def _list_outputs(self):
outputs = self.output_spec().get()
if not isdefined(self.inputs.out_file):
prefix = self._gen_fname(self.inputs.in_file, suffix="_pval")
outputtype = self.inputs.outputtype
if outputtype == "AFNI":
ext = ".HEAD"
suffix = "+tlrc"
else:
ext = Info.output_type_to_ext(outputtype)
suffix = ""
else:
prefix = self.inputs.out_file
ext_ind = max([
prefix.lower().rfind(".nii.gz"),
prefix.lower().rfind(".nii")
])
if ext_ind == -1:
ext = ".HEAD"
suffix = "+tlrc"
else:
ext = prefix[ext_ind:]
suffix = ""
# All outputs should be in the same directory as the prefix
out_dir = os.path.dirname(os.path.abspath(prefix))
outputs["out_file"] = (fname_presuffix(
prefix, suffix=suffix, use_ext=False, newpath=out_dir) + ext)
return outputs
def _gen_fname(self,
basename,
cwd=None,
suffix=None,
change_ext=True,
ext=None):
"""Generate a filename based on the given parameters.
The filename will take the form: cwd/basename<suffix><ext>.
If change_ext is True, it will use the extensions specified in
<instance>inputs.outputtype.
Parameters
----------
basename : str
Filename to base the new filename on.
cwd : str
Path to prefix to the new filename. (default is os.getcwd())
suffix : str
Suffix to add to the `basename`. (default is '')
change_ext : bool
Flag to change the filename extension to the AFNI output type.
(default True)
Returns
-------
fname : str
New filename based on given parameters.
"""
if basename == '':
msg = 'Unable to generate filename for command %s. ' % self.cmd
msg += 'basename is not set!'
raise ValueError(msg)
if cwd is None:
cwd = os.getcwd()
if ext is None:
ext = Info.outputtype_to_ext(self.inputs.outputtype)
if change_ext:
if suffix:
suffix = ''.join((suffix, ext))
else:
suffix = ext
if suffix is None:
suffix = ''
fname = fname_presuffix(basename,
suffix=suffix,
use_ext=False,
newpath=cwd)
return fname
def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True, ext=None):
"""Generate a filename based on the given parameters.
The filename will take the form: cwd/basename<suffix><ext>.
If change_ext is True, it will use the extensions specified in
<instance>inputs.outputtype.
Parameters
----------
basename : str
Filename to base the new filename on.
cwd : str
Path to prefix to the new filename. (default is os.getcwd())
suffix : str
Suffix to add to the `basename`. (default is '')
change_ext : bool
Flag to change the filename extension to the AFNI output type.
(default True)
Returns
-------
fname : str
New filename based on given parameters.
"""
if basename == '':
msg = 'Unable to generate filename for command %s. ' % self.cmd
msg += 'basename is not set!'
raise ValueError(msg)
if cwd is None:
cwd = os.getcwd()
if ext is None:
ext = Info.outputtype_to_ext(self.inputs.outputtype)
if change_ext:
if suffix:
suffix = ''.join((suffix, ext))
else:
suffix = ext
if suffix is None:
suffix = ''
fname = fname_presuffix(basename, suffix = suffix,
use_ext = False, newpath = cwd)
return fname
def functional_preprocessing(
subject_list,
inputs, outputs, workingdir, output_type,
fwhm, hpfilter, lpfilter, tr, label, outlabel,
name="%s_preprocessing_p2"):
"""todo"""
#####
# Setup pipeline
#####
if name.find("%s") != -1:
name = name % label
else:
print "ERROR: You must have a '%s' in the name for the label"
raise SystemExit(2)
func_preproc_name = "func_preproc_%s_%ism_%ihp_%ilp" % (outlabel, int(fwhm*10),
hpfilter*(hpfilter>0), lpfilter*(lpfilter>0))
preproc = create_func_preproc_workflow(name, func_preproc_name)
preproc.base_dir = workingdir
preproc.inputs.inputspec.fwhm = fwhm
preproc.inputs.inputspec.highpass = float(hpfilter)/(2*tr)
preproc.inputs.inputspec.lowpass = float(lpfilter)/(2*tr)
## save for later
inputnode = preproc.get_node("inputspec")
outputnode = preproc.get_node("outputspec")
######
# Setup data source
######
# File extension
ext = Info.outputtype_to_ext(output_type)
afni.AFNICommand.set_default_outputtype(output_type)
# Subject ID Node
subinfo = pe.Node(interface=util.IdentityInterface(fields=['subject_id']), name='subinfo',
iterables=('subject_id', subject_list))
# Location of input data
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['func', 'func_mask'],
sort_filelist=True),
name='datasource')
datasource.inputs.base_directory=os.path.abspath(inputs.basedir)
datasource.inputs.template = "*"
datasource.inputs.field_template = dict(
func = os.path.join("%s", inputs.funcdir, 'run_[0-9]*', inputs.infunc),
func_mask = os.path.join("%s", inputs.funcdir, 'run_[0-9]*', inputs.inmask)
)
datasource.inputs.template_args = dict(
func = [['subject_id']],
func_mask = [['subject_id']]
)
# Get the number of runs for each participant (for renaming purposes)
datasource.inputs.subject_id = subject_list
ds = datasource.run()
runs1 = [ len(x) for x in tolist(ds.outputs.func) ]
runs2 = [ len(x) for x in tolist(ds.outputs.func_mask) ]
for i in xrange(len(runs1)):
if runs1[i] != runs2[i]:
print "ERROR: mismatch in number of functionals and masks"
raise SystemExit(2)
max_runs = max(runs1)
# Link inputs
preproc.connect(subinfo, 'subject_id', datasource, 'subject_id')
preproc.connect(datasource, 'func', inputnode, 'func')
preproc.connect(datasource, 'func_mask', inputnode, 'func_mask')
######
# Setup data sink
######
# Datasink
## will get: "base_directory/subject_id/output_anatdir"
datasink = pe.Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = outputs.basedir
## substitute map stuff
preprocsinksubs = get_mapnode_substitutions(preproc, outputnode, max_runs)
datasink.inputs.substitutions = preprocsinksubs
# replace subject_id stuff with functional scan
datasink.inputs.regexp_substitutions = (r"_subject_id_(\w|\d)+", outputs.funcdir)
## connect
preproc.connect(subinfo, 'subject_id', datasink, 'container')
output_fields = outputnode.outputs.get()
for field in output_fields:
preproc.connect(outputnode, field, datasink, "@%s" % field)
return preproc
def register(
subject_list,
inputs, outputs, workingdir, output_type,
standard, fnirt, interp, search, func_label,
name="registration"):
#####
# Setup workflow
#####
if hasattr(inputs, "coplanar"):
have_coplanar = True
else:
have_coplanar = False
# highres2standard
h2s = create_highres2standard_workflow("%s_highres2standard" % name, search, fnirt)
h2s.base_dir = workingdir
h2s_inputnode = h2s.get_node("inputspec")
h2s_outputnode = h2s.get_node("outputspec")
# func2standard
f2s = create_func2standard_workflow("%s_%s2standard" % (name, func_label), have_coplanar,
search, fnirt)
f2s.base_dir = workingdir
f2s_inputnode = f2s.get_node("inputspec")
f2s_outputnode = f2s.get_node("outputspec")
######
# Setup data source
######
# File extension
ext = Info.outputtype_to_ext(output_type)
afni.AFNICommand.set_default_outputtype(output_type)
# Subject ID Node
subinfo = pe.Node(interface=util.IdentityInterface(fields=['subject_id']), name='subinfo',
iterables=('subject_id', subject_list))
# Location of input data
outfields = ['func', 'highres']
if have_coplanar:
outfields.append('coplanar')
if fnirt:
outfields.append('highres_head')
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=outfields),
name='datasource')
datasource.inputs.base_directory = op.abspath(op.expanduser(inputs.basedir))
datasource.inputs.template = "*"
datasource.inputs.field_template = dict(
func = os.path.join("%s", inputs.func),
highres = os.path.join("%s", inputs.highres)
)
datasource.inputs.template_args = dict(
func = [['subject_id']],
highres = [['subject_id']]
)
if have_coplanar:
datasource.inputs.field_template['coplanar'] = os.path.join("%s", inputs.coplanar)
datasource.inputs.template_args['coplanar'] = [['subject_id']]
if fnirt:
datasource.inputs.field_template['highres_head'] = os.path.join("%s", inputs.highres_head)
datasource.inputs.template_args['highres_head'] = [['subject_id']]
######
# Link Inputs
######
# highres2standard
h2s_inputnode.inputs.interp = interp
h2s_inputnode.inputs.standard = standard
h2s.connect([
(subinfo, datasource, [('subject_id', 'subject_id')]),
(datasource, h2s_inputnode, [('highres', 'highres')])
])
if fnirt:
h2s.connect(datasource, 'highres_head', h2s_inputnode, 'highres_head')
h2s_inputnode.inputs.standard_head = inputs.standard_head
h2s_inputnode.inputs.standard_mask = inputs.standard_mask
h2s_inputnode.inputs.fnirt_config = inputs.fnirt_config
# func2standard
f2s_inputnode.inputs.interp = interp
f2s_inputnode.inputs.standard = standard
path = op.join(outputs.basedir, "%s", outputs.highres, "highres2standard.mat")
f2s.connect([
(subinfo, datasource, [('subject_id', 'subject_id')]),
(datasource, f2s_inputnode, [('func', 'func'), ('highres', 'highres')]),
(subinfo, f2s_inputnode, [(('subject_id', regpath, path), 'highres2standard_mat')])
])
if have_coplanar:
f2s.connect(datasource, 'coplanar', f2s_inputnode, 'coplanar')
if fnirt:
path = op.join(outputs.basedir, "%s", outputs.highres, "highres2standard_warp.*")
f2s.connect(subinfo, ('subject_id', regpath, path), f2s_inputnode, 'highres2standard_warp')
######
# Setup data sink
######
# highres2standard
## setup
h2s_datasink = pe.Node(interface=nio.DataSink(), name="datasink")
h2s_datasink.inputs.base_directory = op.abspath(op.expanduser(outputs.basedir))
h2s.connect(subinfo, 'subject_id', h2s_datasink, 'container')
h2s_datasink.inputs.regexp_substitutions = (r"_subject_id_(\w|\d)+", outputs.highres)
## link
outfields = h2s_outputnode.outputs.get()
for outfield in outfields:
h2s.connect(h2s_outputnode, outfield, h2s_datasink, "@%s" % outfield)
# func2standard
## setup
f2s_datasink = pe.Node(interface=nio.DataSink(), name="datasink")
f2s_datasink.inputs.base_directory = op.abspath(op.expanduser(outputs.basedir))
f2s.connect(subinfo, 'subject_id', f2s_datasink, 'container')
f2s_datasink.inputs.regexp_substitutions = (r"_subject_id_(\w|\d)+", outputs.func)
## link
outfields = f2s_outputnode.outputs.get()
for outfield in outfields:
f2s.connect(f2s_outputnode, outfield, f2s_datasink, "@%s" % outfield)
return [h2s, f2s]
def functional_preprocessing(
subject_list,
inputs, outputs, workingdir, output_type,
fwhm, hpfilter, lpfilter, tr, motion_nstages, label,
whichvol="middle", timeshift=False, tpattern=None, delete_vols=0,
name="%s_preprocessing"):
"""todo"""
#####
# Setup pipeline
#####
if name.find("%s") != -1:
name = name % label
else:
print "ERROR: You must have a '%s' in the name for the label"
raise SystemExit(2)
func_preproc_name = "func_preproc_%ism_%ihp_%ilp" % (int(fwhm*10),
hpfilter*(hpfilter>0), lpfilter*(lpfilter>0))
preproc = create_func_preproc_workflow(name, whichvol, timeshift, tpattern,
delete_vols, func_preproc_name)
preproc.base_dir = workingdir
preproc.inputs.inputspec.fwhm = fwhm
preproc.inputs.inputspec.highpass = float(hpfilter)/(2*tr)
preproc.inputs.inputspec.lowpass = float(lpfilter)/(2*tr)
preproc.inputs.inputspec.motion_nstages = motion_nstages
## save for later
inputnode = preproc.get_node("inputspec")
outputnode = preproc.get_node("outputspec")
######
# Setup data source
######
# File extension
ext = Info.outputtype_to_ext(output_type)
afni.AFNICommand.set_default_outputtype(output_type)
# Subject ID Node
subinfo = pe.Node(interface=util.IdentityInterface(fields=['subject_id']), name='subinfo',
iterables=('subject_id', subject_list))
# Location of input data
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['func'],
sort_filelist=True),
name='datasource')
datasource.inputs.base_directory=os.path.abspath(inputs.basedir)
datasource.inputs.template = os.path.join("%s", inputs.func)
datasource.inputs.template_args = dict(func=[['subject_id']])
# Get the number of runs for each participant (for renaming purposes)
datasource.inputs.subject_id = subject_list
ds = datasource.run()
runs = [ len(x) for x in tolist(ds.outputs.func) ] # list with number of runs per subject
max_runs = max(runs)
# Link inputs
preproc.connect(subinfo, 'subject_id', datasource, 'subject_id')
preproc.connect(datasource, 'func', inputnode, 'func')
######
# Setup data sink
######
# Datasink
## will get: "base_directory/subject_id/output_anatdir"
datasink = pe.Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = outputs.basedir
## substitute map stuff
preprocsinksubs = get_mapnode_substitutions(preproc, outputnode, max_runs,
unmap=["func_mc_ref", "example_func_all", "func_mask_all", "motion_all",
"motion_01", "motion_02", "motion_03", "motion_04", "motion_05", "motion_06"])
datasink.inputs.substitutions = preprocsinksubs
# replace subject_id stuff with functional scan
datasink.inputs.regexp_substitutions = (r"_subject_id_(\w|\d)+", outputs.func)
## connect
preproc.connect(subinfo, 'subject_id', datasink, 'container')
output_fields = outputnode.outputs.get()
for field in output_fields:
preproc.connect(outputnode, field, datasink, "@%s" % field)
return preproc