class DesignSpec(TraitedSpec):
regressors = traits.Dict(
traits.Str,
traits.List(traits.Float),
mandatory=True,
)
contrasts = traits.List(
traits.Either(
traits.Tuple(
traits.Str,
traits.Enum("T"),
traits.List(traits.Str),
traits.List(traits.Float),
),
traits.Tuple(
traits.Str,
traits.Enum("F"),
traits.List(
traits.Tuple(
traits.Str,
traits.Enum("T"),
traits.List(traits.Str),
traits.List(traits.Float),
),
),
),
),
mandatory=True,
)
class OverlayInputSpec(FSLCommandInputSpec):
transparency = traits.Bool(desc='make overlay colors semi-transparent',
position=1, argstr='%s', usedefault=True, default_value=True)
out_type = traits.Enum('float', 'int', position=2, usedefault=True, argstr='%s',
desc='write output with float or int')
use_checkerboard = traits.Bool(desc='use checkerboard mask for overlay',
argstr='-c', position=3)
background_image = File(exists=True, position=4, mandatory=True, argstr='%s',
desc='image to use as background')
_xor_inputs = ('auto_thresh_bg', 'full_bg_range', 'bg_thresh')
auto_thresh_bg = traits.Bool(desc='automatically threhsold the background image',
argstr='-a', position=5, xor=_xor_inputs, mandatory=True)
full_bg_range = traits.Bool(desc='use full range of background image',
argstr='-A', position=5, xor=_xor_inputs, mandatory=True)
bg_thresh = traits.Tuple(traits.Float, traits.Float, argstr='%.3f %.3f', position=5,
desc='min and max values for background intensity',
xor=_xor_inputs, mandatory=True)
stat_image = File(exists=True, position=6, mandatory=True, argstr='%s',
desc='statistical image to overlay in color')
stat_thresh = traits.Tuple(traits.Float, traits.Float, position=7, mandatory=True,
desc='min and max values for the statistical overlay',
argstr='%.2f %.2f')
show_negative_stats = traits.Bool(desc='display negative statistics in overlay',
xor=['stat_image2'], argstr='%s', position=8)
stat_image2 = File(exists=True, position=9, xor=['show_negative_stats'], argstr='%s',
desc='second statistical image to overlay in color')
stat_thresh2 = traits.Tuple(traits.Float, traits.Float, position=10,
desc='min and max values for second statistical overlay',
argstr='%.2f %.2f')
out_file = File(desc='combined image volume', position=-1, argstr='%s', genfile=True, hash_files=False)
class PlotTimeSeriesInputSpec(FSLCommandInputSpec):
in_file = traits.Either(File(exists=True), traits.List(File(exists=True)),
mandatory=True, argstr="%s", position=1,
desc="file or list of files with columns of timecourse information")
plot_start = traits.Int(argstr="--start=%d", xor=("plot_range",),
desc="first column from in-file to plot")
plot_finish = traits.Int(argstr="--finish=%d", xor=("plot_range",),
desc="final column from in-file to plot")
plot_range = traits.Tuple(traits.Int, traits.Int, argstr="%s", xor=("plot_start", "plot_finish"),
desc="first and last columns from the in-file to plot")
title = traits.Str(argstr="%s", desc="plot title")
legend_file = File(exists=True, argstr="--legend=%s", desc="legend file")
labels = traits.Either(traits.Str, traits.List(traits.Str),
argstr="%s", desc="label or list of labels")
y_min = traits.Float(argstr="--ymin=%.2f", desc="minumum y value", xor=("y_range",))
y_max = traits.Float(argstr="--ymax=%.2f", desc="maximum y value", xor=("y_range",))
y_range = traits.Tuple(traits.Float, traits.Float, argstr="%s", xor=("y_min", "y_max"),
desc="min and max y axis values")
x_units = traits.Int(argstr="-u %d", usedefault=True, default_value=1,
desc="scaling units for x-axis (between 1 and length of in file)")
plot_size = traits.Tuple(traits.Int, traits.Int, argstr="%s",
desc="plot image height and width")
x_precision = traits.Int(argstr="--precision=%d", desc="precision of x-axis labels")
sci_notation = traits.Bool(argstr="--sci", desc="switch on scientific notation")
out_file = File(argstr="-o %s", genfile=True, desc="image to write", hash_files=False)
class TprojectInputSpec(AFNICommandInputSpec):
in_file = File(desc='input file to 3dTproject', argstr='-input %s',
exists=True, mandatory=True)
out_file = File(name_template="%s_filt", desc='output image file name',
argstr='-prefix %s', name_source="in_file")
ort = File(desc='remove each column in file. Multiple ort are allowed',
argstr='-ort %s', exists=True)
polort = traits.Int(desc='remove polynomials up to degree specified',
argstr='-polort %s')
dsort = File(
desc='Remove the 3D+time time series in the specified dataset',
argstr='-dsort %s', exists=True)
passband = traits.Tuple(
(traits.Float(), traits.Float()), argstr='-passband %f %f',
desc='Remove all frequencies EXCEPT those in the range provided.')
stopband = traits.Tuple(
(traits.Float(), traits.Float()), argstr='-stopband %f %f',
desc='Remove all frequencies in the range provided.')
delta_t = traits.Float(desc='time step for frequency calculations',
argstr='-dt %f')
mask = File(desc='Only operate on voxels nonzero in the provided mask.',
argstr='-mask %s', exists=True, xor=['automask'])
automask = traits.Bool(desc='the program will generate the mask to use for'
'calculations.', argstr='-automask', xor=['mask'])
blur = traits.Float(desc='Blur (inside the mask only) with a filter that'
'has width (FWHM) of fff millimeters.',
argstr='-blur %f')
normalize = traits.Bool(desc='Normalize each output time series to have'
'sum of squares = 1. This is the LAST operation.',
argstr='-norm')
class TemplateDimensionsOutputSpec(TraitedSpec):
t1w_valid_list = OutputMultiPath(exists=True, desc='valid T1w images')
target_zooms = traits.Tuple(traits.Float, traits.Float, traits.Float,
desc='Target zoom information')
target_shape = traits.Tuple(traits.Int, traits.Int, traits.Int,
desc='Target shape information')
out_report = File(exists=True, desc='conformation report')
class FMRISummaryInputSpec(BaseInterfaceInputSpec):
in_func = File(
exists=True,
mandatory=True,
desc="input BOLD time-series (4D file) or dense timeseries CIFTI",
)
in_mask = File(exists=True, desc="3D brain mask")
in_segm = File(exists=True, desc="resampled segmentation")
confounds_file = File(exists=True, desc="BIDS' _confounds.tsv file")
str_or_tuple = traits.Either(
traits.Str,
traits.Tuple(traits.Str, traits.Either(None, traits.Str)),
traits.Tuple(traits.Str, traits.Either(None, traits.Str),
traits.Either(None, traits.Str)),
)
confounds_list = traits.List(
str_or_tuple,
minlen=1,
desc="list of headers to extract from the confounds_file",
)
tr = traits.Either(None,
traits.Float,
usedefault=True,
desc="the repetition time")
class DARTELExistingTemplateInputSpec(SPMCommandInputSpec):
image_files = traits.List(traits.List(File(exists=True)),
desc="A list of files to be segmented",
field='warp1.images', copyfile=False, mandatory=True)
regularization_form = traits.Enum('Linear', 'Membrane', 'Bending',
field='warp1.settings.rform',
desc='Form of regularization energy term')
iteration_parameters = traits.List(traits.Tuple(traits.Range(1, 10),
traits.Tuple(traits.Float,
traits.Float,
traits.Float),
traits.Range(0, 9),
traits.File(exists=True)),
minlen=3,
maxlen=12,
mandatory=True,
field='warp1.settings.param',
desc="""List of tuples for each iteration
- Inner iterations
- Regularization parameters
- Time points for deformation model
- DARTEL template
""")
optimization_parameters = traits.Tuple(traits.Float, traits.Range(1, 8),
traits.Range(1, 8),
field='warp1.settings.optim',
desc="""Optimization settings a tuple
- LM regularization
- cycles of multigrid solver
- relaxation iterations
""")
class DataSinkInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
base_directory = Directory(
desc='Path to the base directory for storing data.')
container = traits.Str(
desc='Folder within base directory in which to store output')
parameterization = traits.Bool(
True, usedefault=True, desc='store output in parametrized structure')
strip_dir = Directory(desc='path to strip out of filename')
substitutions = InputMultiPath(traits.Tuple(traits.Str, traits.Str),
desc=('List of 2-tuples reflecting string '
'to substitute and string to replace '
'it with'))
regexp_substitutions = InputMultiPath(
traits.Tuple(traits.Str, traits.Str),
desc=('List of 2-tuples reflecting a pair '
'of a Python regexp pattern and a '
'replacement string. Invoked after '
'string `substitutions`'))
_outputs = traits.Dict(traits.Str, value={}, usedefault=True)
remove_dest_dir = traits.Bool(
False, usedefault=True, desc='remove dest directory when copying dirs')
def __setattr__(self, key, value):
if key not in self.copyable_trait_names():
if not isdefined(value):
super(DataSinkInputSpec, self).__setattr__(key, value)
self._outputs[key] = value
else:
if key in self._outputs:
self._outputs[key] = value
super(DataSinkInputSpec, self).__setattr__(key, value)
class FormatHMCParamInputSpec(BaseInterfaceInputSpec):
translations = traits.List(traits.Tuple(traits.Float, traits.Float, traits.Float),
mandatory=True, desc='three translations in mm')
rot_angles = traits.List(traits.Tuple(traits.Float, traits.Float, traits.Float),
mandatory=True, desc='three rotations in rad')
fmt = traits.Enum('confounds', 'movpar_file', usedefault=True,
desc='type of resulting file')
class _ConformInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="Input image")
target_zooms = traits.Tuple(
traits.Float, traits.Float, traits.Float, desc="Target zoom information"
)
target_shape = traits.Tuple(
traits.Int, traits.Int, traits.Int, desc="Target shape information"
)
class SQLiteGrabberInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
database_file = File(exists=True, mandatory=True)
table_name = traits.Str(mandatory=True)
columns = traits.List(traits.Str, mandatory=True)
constraints = traits.List(traits.Tuple(traits.Str, traits.Either(traits.Str,
traits.List(traits.Str))),
minlen=1, desc="The list of column/value pairs for WHERE creation")
distinct = traits.Bool(default=False, usedefault=True)
orderby = traits.List(traits.Tuple(traits.Str, traits.Enum(('ASC', 'DESC'))),
desc='List of tuples with column and order direction')
class GrappaOutputSpec(BaseMatlabOutputSpec):
channels_dir = Directory(exists=True)
main_field_strength = traits.Float(
desc="The orientation of the scanners main magnetic field"),
main_field_orient = traits.Tuple(
(traits.Int, traits.Int, traits.Int),
desc="The orientation of the scanners main magnetic field")
echo_times = traits.Tuple(traits.Int, desc="The echo times in seconds")
larmor_freq = traits.Float(desc="The larmor frequency of the scanner")
class RepositorySinkOutputSpec(RepositorySinkSpec):
files = traits.List(PATH_TRAIT, desc='Output filesets')
fields = traits.List(traits.Tuple(traits.Str, FIELD_TRAIT),
desc='Output fields')
combined = traits.List(traits.Either(PATH_TRAIT,
traits.Tuple(traits.Str,
FIELD_TRAIT)),
desc="Combined fileset and field outputs")
class ConformInputSpec(BaseInterfaceInputSpec):
in_file = File(mandatory=True, desc='Input image')
target_zooms = traits.Tuple(traits.Float,
traits.Float,
traits.Float,
desc='Target zoom information')
target_shape = traits.Tuple(traits.Int,
traits.Int,
traits.Int,
desc='Target shape information')
deoblique_header = traits.Bool(False, usedfault=True)
class _TemplateDimensionsOutputSpec(TraitedSpec):
t1w_valid_list = OutputMultiObject(exists=True, desc="valid T1w images")
target_zooms = traits.Tuple(traits.Float,
traits.Float,
traits.Float,
desc="Target zoom information")
target_shape = traits.Tuple(traits.Int,
traits.Int,
traits.Int,
desc="Target shape information")
out_report = File(exists=True, desc="conformation report")
class ImageInfoOutputSpec(TraitedSpec):
info = traits.Any(desc="output of mri_info")
out_file = File(exists=True, desc="text file with image information")
data_type = traits.String(desc="image data type")
file_format = traits.String(desc="file format")
TE = traits.String(desc="echo time (msec)")
TR = traits.String(desc="repetition time(msec)")
TI = traits.String(desc="inversion time (msec)")
dimensions = traits.Tuple(desc="image dimensions (voxels)")
vox_sizes = traits.Tuple(desc="voxel sizes (mm)")
orientation = traits.String(desc="image orientation")
ph_enc_dir = traits.String(desc="phase encode direction")
class Level1DesignInputSpec(BaseInterfaceInputSpec):
interscan_interval = traits.Float(mandatory=True,
desc='Interscan interval (in secs)')
session_info = traits.Any(mandatory=True,
desc=('Session specific information generated '
'by ``modelgen.SpecifyModel``'))
bases = traits.Either(
traits.Dict(traits.Enum('dgamma'),
traits.Dict(traits.Enum('derivs'), traits.Bool)),
traits.Dict(
traits.Enum('gamma'),
traits.Dict(traits.Enum('derivs', 'gammasigma', 'gammadelay'))),
traits.Dict(traits.Enum('none'), traits.Dict()),
mandatory=True,
desc=("name of basis function and options e.g., "
"{'dgamma': {'derivs': True}}"))
orthogonalization = traits.Dict(
traits.Int,
traits.Dict(traits.Int, traits.Either(traits.Bool, traits.Int)),
mandatory=False,
default={},
)
model_serial_correlations = traits.Bool(
desc="Option to model serial correlations using an \
autoregressive estimator (order 1). Setting this option is only \
useful in the context of the fsf file. If you set this to False, you need to \
repeat this option for FILMGLS by setting autocorr_noestimate to True",
mandatory=True)
contrasts = traits.List(
traits.Either(
traits.Tuple(traits.Str, traits.Enum('T'), traits.List(traits.Str),
traits.List(traits.Float)),
traits.Tuple(traits.Str, traits.Enum('T'), traits.List(traits.Str),
traits.List(traits.Float), traits.List(traits.Float)),
traits.Tuple(
traits.Str, traits.Enum('F'),
traits.List(
traits.Either(
traits.Tuple(traits.Str, traits.Enum('T'),
traits.List(traits.Str),
traits.List(traits.Float)),
traits.Tuple(traits.Str, traits.Enum('T'),
traits.List(traits.Str),
traits.List(traits.Float),
traits.List(traits.Float)))))),
desc="List of contrasts with each contrast being a list of the form - \
[('name', 'stat', [condition list], [weight list], [session list])]. if \
session list is None or not provided, all sessions are used. For F \
contrasts, the condition list should contain previously defined \
T-contrasts.")
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 DWIDenoiseInputSpec(MRTrix3BaseInputSpec, SeriesPreprocReportInputSpec):
in_file = File(exists=True,
argstr='%s',
position=-2,
mandatory=True,
desc='input DWI image')
mask = File(exists=True, argstr='-mask %s', position=1, desc='mask image')
extent = traits.Tuple(
(traits.Int, traits.Int, traits.Int),
argstr='-extent %d,%d,%d',
desc='set the window size of the denoising filter. (default = 5,5,5)')
noise_image = File(argstr='-noise %s',
name_template='%s_noise.nii.gz',
name_source=['in_file'],
keep_extension=False,
desc='the output noise map')
out_file = File(name_template='%s_denoised.nii.gz',
name_source=['in_file'],
keep_extension=False,
argstr='%s',
position=-1,
desc='the output denoised DWI image')
out_report = File('dwidenoise_report.svg',
usedefault=True,
desc='filename for the visual report')
class DWIDenoiseInputSpec(MRTrix3BaseInputSpec):
in_file = File(
exists=True,
argstr='%s',
position=-2,
mandatory=True,
desc='input DWI image')
mask = File(
exists=True,
argstr='-mask %s',
position=1,
desc='mask image')
extent = traits.Tuple(
(traits.Int, traits.Int, traits.Int),
argstr='-extent %d,%d,%d',
desc='set the window size of the denoising filter. (default = 5,5,5)')
noise = File(
argstr='-noise %s',
name_template='%s_noise',
name_source=['in_file'],
keep_extension=True,
desc='the output noise map')
out_file = File(
name_template='%s_denoised',
name_source=['in_file'],
keep_extension=True,
argstr='%s',
position=-1,
desc='the output denoised DWI image')
class DWIDenoiseInputSpec(MRTrix3BaseInputSpec):
in_file = File(
exists=True,
argstr="%s",
position=-2,
mandatory=True,
desc="input DWI image",
)
mask = File(exists=True, argstr="-mask %s", position=1, desc="mask image")
extent = traits.Tuple(
(traits.Int, traits.Int, traits.Int),
argstr="-extent %d,%d,%d",
desc="set the window size of the denoising filter. (default = 5,5,5)",
)
noise = File(
argstr="-noise %s",
name_template="%s_noise",
name_source=["in_file"],
keep_extension=True,
desc="the output noise map",
)
out_file = File(
argstr="%s",
name_template="%s_denoised",
name_source=["in_file"],
keep_extension=True,
position=-1,
desc="the output denoised DWI image",
)
class MRCropInputSpec(CommandLineInputSpec):
in_file = File(exists=True,
argstr='%s',
mandatory=True,
position=-2,
desc="Diffusion weighted images with graident info")
out_file = File(genfile=True,
argstr='%s',
position=-1,
desc="Extracted DW or b-zero images")
axis = traits.Tuple(
traits.Int(desc="index"), # @UndefinedVariable
traits.Int(desc="start"), # @UndefinedVariable
traits.Int(desc='end'), # @UndefinedVariable
mandatory=False,
argstr="-axis %s %s %s", # @UndefinedVariable @IgnorePep8
desc=("crop the input image in the provided axis"))
mask = File(mandatory=False,
exists=True,
argstr="-mask %s",
desc=("Crop the input image according to the spatial extent of"
" a mask image"))
quiet = traits.Bool(mandatory=False,
argstr="-quiet",
desc="Don't display output during operation")
class InputSubjectsInputSpec(TraitedSpec):
subject_id = traits.Str(mandatory=True, desc=("The subject ID"))
prereq_reports = traits.List(
traits.Tuple(traits.Str, traits.Str),
value=[],
desc=("A list of session reports from all prerequisite pipelines"))
class _BSplineApproxInputSpec(BaseInterfaceInputSpec):
in_data = File(exists=True, mandatory=True, desc="path to a fieldmap")
in_mask = File(exists=True, desc="path to a brain mask")
bs_spacing = InputMultiObject(
[DEFAULT_ZOOMS_MM],
traits.Tuple(traits.Float, traits.Float, traits.Float),
usedefault=True,
desc="spacing between B-Spline control points",
)
ridge_alpha = traits.Float(
0.01, usedefault=True, desc="controls the regularization"
)
recenter = traits.Enum(
"mode",
"median",
"mean",
False,
usedefault=True,
desc="strategy to recenter the distribution of the input fieldmap",
)
extrapolate = traits.Bool(
True,
usedefault=True,
desc="generate a field, extrapolated outside the brain mask",
)
class NewImageInputSpec(QI.InputBaseSpec):
# Options
img_size = traits.List(minsize=2,
maxsize=4,
mandatory=True,
desc='Image size',
argstr='--size=%s',
sep=',')
voxel_spacing = traits.Float(desc='Voxel spacing', argstr='--spacing=%f')
origin = traits.Float(desc='Image origin', argstr='--origin=%f')
fill = traits.Float(desc='Fill with value', argstr='--fill=%f')
grad_dim = traits.Int(desc='Fill with gradient along dimension',
argstr='--grad_dim=%d')
grad_vals = traits.Tuple(desc='Gradient start/end values',
argstr='--grad_vals=%f,%f')
grad_steps = traits.Int(desc='Gradient in N discrete steps',
argstr='--steps=%s')
wrap = traits.Float(desc='Wrap image values at the given value',
argstr='--wrap=%f')
# Output file
out_file = traits.File(desc='Output file',
exists=False,
position=-1,
argstr='%s',
mandatory=True)
class _CompCorVariancePlotInputSpec(BaseInterfaceInputSpec):
metadata_files = traits.List(
File(exists=True),
mandatory=True,
desc="List of files containing component "
"metadata",
)
metadata_sources = traits.List(
traits.Str,
desc="List of names of decompositions "
"(e.g., aCompCor, tCompCor) yielding "
"the arguments in `metadata_files`",
)
variance_thresholds = traits.Tuple(
traits.Float(0.5),
traits.Float(0.7),
traits.Float(0.9),
usedefault=True,
desc="Levels of explained variance to include in "
"plot",
)
out_file = traits.Either(None,
File,
value=None,
usedefault=True,
desc="Path to save plot")
class MRPadInputSpec(CommandLineInputSpec):
in_file = File(exists=True,
argstr='%s',
mandatory=True,
position=-2,
desc="Diffusion weighted images with graident info")
out_file = File(genfile=True,
argstr='%s',
position=-1,
desc="Extracted DW or b-zero images")
axis = traits.Tuple(
traits.Int(desc="index"), # @UndefinedVariable
traits.Int(desc="lower"), # @UndefinedVariable
traits.Int(desc='upper'), # @UndefinedVariable
mandatory=False,
argstr="-axis %s %s %s", # @UndefinedVariable @IgnorePep8
desc=("Pad the input image along the provided axis (defined by index)."
"Lower and upper define the number of voxels to add to the lower"
" and upper bounds of the axis"))
uniform = File(mandatory=False,
exists=True,
argstr="-uniform %s",
desc=("Pad the input image by a uniform number of voxels on"
" all sides (in 3D)"))
quiet = traits.Bool(mandatory=False,
argstr="-quiet",
desc="Don't display output during operation")
class SQLiteGrabberOutputSpec(TraitedSpec):
"""
This class represents a...
:param TraitedSpec:
"""
results = traits.List(traits.Tuple(), desc='Results of query')
query = traits.Str(desc='Query sent to database')
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 PipelineReportInputSpec(TraitedSpec):
subject_session_pairs = traits.List(
traits.Tuple(traits.Str, traits.Str),
desc="Subject & session pairs from per-session sink")
subjects = traits.List(traits.Str, desc="Subjects from per-subject sink")
visits = traits.List(traits.Str, desc="Visits from per_visit sink")
project = traits.Str(desc="Project ID from per-project sink")
