def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
if opt == 'phase' or opt == 'magnitude' or opt == 'anat':
return scans_for_fname(filename_to_list(val))
if opt == 'epi' or opt == 'magnitude':
return scans_for_fname(filename_to_list(val))
return super(FieldMap, self)._format_arg(opt, spec, val)
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
if opt == "template":
return scans_for_fname(filename_to_list(val))
if opt == "source":
return scans_for_fname(filename_to_list(val))
if opt == "apply_to_files":
return scans_for_fnames(filename_to_list(val))
if opt == "parameter_file":
return np.array([list_to_filename(val)], dtype=object)
if opt in ["write_wrap"]:
if len(val) != 3:
raise ValueError("%s must have 3 elements" % opt)
return val
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
if opt == 'template':
return scans_for_fname(filename_to_list(val))
if opt == 'source':
return scans_for_fname(filename_to_list(val))
if opt == 'apply_to_files':
return scans_for_fnames(filename_to_list(val))
if opt == 'parameter_file':
return np.array([list_to_filename(val)], dtype=object)
if opt in ['write_wrap']:
if len(val) != 3:
raise ValueError('%s must have 3 elements' % opt)
return super(Normalize, self)._format_arg(opt, spec, val)
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm"""
if opt == "in_files":
if isinstance(val, list):
return scans_for_fnames(val)
else:
return scans_for_fname(val)
return super(CAT12SANLMDenoising, self)._format_arg(opt, spec, val)
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
clean_masks_dict = {"no": 0, "light": 1, "thorough": 2}
if opt in ["data", "tissue_prob_maps"]:
if isinstance(val, list):
return scans_for_fnames(val)
else:
return scans_for_fname(val)
if "output_type" in opt:
return [int(v) for v in val]
if opt == "save_bias_corrected":
return int(val)
if opt == "mask_image":
return scans_for_fname(val)
if opt == "clean_masks":
return clean_masks_dict[val]
return val
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
clean_masks_dict = {'no':0, 'light':1, 'thorough':2}
if opt in ['data', 'tissue_prob_maps']:
if isinstance(val, list):
return scans_for_fnames(val)
else:
return scans_for_fname(val)
if 'output_type' in opt:
return [int(v) for v in val]
if opt == 'save_bias_corrected':
return int(val)
if opt == 'mask_image':
return scans_for_fname(val)
if opt == 'clean_masks':
return clean_masks_dict[val]
return val
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm"""
if opt == "in_files":
if isinstance(val, list):
return scans_for_fnames(val)
else:
return scans_for_fname(val)
if opt == "spm_type":
type_map = {"same": 0, "uint8": 2, "uint16": 512, "float32": 16}
val = type_map[val]
return super(CAT12SANLMDenoising, self)._format_arg(opt, spec, val)
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm"""
if opt == "in_files":
if isinstance(val, list):
return scans_for_fnames(val)
else:
return scans_for_fname(val)
elif opt in ["tpm", "shooting_tpm"]:
return Cell2Str(val)
return super(CAT12Segment, self)._format_arg(opt, spec, val)
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
if opt == 'in_files':
return scans_for_fnames(filename_to_list(val))
if opt == 'target':
return scans_for_fname(filename_to_list(val))
if opt == 'deformation':
return np.array([list_to_filename(val)], dtype=object)
if opt == 'deformation_field':
return np.array([list_to_filename(val)], dtype=object)
return val
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
if opt == 'in_files':
return scans_for_fnames(filename_to_list(val))
if opt == 'target':
return scans_for_fname(filename_to_list(val))
if opt == 'deformation':
return np.array([list_to_filename(val)], dtype=object)
if opt == 'deformation_field':
return np.array([list_to_filename(val)], dtype=object)
return val
def _parse_inputs(self):
"""validate spm realign options if set to None ignore
"""
einputs = super(Normalize, self)._parse_inputs(skip=("jobtype", "apply_to_files"))
if isdefined(self.inputs.apply_to_files):
inputfiles = deepcopy(self.inputs.apply_to_files)
if isdefined(self.inputs.source):
inputfiles.extend(self.inputs.source)
einputs[0]["subj"]["resample"] = scans_for_fnames(inputfiles)
jobtype = self.inputs.jobtype
if jobtype in ["estwrite", "write"]:
if not isdefined(self.inputs.apply_to_files):
if isdefined(self.inputs.source):
einputs[0]["subj"]["resample"] = scans_for_fname(self.inputs.source)
return [{"%s" % (jobtype): einputs[0]}]
def _parse_inputs(self):
"""validate spm realign options if set to None ignore
"""
einputs = super(Normalize, self)._parse_inputs(skip=('jobtype',
'apply_to_files'))
if isdefined(self.inputs.apply_to_files):
inputfiles = deepcopy(self.inputs.apply_to_files)
if isdefined(self.inputs.source):
inputfiles.extend(self.inputs.source)
einputs[0]['subj']['resample'] = scans_for_fnames(inputfiles)
jobtype = self.inputs.jobtype
if jobtype in ['estwrite', 'write']:
if not isdefined(self.inputs.apply_to_files):
if isdefined(self.inputs.source):
einputs[0]['subj']['resample'] = scans_for_fname(self.inputs.source)
return [{'%s' % (jobtype):einputs[0]}]
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
if opt == "channel_files":
# structure have to be recreated, because of some weird traits error
new_channels = []
for channel in val:
new_channel = {}
new_channel["vols"] = scans_for_fname(filename_to_list(channel))
new_channels.append(new_channel)
return new_channels
elif opt == "channel_info":
# structure have to be recreated, because of some weird traits error
new_channels = []
for channel in val:
new_channel = {}
new_channel["biasreg"] = channel[0]
new_channel["biasfwhm"] = channel[1]
new_channel["write"] = [int(channel[2][0]), int(channel[2][1])]
new_channels.append(new_channel)
return new_channels
elif opt == "tissues":
new_tissues = []
for tissue in val:
new_tissue = {}
new_tissue["tpm"] = scans_for_fnames(tissue[0])
new_tissue["ngauss"] = tissue[1]
new_tissue["native"] = [int(tissue[2][0]), int(tissue[2][1])]
new_tissue["warped"] = [int(tissue[3][0]), int(tissue[3][1])]
new_tissues.append(new_tissue)
return new_tissues
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
if opt == 'channel_files':
# structure have to be recreated, because of some weird traits error
new_channels = []
for channel in val:
new_channel = {}
new_channel['vols'] = scans_for_fname(filename_to_list(channel))
new_channels.append(new_channel)
return new_channels
elif opt == 'channel_info':
# structure have to be recreated, because of some weird traits error
new_channels = []
for channel in val:
new_channel = {}
new_channel['biasreg'] = channel[0]
new_channel['biasfwhm'] = channel[1]
new_channel['write'] = [int(channel[2][0]), int(channel[2][1])]
new_channels.append(new_channel)
return new_channels
elif opt == 'tissues':
new_tissues = []
for tissue in val:
new_tissue = {}
new_tissue['tpm'] = scans_for_fnames(tissue[0])
new_tissue['ngauss'] = tissue[1]
new_tissue['native'] = [int(tissue[2][0]), int(tissue[2][1])]
new_tissue['warped'] = [int(tissue[3][0]), int(tissue[3][1])]
new_tissues.append(new_tissue)
return new_tissues
