class LFComputationConnInputSpec(BaseInterfaceInputSpec):
sbj_id = traits.String(desc='subject id', mandatory=True)
sbj_dir = traits.String(exists=True,
desc='Freesurfer main directory',
mandatory=True)
raw_info = traits.Any(desc='raw info', mandatory=True)
raw_fname = traits.String(desc='raw file name', mandatory=True)
spacing = traits.String(desc='spacing to use to setup a source space',
mandatory=False)
aseg = traits.Bool(desc='if true sub structures will be considered',
mandatory=False)
aseg_labels = traits.List(desc='list of substructures in the src space',
mandatory=False)
save_mixed_src_space = traits.Bool(False,
desc='if true save src space',
usedefault=True,
mandatory=False)
class ROI_stats_to_spreadsheetInputSpec(BaseInterfaceInputSpec):
imgFile = File(exists=True, desc='Image list', mandatory=True)
labelImgFile = File(exists=True, desc='Label image list', mandatory=True)
ROI_list = traits.List(
traits.Int(),
minlen=1,
desc=
'list of ROI indices for which stats will be computed (should match the label indices in the label image)',
mandatory=True)
ROI_names = traits.List(
traits.String(),
minlen=1,
desc=
'list of equal size to ROI_list that lists the corresponding ROI names',
mandatory=True)
additionalROIs = traits.List(traits.List(traits.Int()),
desc='list of lists of integers')
additionalROI_names = traits.List(
traits.String(), desc='names corresponding to additional ROIs')
stat = traits.Enum('mean',
'Q1',
'median',
'Q3',
'min',
'max',
desc='one of: mean, Q1, median, Q3, min, max',
mandatory=True)
class FuseSegmentationsInputSpec(FSTraitedSpec):
# required
subject_id = traits.String(argstr='%s',
position=-3,
desc="subject_id being processed")
timepoints = InputMultiPath(
traits.String(),
mandatory=True,
argstr='%s',
position=-2,
desc='subject_ids or timepoints to be processed')
out_file = File(exists=False,
mandatory=True,
position=-1,
desc="output fused segmentation file")
in_segmentations = InputMultiPath(
File(exists=True),
argstr="-a %s",
mandatory=True,
desc="name of aseg file to use (default: aseg.mgz) \
must include the aseg files for all the given timepoints")
in_segmentations_noCC = InputMultiPath(
File(exists=True),
argstr="-c %s",
mandatory=True,
desc="name of aseg file w/o CC labels (default: aseg.auto_noCCseg.mgz) \
must include the corresponding file for all the given timepoints")
in_norms = InputMultiPath(
File(exists=True),
argstr="-n %s",
mandatory=True,
desc="-n <filename> - name of norm file to use (default: norm.mgs) \
must include the corresponding norm file for all given timepoints \
as well as for the current subject")
class WriteXMLFilesInputSpec(BaseInterfaceInputSpec):
# import datetime
# now = datetime.datetime.now()
today_date = 'today' #now.strftime("%Y_%b_%d") # %Y_%b_%dT%Hh%M
type_xml_file = traits.Enum(
'All',
'Def',
'Obj',
usedefault=True,
desc="'Def' to write the deformation xml file. " +
"'Obj' for the object parameter xml file. " +
"'All' to write both files",
mandatory=True)
path = traits.String(
desc=" path where the xml_files folder will be created.")
dkw = traits.Int(desc=' Diffeo Kernel width ', default_value=10)
dkt = traits.String("Exact", desc=' Diffeo Kernel type ', usedefault=True)
dtp = traits.Int(desc=' Diffeo: number of time points', default_value=30)
dsk = traits.Float(desc=' Diffeo: smoothing kernel width ',
default_value=0.5)
dcps = traits.Int(desc="Diffeos: Initial spacing for Control Points",
usedefault=True,
default_value=5)
dcpp = File(
'x',
desc=
"Diffeos: name of a file containing positions of control points. In case of conflict with "
+
"initial-cp-spacing, if a file name is given in initial-cp-position and initial-cp-spacing "
+
"is set, the latter is ignored and control point positions in the file name are used.",
usedefault=True)
dfcp = traits.String('Off',
desc="Diffeos: Freeze the Control Points",
usedefault=True)
dmi = traits.Int(desc="Diffeos : Maximum of descent iterations",
usedefault=True,
default_value=100)
dat = traits.Float(
desc="Diffeos : adaptative tolerence for the gradient descent",
usedefault=True,
default_value=0.00005)
dls = traits.Int(desc="Diffeos: Maximum line search iterations",
usedefault=True,
default_value=20)
ods = traits.ListFloat(
desc="Object: weight of the object in the fidelity-to-data term ",
usedefault=True,
default_value=[0.5])
okw = traits.ListInt(default_value=[3],
desc="Object: Kernel width",
usedefault=True)
ot = traits.List(["NonOrientedSurfaceMesh"],
desc="Object type",
usedefault=True)
xml_diffeo = traits.String(
'parametersDiffeo' + today_date + '.xml',
desc='Name of the xml file containing the diffeo parameters',
usedefault=True)
class EPIDeWarpInputSpec(FSLCommandInputSpec):
mag_file = File(exists=True,
desc='Magnitude file',
argstr='--mag %s', position=0, mandatory=True)
dph_file = File(exists=True,
desc='Phase file assumed to be scaled from 0 to 4095',
argstr='--dph %s', mandatory=True)
exf_file = File(exists=True,
desc='example func volume (or use epi)',
argstr='--exf %s')
epi_file = File(exists=True,
desc='EPI volume to unwarp',
argstr='--epi %s')
tediff = traits.Float(2.46, usedefault=True,
desc='difference in B0 field map TEs',
argstr='--tediff %s')
esp = traits.Float(0.58, desc='EPI echo spacing',
argstr='--esp %s', usedefault=True)
sigma = traits.Int(2, usedefault=True, argstr='--sigma %s',
desc="2D spatial gaussing smoothing \
stdev (default = 2mm)")
vsm = traits.String(genfile=True, desc='voxel shift map',
argstr='--vsm %s')
exfdw = traits.String(desc='dewarped example func volume', genfile=True,
argstr='--exfdw %s')
epidw = traits.String(desc='dewarped epi volume', genfile=False,
argstr='--epidw %s')
tmpdir = traits.String(genfile=True, desc='tmpdir',
argstr='--tmpdir %s')
nocleanup = traits.Bool(True, usedefault=True, desc='no cleanup',
argstr='--nocleanup')
cleanup = traits.Bool(desc='cleanup',
argstr='--cleanup')
class ImportBrainVisionAsciiInputSpec(BaseInterfaceInputSpec):
"""Import brainvision ascii input spec."""
txt_file = File(exists=True,
desc='Ascii text file exported from BrainVision',
mandatory=True)
sample_size = traits.Float(desc='Size (nb of time points) of all samples',
mandatory=True)
sep_label_name = traits.String("",
desc='Separator between electrode name \
(normally a capital letter) and \
contact numbers',
usedefault=True)
repair = traits.Bool(True,
desc='Repair file if behaves strangely (adding \
space sometimes...)',
usedefault=True)
sep = traits.Str(";",
desc="Separator between time points",
usedefault=True)
keep_electrodes = traits.String("",
desc='keep_electrodes',
usedefault=True)
class MaskSurfaceInputSpec(BaseInterfaceInputSpec):
sxfmout = File(exists=True, desc='original surface', mandatory=True)
hemi = traits.String(exists=True, desc='hemisphere', mandatory=True)
fs = traits.String(exists=True, desc='fsaverage', mandatory=True)
freesurferdir = traits.String(exists=True, desc='freesurfer directory', mandatory=True)
sourcelabels = traits.ListInt(exists=True, desc= 'source labels', mandatory=True)
targetlabels = traits.ListInt(exists=True, desc= 'target labels', mandatory=True)
class DiffInputSpec(QI.InputBaseSpec):
# Options
in_file = traits.String(desc='Input file', argstr='--input=%s')
baseline = traits.String(desc='Baseline file', argstr='--baseline=%s')
noise = traits.Float(desc='Added noise level', argstr='--noise=%f')
abs_diff = traits.Bool(desc='Use absolute difference, not relative',
argstr='--abs')
class RenameInputSpec(DynamicTraitedSpec):
in_file = File(exists=True, mandatory=True, desc="file to rename")
keep_ext = traits.Bool(
desc="Keep in_file extension, replace non-extension component of name")
format_string = traits.String(
mandatory=True, desc="Python formatting string for output template")
parse_string = traits.String(
desc="Python regexp parse string to define replacement inputs")
class SubjectSpaceParcellationInputSpec(BaseInterfaceInputSpec):
subject_id = traits.String(desc='subject ID')
subjects_dir = File(exist=True, desc='FreeSufer subject directory')
source_subject = traits.String(desc='subject ID')
source_annot_file = File(exist=True, desc='annotation file to be transformed')
parcellation_directory = File(desc='directory containing the subjects parcellations')
out_directory = File(
exist=True, desc='directory where FreeSurfer output should be directed')
wm = File(exit=True, desc='segmented white matter image')
class AtlasTransformInputSpec(TraitedSpec):
nifti = traits.Any(mandatory=True, desc='input nifti')
atlas_name = traits.String(mandatory=True, desc='atlas name')
bids_dir = traits.String(mandatory=True, desc='atlas name')
resolution = traits.Int(mandatory=False,
desc='resolution (for shen atlas)')
number_of_clusters = traits.Int(mandatory=False, desc='for craddock')
similarity_measure = traits.String(mandatory=False, desc='for craddock')
algorithm = traits.String(mandatory=False, desc='for craddock')
class GetDPInputSpec(CommandLineInputSpec):
problem_file = File(exists=True, argstr="%s", position=1, mandatory=True)
mesh_file = File(exists=True,
mandatory=True,
argstr="-msh %s",
desc="Read mesh (in Gmsh .msh format) from file")
adapatation_constraint_file = File(
exists=True,
argstr="-adapt %s",
desc="Read adaptation constraints from file")
gmsh_read_file = File(
exists=True,
argstr="-gmshread %s",
desc="Read gmsh data (same as GmshRead in resolution)")
results_file = File(exists=True,
argstr="-res %s",
desc="Load processing results from file(s)")
preprocessing_type = traits.String(argstr="-pre %s", desc="Pre-processing")
run_processing = traits.Bool(argstr='-cal', desc="Run processing")
postprocessing_type = traits.String(argstr="-post %s",
desc="Post-processing")
save_results_separately = traits.Bool(
argstr='-split', desc="Save processing results in separate files")
restart_processing = traits.Bool(
argstr='-restart', desc="Resume processing from where it stopped")
solve = traits.String(argstr="-solve %s",
desc="Solve (same as -pre 'Resolution' -cal)")
output_name = traits.String("getdp",
argstr="-name %s",
usedefault=True,
desc="Generic file name")
maximum_interpolation_order = traits.Int(
argstr='-order %d', desc="Restrict maximum interpolation order")
# Linear solver options
binary_output_files = traits.Bool(argstr='-bin',
desc="Create binary output files")
mesh_based_output_files = traits.Bool(
argstr='-v2', desc="Create mesh-based Gmsh output files when possible")
out_table_filenames = traits.List(
traits.Str,
desc="List of table text files generated by the specified problem file. \
If tables are written during the GetDP solving process, this is required for Nipype to find the proper output files for the interface."
)
out_pos_filenames = traits.List(
traits.Str,
desc="List of postprocessing (.pos) files generated by the specified \
problem file. If tables are written during the GetDP solving process, this is required for Nipype to find the proper \
output files for the interface.")
class Aparc2AsegInputSpec(CommandLineInputSpec):
subject = traits.String(desc='subject id', argstr='--s %s', position=0, mandatory=True)
annot = traits.String(desc='name of annot file', argstr='--annot %s', position=1, mandatory=True)
labelwm = traits.Bool(desc='percolate white matter', argstr='--labelwm', position=2)
dmax = traits.Int(desc='depth to percolate', argstr='--wmparc-dmax %d', position=3)
rip = traits.Bool(desc='rip unknown label', argstr='--rip-unknown', position=4)
hypo = traits.Bool(desc='hypointensities as wm', argstr='--hypo-as-wm', position=5)
out_file = traits.File(desc='output aseg file', argstr='--o %s', position=6)
in_lobes_rh = traits.File(desc='input lobar file RH', exists=True)
in_lobes_lh = traits.File(desc='input lobar file LH', exists=True)
class ReferenceInputSpec(MatlabInputSpec):
data_file = traits.File(exists=True,
desc='FieldTrip data structure .mat',
mandatory=True)
ft_path = traits.String('', desc='FieldTrip path', mandatory=True)
channels = traits.String('', desc='channel names')
# updatesens = traits.String('', desc='update sensors (yes or no)', usedefault=True) # noqa
refmethod = traits.String('',
desc='reference type (avg, bipolar)',
mandatory=True) # noqa
class CompIcaInputSpec(BaseInterfaceInputSpec):
"""Input specification for CompIca."""
fif_file = traits.File(exists=True,
desc='raw meg data in fif format',
mandatory=True)
ecg_ch_name = traits.String(desc='name of ecg channel')
eog_ch_name = traits.String(desc='name of eog channel')
n_components = traits.Float(desc='number of ica components')
reject = traits.Dict(desc='rejection parameters', mandatory=False)
class AtlasBREXInputSpec(CommandLineInputSpec):
import os
package_directory = os.path.dirname(os.path.abspath(__file__))
script_atlas_BREX = File('{}/../bash/atlasBREX.sh'.format(
package_directory), usedefault=True,
exists=True,
desc='atlasBREX script',
mandatory=False, position=0, argstr="%s")
NMT_SS_file = File(
exists=True,
desc='Skullstriped version of the template',
mandatory=True, position=1, argstr="-b %s")
NMT_file = File(
exists=True,
desc='template img',
mandatory=True, position=2, argstr="-nb %s")
t1_restored_file = File(
exists=True,
desc='T1 image to map',
mandatory=True, position=3, argstr="-h %s")
f = traits.Float(
0.5, usedefault=True, desc='f', position=4, argstr="-f %f",
mandatory=True)
wrp = traits.String(
"10,10,10", usedefault=True, desc="wrp", position=5, argstr="-wrp %s",
mandatory=True)
reg = traits.Enum(
1, 0, 2, 3, usedefault=True, desc="Method: 0 = FNIRT w/ bending, \
1 (default) = FNIRT membrane-energy regularization, \
2 = ANTs/SyN w/, \
3 = w/o N4Bias",
position=6, argstr="-reg %d", mandatory=True)
msk = traits.String(
"a,0,0", usedefault=True, desc="msk", position=7, argstr="-msk %s",
mandatory=True)
dil = traits.Int(desc="dil", argstr="-dil %d")
nrm = traits.Enum(1, 2, desc="nrm", argstr="-nrm %d")
vox = traits.Int(desc="vox", argstr="-vox %d")
args = traits.String(desc="args", position=-1, argstr=" %s",
mandatory=False)
class ZShimInputSpec(base.InputBaseSpec):
in_file = File(argstr='%s',
mandatory=True,
exists=True,
position=-1,
desc='Input file to fit polynomial to')
zshims = traits.Int(argstr='--zshims=%d', desc='Number of Z-shims')
yshims = traits.Int(argstr='--yshims=%d', desc='Number of Y-shums')
prefix = traits.String(argstr='--out=%s',
desc='Output prefix (default is input filename)')
noiseregion = traits.String(desc='Noise region for background correction',
argstr='--noiseregion=%s')
class SignalExtractionInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True)
time_series_out_file = File(mandatory=True)
correlation_matrix_out_file = File(mandatory=True)
image_parcellation_path = traits.String(mandatory=True)
labels_parcellation_path = traits.Either(
traits.String(),
traits.ArrayOrNone())
mask_img = File(mandatory=True)
tr = traits.Float(mandatory=True)
low_pass = traits.Float(default_value=None)
high_pass = traits.Float(default_value=None)
plot = traits.Bool(default_value=False, mandatory=False)
class NiftiToDatasetInputSpec(BaseInterfaceInputSpec):
nifti_file = File(desc='nifti file to convert to ML dataset format',
exists=True,
mandatory=True)
attributes_file = File(desc='attribute file containing information for ML',
exists=True)
annot_file = File(
desc='annotation file containing parcelation information', exists=True)
subject_id = traits.String(desc='unique subject identifier')
session_id = traits.String(desc='unique session identifier')
ds_file = traits.String('dataset.hdf5',
desc='name of ds file',
usedefault=True)
class RenameInputSpec(DynamicTraitedSpec):
in_file = File(exists=True, mandatory=True, desc="file to rename")
keep_ext = traits.Bool(desc=("Keep in_file extension, replace "
"non-extension component of name"))
format_string = traits.String(mandatory=True,
desc=("Python formatting string for output "
"template"))
parse_string = traits.String(desc=("Python regexp parse string to define "
"replacement inputs"))
use_fullpath = traits.Bool(False,
usedefault=True,
desc="Use full path as input to regex parser")
class InputSpec(InputBaseSpec):
"""
Input Specification for most QUIT tools
"""
json = traits.File(exists=True, desc='JSON Input file', argstr='--json=%s')
subregion = traits.String(
desc='Only process a subregion of the image. Argument should be a string "start_x,start_y,start_z,size_x,size_y,size_z"', argstr='--subregion=%s')
threads = traits.Int(
desc='Use N threads (default=4, 0=hardware limit)', argstr='--threads=%d')
prefix = traits.String(
desc='Add a prefix to output filenames', argstr='--out=%s')
mask_file = File(
desc='Only process voxels within the mask', argstr='--mask=%s')
class ImportMatInputSpec(BaseInterfaceInputSpec):
"""Input specification for ImportMat."""
tsmat_file = traits.File(exists=True,
desc='time series in .mat (matlab format)',
mandatory=True)
data_field_name = traits.String('F',
desc='Name of structure in matlab',
usedefault=True)
good_channels_field_name = traits.String('ChannelFlag',
desc='Boolean structure for\
choosing nodes, name of\
structure in matlab file')
class ClusterInputSpec(BaseInterfaceInputSpec):
in_File = File(exists=True, desc='surface to be clustered', mandatory=True)
hemi = traits.String(exists=True, desc='hemisphere', mandatory=True)
cluster_type = traits.String(
exists=True,
desc='spectral, hiercluster, kmeans, or dbscan',
mandatory=True)
n_clusters = traits.Int(exists=True,
desc='number of clusters',
mandatory=True)
epsilon = traits.Float(exists=True,
desc='epsilon parameter for dbscan',
mandatory=False)
class AtroposN4InputSpec(CommandLineInputSpec):
dimension = traits.Int(3,
default=True,
usedefault=True,
exists=True,
desc='Dimension',
mandatory=True,
position=0,
argstr="-d %d")
brain_file = File(exists=True,
desc='brain_file',
mandatory=True,
position=1,
argstr="-a %s")
brainmask_file = File(exists=True,
desc='brainmask_file',
mandatory=True,
position=2,
argstr="-x %s")
numberOfClasses = traits.Int(3,
usedefault=True,
exists=True,
desc='numberOfClasses',
mandatory=True,
position=3,
argstr="-c %d")
out_pref = traits.String("segment_",
usedefault=True,
desc="output prefix",
mandatory=True,
position=-1,
argstr="-o %s")
priors = traits.Either(traits.List(File(exists=True)),
traits.String(),
desc='template_file',
mandatory=False,
position=4,
argstr="-p %s")
prior_weight = traits.Float(0,
desc='Atropos prior segmentation weight',
mandatory=False,
position=5,
argstr="-w %f")
class PCAInputSpec(QI.InputSpec):
in_file = File(argstr='%s',
mandatory=True,
exists=True,
position=-1,
desc='Input file for PCA denoising')
retain = traits.Int(argstr='--retain=%d', desc='Number of PCs to retain')
mask_file = File(argstr='--mask=%s', exists=True, desc='Mask file')
projections_file = traits.String(argstr='--project=%s',
desc='File to save projections to')
pc_json_file = traits.String(argstr='--save_pcs=%s',
desc='Save Principal Components to JSON file')
out_file = traits.String(argstr='--out=%s',
desc='Name of output file (default is input_pca)')
class SlicesInputSpec(BaseInterfaceInputSpec):
out_file = traits.File(mandatory=True, desc='Output slice image')
base_file = traits.File(exists=True,
mandatory=True,
desc='Image file to slice')
base_map = traits.String('gist_gray',
usedefault=True,
desc='Color map for base image')
base_window = traits.Tuple(minlen=2,
maxlen=2,
desc='Window for base image')
base_scale = traits.Float(1.0,
usedefault=True,
desc='Scaling factor for base image')
base_label = traits.String('',
usedefault=True,
desc='Label for base image color-bar')
mask_file = traits.File(None,
usedefault=True,
mandatory=True,
desc='Mask file')
slice_axis = traits.String('z', usedefault=True, desc='Axis to slice')
slice_lims = traits.Tuple(
(0.1, 0.9),
minlen=2,
maxlen=2,
usedefault=True,
desc='Limits of axis to slice, fraction (low, high) default (0.1, 0.9)'
)
slice_layout = traits.Tuple((3, 6),
minlen=2,
maxlen=2,
usedefault=True,
desc='Slices layout (rows, cols)')
volume = traits.Int(0,
usedefault=True,
desc='Volume for slicing in multi-volume file')
figsize = traits.Tuple(minlen=2, maxlen=2, desc='Output figure size')
preclinical = traits.Bool(False,
usedefault=True,
desc='Data is pre-clinical, fix orientation')
transpose = traits.Bool(False,
usedefault=True,
desc='Transpose slice layout')
bar_pos = traits.String('bottom',
usedefault=True,
desc='Color/Alphabar position')
class ASLInputSpec(QI.FitInputSpec):
# Additional Options
average = traits.Bool(
desc='Average across time-series', argstr='--average')
T1_blood = traits.Float(
desc='Value of blood T1 (default 1.65)', argstr='--blood=%f')
T1_tissue = traits.String(
desc='Path to tissue T1 map (units are seconds)', argstr='--tissue=%s')
PD_map = traits.String(desc='Path to PD weighted image', argstr='--pd=%s')
label_efficiency = traits.Float(
desc='Labelling efficiency, default 0.9', argstr='--alpha=%f')
blood_brain_partition = traits.Float(
desc='Blood-Brain Partition Co-efficient, default 0.9 mL/g', argstr='--lambda=%f')
slicetime = traits.Bool(
desc='Apply slice-timing correction (number of PLDs and slices must match)', argstr='--slicetime')
class PETtoT1LinRegInput(BaseInterfaceInputSpec):
in_target_file = File(position=0, argstr="%s", exists=True, mandatory=True, desc="target image")
in_source_file = File(position=1, argstr="%s", exists=True, mandatory=True, desc="source image")
in_target_mask = File(position=2, argstr="-source_mask %s", exists=True, desc="target mask")
in_source_mask = File(position=3, argstr="-target_mask %s", exists=True, desc="source mask")
init_file_xfm = File(argstr="-init_xfm %s", exists=True, desc="initial transformation (default identity)")
out_file_xfm = File(position=-3, argstr="%s", desc="transformation matrix")
out_file_xfm_invert = File(position=-2, argstr="%s", desc="inverted transformation matrix")
out_file_img = File(position=-1, argstr="%s", desc="resampled image")
lsq = traits.String(desc="Number of parameters to use for transformation")
metric = traits.String(desc="Metric for coregistration", default="mi", use_default=True)
error = traits.String(desc="Error level by which to mis-register PET image")
clobber = traits.Bool(position=-5, argstr="-clobber", usedefault=True, default_value=True, desc="Overwrite output file")
run = traits.Bool(position=-4, argstr="-run", usedefault=True, default_value=True, desc="Run the commands")
verbose = traits.Bool(position=-3, argstr="-verbose", usedefault=True, default_value=True, desc="Write messages indicating progress")
class ParseParamsInputSpec(BaseInterfaceInputSpec):
params = traits.Dict(desc='Dictionnary to tap from')
key = traits.Either(traits.String(),
traits.Tuple(),
desc='which key to tap from')
class tensor2metricInputSpec(MRTrix3BaseInputSpec):
input_file = File(exists=True,
argstr="%s",
mandatory=True,
position=-4,
desc="input DTI")
vector_file = File(
argstr="-vector %s",
mandatory=False,
position=-1,
desc="compute the selected eigenvector(s) of the diffusion tensor.")
fa_file = File(
argstr="-fa %s",
mandatory=False,
position=-1,
desc="compute the fractional anisotropy (FA) of the diffusion tensor.")
adc_file = File(
argstr="-adc %s",
mandatory=False,
position=-1,
desc=
"compute the mean apparent diffusion coefficient (ADC) of the diffusion tensor"
)
modulate = traits.String(argstr="-modulate %s",
mandatory=False,
position=-3,
desc="specify how to modulate (deault = FA)")
num = traits.Int(argstr="-num %d",
mandatory=False,
position=-2,
desc="specify the desired eigenvalue/eigenvector(s)")
