def _run_interface(self, runtime):
""" delegates to base interface run method, then attempts to generate reports """
if not self._mock_run:
try:
runtime = super(ReportCapableInterface,
self)._run_interface(runtime)
except NotImplementedError:
pass # the interface is derived from BaseInterface
else:
runtime.returncode = 0
# leave early if there's nothing to do
if not self.inputs.generate_report:
return runtime
self._out_report = os.path.abspath(self.inputs.out_report)
self._post_run_hook(runtime)
# check exit code and act consequently
NIWORKFLOWS_LOG.debug('Running report generation code')
if hasattr(runtime,
'returncode') and runtime.returncode not in [0, None]:
self._generate_error_report(errno=runtime.get('returncode', None))
else:
self._generate_report()
NIWORKFLOWS_LOG.info('Successfully created report (%s)',
self._out_report)
return runtime
def _run_interface(self, runtime):
settings_files = self._get_settings()
for ants_settings in settings_files:
interface_result = None
self._config_ants(ants_settings)
NIWORKFLOWS_LOG.info(
'Retry #%d, commandline: \n%s', self.retry, self.norm.cmdline)
try:
interface_result = self.norm.run()
except Exception as exc:
NIWORKFLOWS_LOG.warn(
'Retry #%d failed: %s.', self.retry, exc)
errfile = op.join(runtime.cwd, 'stderr.nipype')
outfile = op.join(runtime.cwd, 'stdout.nipype')
shutil.move(errfile, errfile + '.%03d' % self.retry)
shutil.move(outfile, outfile + '.%03d' % self.retry)
if interface_result is not None:
runtime.returncode = 0
self._results.update(interface_result.outputs.get())
NIWORKFLOWS_LOG.info(
'Successful spatial normalization (retry #%d).', self.retry)
return runtime
self.retry += 1
raise RuntimeError(
'Robust spatial normalization failed after %d retries.' % (self.retry - 1))
def _generate_report(self):
""" Generates the visual report """
from niworkflows.viz.utils import compose_view, plot_registration
NIWORKFLOWS_LOG.info('Generating visual report')
anat = load_img(self._anat_file)
contour_nii = load_img(
self._contour) if self._contour is not None else None
if self._mask_file:
anat = unmask(apply_mask(anat, self._mask_file), self._mask_file)
mask_nii = load_img(self._mask_file)
else:
mask_nii = threshold_img(anat, 1e-3)
n_cuts = 7
if not self._mask_file and contour_nii:
cuts = cuts_from_bbox(contour_nii, cuts=n_cuts)
else:
cuts = cuts_from_bbox(mask_nii, cuts=n_cuts)
# Call composer
compose_view(plot_registration(anat,
'fixed-image',
estimate_brightness=True,
cuts=cuts,
contour=contour_nii,
compress=self.inputs.compress_report),
[],
out_file=self._out_report)
def _generate_report(self):
"""Generate a reportlet."""
NIWORKFLOWS_LOG.info('Generating visual report')
refnii = load_img(self.inputs.reference)
fmapnii = load_img(self.inputs.fieldmap)
contour_nii = load_img(self.inputs.mask) if isdefined(
self.inputs.mask) else None
mask_nii = threshold_img(refnii, 1e-3)
cuts = cuts_from_bbox(contour_nii or mask_nii, cuts=self._n_cuts)
fmapdata = fmapnii.get_fdata()
vmax = max(fmapdata.max(), abs(fmapdata.min()))
# Call composer
compose_view(plot_registration(refnii,
'fixed-image',
estimate_brightness=True,
cuts=cuts,
label='reference',
contour=contour_nii,
compress=False),
plot_registration(fmapnii,
'moving-image',
estimate_brightness=True,
cuts=cuts,
label='fieldmap (Hz)',
contour=contour_nii,
compress=False,
plot_params={
'cmap': coolwarm_transparent(),
'vmax': vmax,
'vmin': -vmax
}),
out_file=self._out_report)
def _post_run_hook(self, runtime):
''' generates a report showing nine slices, three per axis, of an
arbitrary volume of `in_files`, with the resulting segmentation
overlaid '''
outputs = self.aggregate_outputs()
self._melodic_dir = outputs.out_dir
NIWORKFLOWS_LOG.info('Generating report for MELODIC')
def _post_run_hook(self, runtime):
self._fixed_image = self.inputs.reference
self._moving_image = self.aggregate_outputs().out_file
self._contour = self.inputs.wm_seg if isdefined(
self.inputs.wm_seg) else None
NIWORKFLOWS_LOG.info(
'Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
def _post_run_hook(self, runtime):
mri_dir = os.path.join(self.inputs.subjects_dir,
self.inputs.subject_id, 'mri')
self._fixed_image = os.path.join(mri_dir, 'brainmask.mgz')
self._moving_image = self.aggregate_outputs().registered_file
self._contour = os.path.join(mri_dir, 'ribbon.mgz')
NIWORKFLOWS_LOG.info(
'Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
def _post_run_hook(self, runtime):
# We need to dig into the internal ants.Registration interface
self._fixed_image = self.norm.inputs.fixed_image[
0] # and get first item
if isdefined(self.norm.inputs.fixed_image_mask):
self._fixed_image_mask = self.norm.inputs.fixed_image_mask
self._moving_image = self.aggregate_outputs().warped_image
NIWORKFLOWS_LOG.info(
'Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
def _post_run_hook(self, runtime):
self._fixed_image_label = "after"
self._moving_image_label = "before"
self._fixed_image = self.aggregate_outputs().unwarped_file
self._moving_image = self.inputs.in_file
self._contour = self.inputs.wm_seg if isdefined(
self.inputs.wm_seg) else None
NIWORKFLOWS_LOG.info(
'Report - setting corrected (%s) and warped (%s) images',
self._fixed_image, self._moving_image)
def _post_run_hook(self, runtime):
''' generates a report showing slices from each axis '''
self._anat_file = self.inputs.realigned_file
self._mask_file = self.inputs.mask_file
self._seg_files = [self.inputs.mask_file]
self._masked = False
self._report_title = 'aCompCor ROI'
NIWORKFLOWS_LOG.info(
'Generating report for aCompCor. file "%s", mask "%s"',
self.inputs.realigned_file, self._mask_file)
def _generate_report(self):
"""Generate a reportlet."""
NIWORKFLOWS_LOG.info('Generating visual report')
movnii = refnii = load_img(self.inputs.reference)
fmapnii = load_img(self.inputs.fieldmap)
if isdefined(self.inputs.moving):
movnii = load_img(self.inputs.moving)
contour_nii = mask_nii = None
if isdefined(self.inputs.mask):
contour_nii = load_img(self.inputs.mask)
maskdata = contour_nii.get_fdata() > 0
else:
mask_nii = threshold_img(refnii, 1e-3)
maskdata = mask_nii.get_fdata() > 0
cuts = cuts_from_bbox(contour_nii or mask_nii, cuts=self._n_cuts)
fmapdata = fmapnii.get_fdata()
vmax = max(abs(np.percentile(fmapdata[maskdata], 99.8)),
abs(np.percentile(fmapdata[maskdata], 0.2)))
fmap_overlay = [{
'overlay': fmapnii,
'overlay_params': {
'cmap': coolwarm_transparent(max_alpha=self.inputs.max_alpha),
'vmax': vmax,
'vmin': -vmax,
}
}] * 2
if self.inputs.show != 'both':
fmap_overlay[not self.inputs.show] = {}
# Call composer
compose_view(plot_registration(movnii,
'moving-image',
estimate_brightness=True,
cuts=cuts,
label=self.inputs.moving_label,
contour=contour_nii,
compress=False,
**fmap_overlay[1]),
plot_registration(refnii,
'fixed-image',
estimate_brightness=True,
cuts=cuts,
label=self.inputs.reference_label,
contour=contour_nii,
compress=False,
**fmap_overlay[0]),
out_file=self._out_report)
def _config_ants(self, ants_settings):
NIWORKFLOWS_LOG.info('Loading settings from file %s.', ants_settings)
self.norm = Registration(
moving_image=self.inputs.moving_image,
num_threads=self.inputs.num_threads,
from_file=ants_settings,
terminal_output='file',
write_composite_transform=True
)
if isdefined(self.inputs.moving_mask):
if self.inputs.explicit_masking:
self.norm.inputs.moving_image = mask(
self.inputs.moving_image[0],
self.inputs.moving_mask,
"moving_masked.nii.gz")
else:
self.norm.inputs.moving_image_mask = self.inputs.moving_mask
if isdefined(self.inputs.reference_image):
self.norm.inputs.fixed_image = self.inputs.reference_image
if isdefined(self.inputs.reference_mask):
if self.inputs.explicit_masking:
self.norm.inputs.fixed_image = mask(
self.inputs.reference_image[0],
self.inputs.mreference_mask,
"fixed_masked.nii.gz")
else:
self.norm.inputs.fixed_image_mask = self.inputs.reference_mask
else:
get_template = getattr(getters, 'get_{}'.format(self.inputs.template))
mni_template = get_template()
if self.inputs.orientation == 'LAS':
raise NotImplementedError
resolution = self.inputs.template_resolution
if self.inputs.testing:
resolution = 2
if self.inputs.explicit_masking:
self.norm.inputs.fixed_image = mask(op.join(
mni_template, '%dmm_%s.nii.gz' % (resolution, self.inputs.reference)),
op.join(
mni_template, '%dmm_brainmask.nii.gz' % resolution),
"fixed_masked.nii.gz")
else:
self.norm.inputs.fixed_image = op.join(
mni_template,
'%dmm_%s.nii.gz' % (resolution, self.inputs.reference))
self.norm.inputs.fixed_image_mask = op.join(
mni_template, '%dmm_brainmask.nii.gz' % resolution)
def _post_run_hook(self, runtime):
''' generates a report showing slices from each axis '''
self._anat_file = self.inputs.realigned_file
self._mask_file = self.aggregate_outputs().high_variance_mask
self._seg_files = [self.aggregate_outputs().high_variance_mask]
self._masked = False
self._report_title = 'tCompCor - high variance voxels'
NIWORKFLOWS_LOG.info(
'Generating report for tCompCor. file "%s", mask "%s"',
self.inputs.realigned_file,
self.aggregate_outputs().high_variance_mask)
def _get_settings(self):
if isdefined(self.inputs.settings):
NIWORKFLOWS_LOG.info('User-defined settings, overriding defaults')
return self.inputs.settings
filestart = '{}-mni_registration_'.format(self.inputs.moving.lower())
if self.inputs.testing:
filestart += 'testing_'
filenames = [i for i in pkgr.resource_listdir('niworkflows', 'data')
if i.startswith(filestart) and i.endswith('.json')]
return [pkgr.resource_filename('niworkflows.data', f)
for f in sorted(filenames)]
def _post_run_hook(self, runtime):
''' generates a report showing slices from each axis of an arbitrary
volume of in_file, with the resulting binary brain mask overlaid '''
self._anat_file = self.inputs.in_file
self._mask_file = self.aggregate_outputs().mask_file
self._seg_files = [self._mask_file]
self._masked = self.inputs.mask
self._report_title = "BET: brain mask over anatomical input"
NIWORKFLOWS_LOG.info(
'Generating report for BET. file "%s", and mask file "%s"',
self._anat_file, self._mask_file)
def _post_run_hook(self, runtime):
''' generates a report showing slices from each axis '''
brain_extraction_mask = self.aggregate_outputs().BrainExtractionMask
self._anat_file = self.inputs.anatomical_image
self._mask_file = brain_extraction_mask
self._seg_files = [brain_extraction_mask]
self._masked = False
self._report_title = 'ANTS BrainExtraction: brain mask over anatomical input'
NIWORKFLOWS_LOG.info(
'Generating report for ANTS BrainExtraction. file "%s", mask "%s"',
self._anat_file, self._mask_file)
def _post_run_hook(self, runtime):
''' generates a report showing nine slices, three per axis, of an
arbitrary volume of `in_files`, with the resulting segmentation
overlaid '''
outputs = self.aggregate_outputs()
self._anat_file = os.path.join(outputs.subjects_dir,
outputs.subject_id, 'mri', 'brain.mgz')
self._contour = os.path.join(outputs.subjects_dir, outputs.subject_id,
'mri', 'ribbon.mgz')
self._masked = False
self._report_title = "ReconAll: segmentation over anatomical"
NIWORKFLOWS_LOG.info('Generating report for ReconAll (subject %s)',
outputs.subject_id)
def _post_run_hook(self, runtime):
''' generates a report showing nine slices, three per axis, of an
arbitrary volume of `in_files`, with the resulting segmentation
overlaid '''
self._anat_file = self.inputs.in_files[0],
self._mask_file = self.aggregate_outputs().tissue_class_map
self._seg_files = self.aggregate_outputs().tissue_class_files
self._masked = False
self._report_title = "FAST: segmentation over anatomical"
NIWORKFLOWS_LOG.info(
'Generating report for FAST (in_files %s, '
'segmentation %s, individual tissue classes %s).',
self.inputs.in_files,
self.aggregate_outputs().tissue_class_map,
self.aggregate_outputs().tissue_class_files)
def _generate_report(self):
""" Generates the visual report """
from niworkflows.viz.utils import compose_view, plot_registration
NIWORKFLOWS_LOG.info('Generating visual report')
fixed_image_nii = load_img(self._fixed_image)
moving_image_nii = load_img(self._moving_image)
contour_nii = load_img(
self._contour) if self._contour is not None else None
if self._fixed_image_mask:
fixed_image_nii = unmask(
apply_mask(fixed_image_nii, self._fixed_image_mask),
self._fixed_image_mask)
# since the moving image is already in the fixed image space we
# should apply the same mask
moving_image_nii = unmask(
apply_mask(moving_image_nii, self._fixed_image_mask),
self._fixed_image_mask)
mask_nii = load_img(self._fixed_image_mask)
else:
mask_nii = threshold_img(fixed_image_nii, 1e-3)
n_cuts = 7
if not self._fixed_image_mask and contour_nii:
cuts = cuts_from_bbox(contour_nii, cuts=n_cuts)
else:
cuts = cuts_from_bbox(mask_nii, cuts=n_cuts)
# Call composer
compose_view(plot_registration(fixed_image_nii,
'fixed-image',
estimate_brightness=True,
cuts=cuts,
label=self._fixed_image_label,
contour=contour_nii,
compress=self.inputs.compress_report),
plot_registration(moving_image_nii,
'moving-image',
estimate_brightness=True,
cuts=cuts,
label=self._moving_image_label,
contour=contour_nii,
compress=self.inputs.compress_report),
out_file=self._out_report)
def _run_interface(self, runtime):
""" there is not inner interface to run """
self._out_report = os.path.abspath(self.inputs.out_report)
self._fixed_image_label = "after"
self._moving_image_label = "before"
self._fixed_image = self.inputs.after
self._moving_image = self.inputs.before
self._contour = self.inputs.wm_seg if isdefined(
self.inputs.wm_seg) else None
NIWORKFLOWS_LOG.info(
'Report - setting before (%s) and after (%s) images',
self._fixed_image, self._moving_image)
self._generate_report()
NIWORKFLOWS_LOG.info('Successfully created report (%s)',
self._out_report)
return runtime
def extract_svg(display_object, dpi=300, compress='auto'):
"""
Removes the preamble of the svg files generated with nilearn
"""
image_svg = svg2str(display_object, dpi)
if compress is True or compress == 'auto':
image_svg = svg_compress(image_svg, compress)
image_svg = re.sub(' height="[0-9]+[a-z]*"', '', image_svg, count=1)
image_svg = re.sub(' width="[0-9]+[a-z]*"', '', image_svg, count=1)
image_svg = re.sub(' viewBox',
' preseveAspectRation="xMidYMid meet" viewBox',
image_svg, count=1)
start_tag = '<svg '
start_idx = image_svg.find(start_tag)
end_tag = '</svg>'
end_idx = image_svg.rfind(end_tag)
if start_idx is -1 or end_idx is -1:
NIWORKFLOWS_LOG.info('svg tags not found in extract_svg')
# rfind gives the start index of the substr. We want this substr
# included in our return value so we add its length to the index.
end_idx += len(end_tag)
return image_svg[start_idx:end_idx]
def plot_registration(anat_nii, div_id, plot_params=None,
order=('z', 'x', 'y'), cuts=None,
estimate_brightness=False, label=None, contour=None,
compress='auto'):
"""
Plots the foreground and background views
Default order is: axial, coronal, sagittal
"""
plot_params = {} if plot_params is None else plot_params
# Use default MNI cuts if none defined
if cuts is None:
raise NotImplementedError # TODO
out_files = []
if estimate_brightness:
plot_params = robust_set_limits(anat_nii.get_data().reshape(-1),
plot_params)
# FreeSurfer ribbon.mgz
ribbon = contour is not None and \
np.array_equal(np.unique(contour.get_data()),
[0, 2, 3, 41, 42])
if ribbon:
contour_data = contour.get_data() % 39
white = nlimage.new_img_like(contour, contour_data == 2)
pial = nlimage.new_img_like(contour, contour_data >= 2)
# Plot each cut axis
for i, mode in enumerate(list(order)):
out_file = '{}_{}.svg'.format(div_id, mode)
plot_params['display_mode'] = mode
plot_params['cut_coords'] = cuts[mode]
if i == 0:
plot_params['title'] = label
else:
plot_params['title'] = None
# Generate nilearn figure
display = plot_anat(anat_nii, **plot_params)
if ribbon:
kwargs = {'levels': [0.5], 'linewidths': 0.5}
display.add_contours(white, colors='b', **kwargs)
display.add_contours(pial, colors='r', **kwargs)
elif contour is not None:
display.add_contours(contour, colors='b', levels=[0.5],
linewidths=0.5)
svg = extract_svg(display, compress=compress)
display.close()
# Find and replace the figure_1 id.
try:
xml_data = etree.fromstring(svg)
except etree.XMLSyntaxError as e:
NIWORKFLOWS_LOG.info(e)
return
find_text = etree.ETXPath("//{%s}g[@id='figure_1']" % (SVGNS))
find_text(xml_data)[0].set('id', '%s-%s-%s' % (div_id, mode, uuid4()))
out_files.append(etree.tostring(xml_data))
return out_files
def _post_run_hook(self, runtime):
self._fixed_image = self.inputs.reference_image
self._moving_image = self.aggregate_outputs().output_image
NIWORKFLOWS_LOG.info(
'Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
def _get_dataset_dir(dataset_name,
data_dir=None,
default_paths=None,
verbose=1):
""" Create if necessary and returns data directory of given dataset.
:param str dataset_name: The unique name of the dataset.
:param str data_dir: Path of the data directory. Used to force data storage in
a specified location.
:param list(str) default_paths: Default system paths in which the dataset
may already have been installed by a third party software. They will be
checked first.
:param int verbose: verbosity level (0 means no message).
:returns: the path of the given dataset directory.
:rtype: str
.. note::
This function retrieves the datasets directory (or data directory) using
the following priority :
1. defaults system paths
2. the keyword argument data_dir
3. the global environment variable CRN_SHARED_DATA
4. the user environment variable CRN_DATA
5. ~/.cache/stanford-crn in the user home folder
"""
# We build an array of successive paths by priority
# The boolean indicates if it is a pre_dir: in that case, we won't add the
# dataset name to the path.
paths = []
# Check data_dir which force storage in a specific location
if data_dir is not None:
paths.extend([(d, False) for d in data_dir.split(os.pathsep)])
# Search possible system paths
if default_paths is not None:
for default_path in default_paths:
paths.extend([(d, True) for d in default_path.split(os.pathsep)])
# If data_dir has not been specified, then we crawl default locations
if data_dir is None:
global_data = os.getenv('CRN_SHARED_DATA')
if global_data is not None:
paths.extend([(d, False) for d in global_data.split(os.pathsep)])
local_data = os.getenv('CRN_DATA')
if local_data is not None:
paths.extend([(d, False) for d in local_data.split(os.pathsep)])
paths.append((NIWORKFLOWS_CACHE_DIR, False))
if verbose > 2:
NIWORKFLOWS_LOG.info('Dataset search paths: %s', str(paths))
# Check if the dataset exists somewhere
for path, is_pre_dir in paths:
if not is_pre_dir:
path = op.join(path, dataset_name)
if op.islink(path):
# Resolve path
path = readlinkabs(path)
if op.exists(path) and op.isdir(path):
if verbose > 1:
NIWORKFLOWS_LOG.info('Dataset already cached in %s', path)
return path
# If not, create a folder in the first writeable directory
errors = []
for (path, is_pre_dir) in paths:
if not is_pre_dir:
path = op.join(path, dataset_name)
if not op.exists(path):
try:
os.makedirs(path)
if verbose > 0:
NIWORKFLOWS_LOG.info('Dataset created in %s', path)
return path
except Exception as exc:
short_error_message = getattr(exc, 'strerror', str(exc))
errors.append('\n -{0} ({1})'.format(path,
short_error_message))
raise OSError('niworkflows tried to store the dataset in the following '
'directories, but:' + ''.join(errors))
def _fetch_file(url,
dataset_dir,
filetype=None,
resume=True,
overwrite=False,
md5sum=None,
username=None,
password=None,
retry=0,
verbose=1,
temp_downloads=None):
"""Load requested file, downloading it if needed or requested.
:param str url: contains the url of the file to be downloaded.
:param str dataset_dir: path of the data directory. Used for data
storage in the specified location.
:param bool resume: if true, try to resume partially downloaded files
:param overwrite: if bool true and file already exists, delete it.
:param str md5sum: MD5 sum of the file. Checked if download of the file
is required
:param str username: username used for basic HTTP authentication
:param str password: password used for basic HTTP authentication
:param int verbose: verbosity level (0 means no message).
:returns: absolute path of downloaded file
:rtype: str
..note::
If, for any reason, the download procedure fails, all downloaded files are
removed.
"""
if not overwrite and os.listdir(dataset_dir):
return True
data_dir, _ = op.split(dataset_dir)
if temp_downloads is None:
temp_downloads = op.join(NIWORKFLOWS_CACHE_DIR, 'downloads')
# Determine data path
if not op.exists(temp_downloads):
os.makedirs(temp_downloads)
# Determine filename using URL
parse = urlparse(url)
file_name = op.basename(parse.path)
if file_name == '':
file_name = _md5_hash(parse.path)
if filetype is not None:
file_name += filetype
temp_full_name = op.join(temp_downloads, file_name)
temp_part_name = temp_full_name + ".part"
if overwrite:
shutil.rmtree(dataset_dir, ignore_errors=True)
if op.exists(temp_full_name):
if overwrite:
os.remove(temp_full_name)
t_0 = time.time()
local_file = None
initial_size = 0
# Download data
request = Request(url)
request.add_header('Connection', 'Keep-Alive')
if username is not None and password is not None:
if not url.startswith('https'):
raise ValueError(
'Authentication was requested on a non secured URL ({0!s}).'
'Request has been blocked for security reasons.'.format(url))
# Note: HTTPBasicAuthHandler is not fitted here because it relies
# on the fact that the server will return a 401 error with proper
# www-authentication header, which is not the case of most
# servers.
encoded_auth = base64.b64encode((username + ':' + password).encode())
request.add_header(b'Authorization', b'Basic ' + encoded_auth)
if verbose > 0:
displayed_url = url.split('?')[0] if verbose == 1 else url
NIWORKFLOWS_LOG.info('Downloading data from %s ...', displayed_url)
if resume and op.exists(temp_part_name):
# Download has been interrupted, we try to resume it.
local_file_size = op.getsize(temp_part_name)
# If the file exists, then only download the remainder
request.add_header("Range", "bytes={}-".format(local_file_size))
try:
data = urlopen(request)
content_range = data.info().get('Content-Range')
if (content_range is None or not content_range.startswith(
'bytes {}-'.format(local_file_size))):
raise IOError('Server does not support resuming')
except Exception:
# A wide number of errors can be raised here. HTTPError,
# URLError... I prefer to catch them all and rerun without
# resuming.
if verbose > 0:
NIWORKFLOWS_LOG.warn(
'Resuming failed, try to download the whole file.')
return _fetch_file(url,
dataset_dir,
resume=False,
overwrite=overwrite,
md5sum=md5sum,
username=username,
password=password,
verbose=verbose)
local_file = open(temp_part_name, "ab")
initial_size = local_file_size
else:
try:
data = urlopen(request)
except (HTTPError, URLError):
if retry < MAX_RETRIES:
if verbose > 0:
NIWORKFLOWS_LOG.warn(
'Download failed, retrying (attempt %d)', retry + 1)
time.sleep(5)
return _fetch_file(url,
dataset_dir,
resume=False,
overwrite=overwrite,
md5sum=md5sum,
username=username,
password=password,
verbose=verbose,
retry=retry + 1)
else:
raise
local_file = open(temp_part_name, "wb")
_chunk_read_(data,
local_file,
report_hook=(verbose > 0),
initial_size=initial_size,
verbose=verbose)
# temp file must be closed prior to the move
if not local_file.closed:
local_file.close()
shutil.move(temp_part_name, temp_full_name)
delta_t = time.time() - t_0
if verbose > 0:
# Complete the reporting hook
sys.stderr.write(' ...done. ({0:.0f} seconds, {1:.0f} min)\n'.format(
delta_t, delta_t // 60))
if md5sum is not None:
if _md5_sum_file(temp_full_name) != md5sum:
raise ValueError("File {} checksum verification has failed."
" Dataset fetching aborted.".format(local_file))
if filetype is None:
fname, filetype = op.splitext(op.basename(temp_full_name))
if filetype == '.gz':
fname, ext = op.splitext(fname)
filetype = ext + filetype
if filetype.startswith('.'):
filetype = filetype[1:]
if filetype == 'tar':
sp.check_call(['tar', 'xf', temp_full_name], cwd=data_dir)
os.remove(temp_full_name)
return True
return True
def _generate_report(self):
"""Generate a reportlet."""
NIWORKFLOWS_LOG.info("Generating visual report")
movnii = load_img(self.inputs.reference)
canonical_r = rotation2canonical(movnii)
movnii = refnii = rotate_affine(movnii, rot=canonical_r)
fmapnii = nb.squeeze_image(
rotate_affine(load_img(self.inputs.fieldmap), rot=canonical_r))
if fmapnii.dataobj.ndim == 4:
for i, tstep in enumerate(nb.four_to_three(fmapnii)):
if np.any(np.asanyarray(tstep.dataobj) != 0):
fmapnii = tstep
break
if isdefined(self.inputs.moving):
movnii = rotate_affine(load_img(self.inputs.moving),
rot=canonical_r)
contour_nii = mask_nii = None
if isdefined(self.inputs.mask):
contour_nii = rotate_affine(load_img(self.inputs.mask),
rot=canonical_r)
maskdata = contour_nii.get_fdata() > 0
else:
mask_nii = threshold_img(refnii, 1e-3)
maskdata = mask_nii.get_fdata() > 0
cuts = cuts_from_bbox(contour_nii or mask_nii, cuts=self._n_cuts)
fmapdata = fmapnii.get_fdata()
vmax = max(
abs(np.percentile(fmapdata[maskdata], 99.8)),
abs(np.percentile(fmapdata[maskdata], 0.2)),
)
if self.inputs.apply_mask:
fmapdata[~maskdata] = 0
fmapnii = fmapnii.__class__(fmapdata, fmapnii.affine,
fmapnii.header)
fmap_overlay = [{
"overlay": fmapnii,
"overlay_params": {
"cmap": coolwarm_transparent(max_alpha=self.inputs.max_alpha),
"vmax": vmax,
"vmin": -vmax,
},
}] * 2
if self.inputs.show != "both":
fmap_overlay[not self.inputs.show] = {}
# Call composer
compose_view(
plot_registration(movnii,
"moving-image",
estimate_brightness=True,
cuts=cuts,
label=self.inputs.moving_label,
contour=contour_nii,
compress=False,
**fmap_overlay[1]),
plot_registration(refnii,
"fixed-image",
estimate_brightness=True,
cuts=cuts,
label=self.inputs.reference_label,
contour=contour_nii,
compress=False,
**fmap_overlay[0]),
out_file=self._out_report,
)