make_figure=False, out_name=None
):
"""
This is a scripting interface to `nipy.algorithms.registration.SpaceTimeRealign`
Parameters
----------
input : str or list
A full path to a file-name (4D nifti time-series) , or to a directory
containing 4D nifti time-series, or a list of full-paths to files.
tr : float
The repetition time
slice_order : str (optional)
This is the order of slice-times in the acquisition. This is used as a
key into the ``SLICETIME_FUNCTIONS`` dictionary from
:mod:`nipy.algorithms.slicetiming.timefuncs`. Default: 'descending'.
slice_dim : int (optional)
Denotes the axis in `images` that is the slice axis. In a 4D image,
this will often be axis = 2 (default).
slice_dir : int (optional)
1 if the slices were acquired slice 0 first (default), slice -1 last,
or -1 if acquire slice -1 first, slice 0 last.
apply : bool (optional)
Whether to apply the transformation and produce an output. Default:
True.
make_figure : bool (optional)
Whether to generate a .png figure with the parameters across scans.
out_name : bool (optional)
Specify an output location (full path) for the files that are
generated. Default: generate files in the path of the inputs (with an
`_mc` suffix added to the file-names.
Returns
-------
transforms : ndarray
An (n_times_points,) shaped array containing
`nipy.algorithms.registration.affine.Rigid` class instances for each time
point in the time-series. These can be used as affine transforms by
referring to their `.as_affine` attribute.
"""
if make_figure:
if not HAVE_MPL:
e_s = "You need to have matplotlib installed to run this function"
e_s += " with `make_figure` set to `True`"
raise RuntimeError(e_s)
# If we got only a single file, we motion correct that one:
if op.isfile(input):
if not (input.endswith(".nii") or input.endswith(".nii.gz")):
e_s = "Input needs to be a nifti file ('.nii' or '.nii.gz'"
raise ValueError(e_s)
fnames = [input]
input = nib.load(input)
# If this is a full-path to a directory containing files, it's still a
# string:
elif isinstance(input, str):
list_of_files = os.listdir(input)
fnames = [op.join(input, f) for f in np.sort(list_of_files) if (f.endswith(".nii") or f.endswith(".nii.gz"))]
input = [nib.load(x) for x in fnames]
# Assume that it's a list of full-paths to files:
else:
input = [nib.load(x) for x in input]
slice_times = timefuncs[slice_order]
slice_info = [slice_dim, slice_dir]
reggy = SpaceTimeRealign(input, tr, slice_times, slice_info)
reggy.estimate(align_runs=True)
# We now have the transformation parameters in here:
transforms = np.squeeze(np.array(reggy._transforms))
rot = np.array([t.rotation for t in transforms])
trans = np.array([t.translation for t in transforms])
if apply:
new_reggy = reggy.resample(align_runs=True)
for run_idx, new_im in enumerate(new_reggy):
# Fix output TR - it was probably lost in the image realign step
assert new_im.affine.shape == (5, 5)
new_im.affine[:] = new_im.affine.dot(np.diag([1, 1, 1, tr, 1]))
# Save it out to a '.nii.gz' file:
froot, ext, trail_ext = splitext_addext(fnames[run_idx])
path, fname = op.split(froot)
# We retain the file-name adding '_mc' regardless of where it's
# saved
new_path = path if out_name is None else out_name
save_image(new_im, op.join(new_path, fname + "_mc.nii.gz"))
if make_figure:
# Delay MPL plotting import to latest moment to avoid errors trying
# import the default MPL backend (such as tkinter, which may not be
# installed). See: https://github.com/nipy/nipy/issues/414
import matplotlib.pyplot as plt
figure, ax = plt.subplots(2)
figure.set_size_inches([8, 6])
ax[0].plot(rot)
ax[0].set_xlabel("Time (TR)")
ax[0].set_ylabel("Translation (mm)")
ax[1].plot(trans)
ax[1].set_xlabel("Time (TR)")
ax[1].set_ylabel("Rotation (radians)")
figure.savefig(op.join(os.path.split(fnames[0])[0], "mc_params.png"))
return transforms
def proc_file(infile, opts):
# figure out the output filename, and see if it exists
basefilename = splitext_addext(os.path.basename(infile))[0]
if opts.outdir is not None:
# set output path
basefilename = os.path.join(opts.outdir, basefilename)
# prep a file
if opts.compressed:
verbose("Using gzip compression")
outfilename = basefilename + ".nii.gz"
else:
outfilename = basefilename + ".nii"
if os.path.isfile(outfilename) and not opts.overwrite:
raise IOError('Output file "%s" exists, use --overwrite to ' "overwrite it" % outfilename)
# load the PAR header and data
scaling = "dv" if opts.scaling == "off" else opts.scaling
infile = fname_ext_ul_case(infile)
pr_img = pr.load(infile, permit_truncated=opts.permit_truncated, scaling=scaling, strict_sort=opts.strict_sort)
pr_hdr = pr_img.header
affine = pr_hdr.get_affine(origin=opts.origin)
slope, intercept = pr_hdr.get_data_scaling(scaling)
if opts.scaling != "off":
verbose('Using data scaling "%s"' % opts.scaling)
# get original scaling, and decide if we scale in-place or not
if opts.scaling == "off":
slope = np.array([1.0])
intercept = np.array([0.0])
in_data = pr_img.dataobj.get_unscaled()
out_dtype = pr_hdr.get_data_dtype()
elif not np.any(np.diff(slope)) and not np.any(np.diff(intercept)):
# Single scalefactor case
slope = slope.ravel()[0]
intercept = intercept.ravel()[0]
in_data = pr_img.dataobj.get_unscaled()
out_dtype = pr_hdr.get_data_dtype()
else:
# Multi scalefactor case
slope = np.array([1.0])
intercept = np.array([0.0])
in_data = np.array(pr_img.dataobj)
out_dtype = np.float64
# Reorient data block to LAS+ if necessary
ornt = io_orientation(np.diag([-1, 1, 1, 1]).dot(affine))
if np.all(ornt == [[0, 1], [1, 1], [2, 1]]): # already in LAS+
t_aff = np.eye(4)
else: # Not in LAS+
t_aff = inv_ornt_aff(ornt, pr_img.shape)
affine = np.dot(affine, t_aff)
in_data = apply_orientation(in_data, ornt)
bvals, bvecs = pr_hdr.get_bvals_bvecs()
if not opts.keep_trace: # discard Philips DTI trace if present
if bvecs is not None:
bad_mask = np.logical_and(bvals != 0, (bvecs == 0).all(axis=1))
if bad_mask.sum() > 0:
pl = "s" if bad_mask.sum() != 1 else ""
verbose("Removing %s DTI trace volume%s" % (bad_mask.sum(), pl))
good_mask = ~bad_mask
in_data = in_data[..., good_mask]
bvals = bvals[good_mask]
bvecs = bvecs[good_mask]
# Make corresponding NIfTI image
nimg = nifti1.Nifti1Image(in_data, affine, pr_hdr)
nhdr = nimg.header
nhdr.set_data_dtype(out_dtype)
nhdr.set_slope_inter(slope, intercept)
nhdr.set_sform(affine, code=1)
nhdr.set_qform(affine, code=1)
if "parse" in opts.minmax:
# need to get the scaled data
verbose("Loading (and scaling) the data to determine value range")
if opts.minmax[0] == "parse":
nhdr["cal_min"] = in_data.min() * slope + intercept
else:
nhdr["cal_min"] = float(opts.minmax[0])
if opts.minmax[1] == "parse":
nhdr["cal_max"] = in_data.max() * slope + intercept
else:
nhdr["cal_max"] = float(opts.minmax[1])
# container for potential NIfTI1 header extensions
if opts.store_header:
# dump the full PAR header content into an extension
with open(infile, "rb") as fobj: # contents must be bytes
hdr_dump = fobj.read()
dump_ext = nifti1.Nifti1Extension("comment", hdr_dump)
nhdr.extensions.append(dump_ext)
verbose("Writing %s" % outfilename)
nibabel.save(nimg, outfilename)
# write out bvals/bvecs if requested
if opts.bvs:
if bvals is None and bvecs is None:
verbose("No DTI volumes detected, bvals and bvecs not written")
elif bvecs is None:
verbose("DTI volumes detected, but no diffusion direction info was" "found. Writing .bvals file only.")
with open(basefilename + ".bvals", "w") as fid:
# np.savetxt could do this, but it's just a loop anyway
for val in bvals:
fid.write("%s " % val)
fid.write("\n")
else:
verbose("Writing .bvals and .bvecs files")
# Transform bvecs with reorientation affine
orig2new = npl.inv(t_aff)
bv_reorient = from_matvec(to_matvec(orig2new)[0], [0, 0, 0])
bvecs = apply_affine(bv_reorient, bvecs)
with open(basefilename + ".bvals", "w") as fid:
# np.savetxt could do this, but it's just a loop anyway
for val in bvals:
fid.write("%s " % val)
fid.write("\n")
with open(basefilename + ".bvecs", "w") as fid:
for row in bvecs.T:
for val in row:
fid.write("%s " % val)
fid.write("\n")
# export data labels varying along the 4th dimensions if requested
if opts.vol_info:
labels = pr_img.header.get_volume_labels()
if len(labels) > 0:
vol_keys = list(labels.keys())
with open(basefilename + ".ordering.csv", "w") as csvfile:
csvwriter = csv.writer(csvfile, delimiter=",")
csvwriter.writerow(vol_keys)
for vals in zip(*[labels[k] for k in vol_keys]):
csvwriter.writerow(vals)
# write out dwell time if requested
if opts.dwell_time:
try:
dwell_time = calculate_dwell_time(
pr_hdr.get_water_fat_shift(), pr_hdr.get_echo_train_length(), opts.field_strength
)
except MRIError:
verbose("No EPI factors, dwell time not written")
else:
verbose("Writing dwell time (%r sec) calculated assuming %sT " "magnet" % (dwell_time, opts.field_strength))
with open(basefilename + ".dwell_time", "w") as fid:
fid.write("%r\n" % dwell_time)
def space_time_realign(input,
tr,
slice_order='descending',
slice_dim=2,
slice_dir=1,
apply=True,
make_figure=False,
out_name=None):
"""
This is a scripting interface to `nipy.algorithms.registration.SpaceTimeRealign`
Parameters
----------
input : str or list
A full path to a file-name (4D nifti time-series) , or to a directory
containing 4D nifti time-series, or a list of full-paths to files.
tr : float
The repetition time
slice_order : str (optional)
This is the order of slice-times in the acquisition. This is used as a
key into the ``SLICETIME_FUNCTIONS`` dictionary from
:mod:`nipy.algorithms.slicetiming.timefuncs`. Default: 'descending'.
slice_dim : int (optional)
Denotes the axis in `images` that is the slice axis. In a 4D image,
this will often be axis = 2 (default).
slice_dir : int (optional)
1 if the slices were acquired slice 0 first (default), slice -1 last,
or -1 if acquire slice -1 first, slice 0 last.
apply : bool (optional)
Whether to apply the transformation and produce an output. Default:
True.
make_figure : bool (optional)
Whether to generate a .png figure with the parameters across scans.
out_name : bool (optional)
Specify an output location (full path) for the files that are
generated. Default: generate files in the path of the inputs (with an
`_mc` suffix added to the file-names.
Returns
-------
transforms : ndarray
An (n_times_points,) shaped array containing
`nipy.algorithms.registration.affine.Rigid` class instances for each time
point in the time-series. These can be used as affine transforms by
referring to their `.as_affine` attribute.
"""
if make_figure:
if not HAVE_MPL:
e_s = "You need to have matplotlib installed to run this function"
e_s += " with `make_figure` set to `True`"
raise RuntimeError(e_s)
# If we got only a single file, we motion correct that one:
if op.isfile(input):
if not (input.endswith('.nii') or input.endswith('.nii.gz')):
e_s = "Input needs to be a nifti file ('.nii' or '.nii.gz'"
raise ValueError(e_s)
fnames = [input]
input = nib.load(input)
# If this is a full-path to a directory containing files, it's still a
# string:
elif isinstance(input, str):
list_of_files = os.listdir(input)
fnames = [
op.join(input, f) for f in np.sort(list_of_files)
if (f.endswith('.nii') or f.endswith('.nii.gz'))
]
input = [nib.load(x) for x in fnames]
# Assume that it's a list of full-paths to files:
else:
input = [nib.load(x) for x in input]
slice_times = timefuncs[slice_order]
slice_info = [slice_dim, slice_dir]
reggy = SpaceTimeRealign(input, tr, slice_times, slice_info)
reggy.estimate(align_runs=True)
# We now have the transformation parameters in here:
transforms = np.squeeze(np.array(reggy._transforms))
rot = np.array([t.rotation for t in transforms])
trans = np.array([t.translation for t in transforms])
if apply:
new_reggy = reggy.resample(align_runs=True)
for run_idx, new_im in enumerate(new_reggy):
# Fix output TR - it was probably lost in the image realign step
assert new_im.affine.shape == (5, 5)
new_im.affine[:] = new_im.affine.dot(np.diag([1, 1, 1, tr, 1]))
# Save it out to a '.nii.gz' file:
froot, ext, trail_ext = splitext_addext(fnames[run_idx])
path, fname = op.split(froot)
# We retain the file-name adding '_mc' regardless of where it's
# saved
new_path = path if out_name is None else out_name
save_image(new_im, op.join(new_path, fname + '_mc.nii.gz'))
if make_figure:
figure, ax = plt.subplots(2)
figure.set_size_inches([8, 6])
ax[0].plot(rot)
ax[0].set_xlabel('Time (TR)')
ax[0].set_ylabel('Translation (mm)')
ax[1].plot(trans)
ax[1].set_xlabel('Time (TR)')
ax[1].set_ylabel('Rotation (radians)')
figure.savefig(op.join(os.path.split(fnames[0])[0], 'mc_params.png'))
return transforms