# -*- coding: utf8 -*-
"""Autogenerated file - DO NOT EDIT
If you spot a bug, please report it on the mailing list and/or change the generator."""

from nipype.interfaces.base import CommandLine, CommandLineInputSpec, SEMLikeCommandLine, TraitedSpec, File, Directory, traits, isdefined, InputMultiPath, OutputMultiPath
import os


class JistLaminarVolumetricLayeringInputSpec(CommandLineInputSpec):
    inInner = File(desc="Inner Distance Image (GM/WM boundary)", exists=True, argstr="--inInner %s")
    inOuter = File(desc="Outer Distance Image (CSF/GM boundary)", exists=True, argstr="--inOuter %s")
    inNumber = traits.Int(desc="Number of layers", argstr="--inNumber %d")
    inMax = traits.Int(desc="Max iterations for narrow band evolution", argstr="--inMax %d")
    inMin = traits.Float(desc="Min change ratio for narrow band evolution", argstr="--inMin %f")
    inLayering = traits.Enum("distance-preserving", "volume-preserving", desc="Layering method", argstr="--inLayering %s")
    inLayering2 = traits.Enum("outward", "inward", desc="Layering direction", argstr="--inLayering2 %s")
    incurvature = traits.Int(desc="curvature approximation scale (voxels)", argstr="--incurvature %d")
    inratio = traits.Float(desc="ratio smoothing kernel size (voxels)", argstr="--inratio %f")
    inpresmooth = traits.Enum("true", "false", desc="pre-smooth cortical surfaces", argstr="--inpresmooth %s")
    inTopology = traits.Enum("26/6", "6/26", "18/6", "6/18", "6/6", "wcs", "wco", "no", desc="Topology", argstr="--inTopology %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outContinuous = traits.Either(traits.Bool, File(), hash_files=False, desc="Continuous depth measurement", argstr="--outContinuous %s")
    outDiscrete = traits.Either(traits.Bool, File(), hash_files=False, desc="Discrete sampled layers", argstr="--outDiscrete %s")
    outLayer = traits.Either(traits.Bool, File(), hash_files=False, desc="Layer boundary surfaces", argstr="--outLayer %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistLaminarVolumetricLayeringOutputSpec(TraitedSpec):
    outContinuous = File(desc="Continuous depth measurement", exists=True)
    outDiscrete = File(desc="Discrete sampled layers", exists=True)
    outLayer = File(desc="Layer boundary surfaces", exists=True)


class JistLaminarVolumetricLayering(SEMLikeCommandLine):
    """title: Volumetric Layering

category: Developer Tools

description: Builds a continuous layering of the cortex following distance-preserving or volume-preserving models of cortical folding.
Waehnert MD, Dinse J, Weiss M, Streicher MN, Waehnert P, Geyer S, Turner R, Bazin PL, Anatomically motivated modeling of cortical laminae, Neuroimage, 2013.

version: 3.0.RC

contributor: Miriam Waehnert (waehnert@cbs.mpg.de) http://www.cbs.mpg.de/

"""

    input_spec = JistLaminarVolumetricLayeringInputSpec
    output_spec = JistLaminarVolumetricLayeringOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarVolumetricLayering "
    _outputs_filenames = {'outContinuous':'outContinuous.nii','outLayer':'outLayer.nii','outDiscrete':'outDiscrete.nii'}
    _redirect_x = True


class JistBrainMgdmSegmentationInputSpec(CommandLineInputSpec):
    inMP2RAGE = File(desc="MP2RAGE T1 Map Image", exists=True, argstr="--inMP2RAGE %s")
    inMP2RAGE2 = File(desc="MP2RAGE T1-weighted Image", exists=True, argstr="--inMP2RAGE2 %s")
    inPV = File(desc="PV / Dura Image", exists=True, argstr="--inPV %s")
    inMPRAGE = File(desc="MPRAGE T1-weighted Image", exists=True, argstr="--inMPRAGE %s")
    inFLAIR = File(desc="FLAIR Image", exists=True, argstr="--inFLAIR %s")
    inAtlas = File(desc="Atlas file", exists=True, argstr="--inAtlas %s")
    inData = traits.Float(desc="Data weight", argstr="--inData %f")
    inCurvature = traits.Float(desc="Curvature weight", argstr="--inCurvature %f")
    inPosterior = traits.Float(desc="Posterior scale (mm)", argstr="--inPosterior %f")
    inMax = traits.Int(desc="Max iterations", argstr="--inMax %d")
    inMin = traits.Float(desc="Min change", argstr="--inMin %f")
    inSteps = traits.Int(desc="Steps", argstr="--inSteps %d")
    inTopology = traits.Enum("26/6", "6/26", "18/6", "6/18", "6/6", "wcs", "wco", "no", desc="Topology", argstr="--inTopology %s")
    inCompute = traits.Enum("true", "false", desc="Compute posteriors", argstr="--inCompute %s")
    inAdjust = traits.Enum("true", "false", desc="Adjust intensity priors", argstr="--inAdjust %s")
    inOutput = traits.Enum("segmentation", "memberships", desc="Output images", argstr="--inOutput %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outSegmented = traits.Either(traits.Bool, File(), hash_files=False, desc="Segmented Brain Image", argstr="--outSegmented %s")
    outLevelset = traits.Either(traits.Bool, File(), hash_files=False, desc="Levelset Boundary Image", argstr="--outLevelset %s")
    outPosterior2 = traits.Either(traits.Bool, File(), hash_files=False, desc="Posterior Maximum Memberships (4D)", argstr="--outPosterior2 %s")
    outPosterior3 = traits.Either(traits.Bool, File(), hash_files=False, desc="Posterior Maximum Labels (4D)", argstr="--outPosterior3 %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistBrainMgdmSegmentationOutputSpec(TraitedSpec):
    outSegmented = File(desc="Segmented Brain Image", exists=True)
    outLevelset = File(desc="Levelset Boundary Image", exists=True)
    outPosterior2 = File(desc="Posterior Maximum Memberships (4D)", exists=True)
    outPosterior3 = File(desc="Posterior Maximum Labels (4D)", exists=True)


class JistBrainMgdmSegmentation(SEMLikeCommandLine):
    """title: MGDM Whole Brain Segmentation

category: Developer Tools

description: Estimate brain structures from an atlas for a MRI dataset (multiple input combinations are possible).

version: 2.0.RC

"""

    input_spec = JistBrainMgdmSegmentationInputSpec
    output_spec = JistBrainMgdmSegmentationOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainMgdmSegmentation "
    _outputs_filenames = {'outSegmented':'outSegmented.nii','outPosterior2':'outPosterior2.nii','outPosterior3':'outPosterior3.nii','outLevelset':'outLevelset.nii'}
    _redirect_x = True


class JistLaminarProfileGeometryInputSpec(CommandLineInputSpec):
    inProfile = File(desc="Profile Surface Image", exists=True, argstr="--inProfile %s")
    incomputed = traits.Enum("thickness", "curvedness", "shape_index", "mean_curvature", "gauss_curvature", "profile_length", "profile_curvature", "profile_torsion", desc="computed measure", argstr="--incomputed %s")
    inregularization = traits.Enum("none", "Gaussian", desc="regularization", argstr="--inregularization %s")
    insmoothing = traits.Float(desc="smoothing parameter", argstr="--insmoothing %f")
    inoutside = traits.Float(desc="outside extension (mm)", argstr="--inoutside %f")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outResult = traits.Either(traits.Bool, File(), hash_files=False, desc="Result", argstr="--outResult %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistLaminarProfileGeometryOutputSpec(TraitedSpec):
    outResult = File(desc="Result", exists=True)


class JistLaminarProfileGeometry(SEMLikeCommandLine):
    """title: Profile Geometry

category: Developer Tools

description: Compute various geometric quantities for a cortical layers.

version: 3.0.RC

"""

    input_spec = JistLaminarProfileGeometryInputSpec
    output_spec = JistLaminarProfileGeometryOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarProfileGeometry "
    _outputs_filenames = {'outResult':'outResult.nii'}
    _redirect_x = True


class JistLaminarProfileCalculatorInputSpec(CommandLineInputSpec):
    inIntensity = File(desc="Intensity Profile Image", exists=True, argstr="--inIntensity %s")
    inMask = File(desc="Mask Image (opt, 3D or 4D)", exists=True, argstr="--inMask %s")
    incomputed = traits.Enum("mean", "stdev", "skewness", "kurtosis", desc="computed statistic", argstr="--incomputed %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outResult = traits.Either(traits.Bool, File(), hash_files=False, desc="Result", argstr="--outResult %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistLaminarProfileCalculatorOutputSpec(TraitedSpec):
    outResult = File(desc="Result", exists=True)


class JistLaminarProfileCalculator(SEMLikeCommandLine):
    """title: Profile Calculator

category: Developer Tools

description: Compute various moments for intensities mapped along a cortical profile.

version: 3.0.RC

"""

    input_spec = JistLaminarProfileCalculatorInputSpec
    output_spec = JistLaminarProfileCalculatorOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarProfileCalculator "
    _outputs_filenames = {'outResult':'outResult.nii'}
    _redirect_x = True


class MedicAlgorithmN3InputSpec(CommandLineInputSpec):
    inInput = File(desc="Input Volume", exists=True, argstr="--inInput %s")
    inSignal = traits.Float(desc="Default = min + 1, Values at less than threshold are treated as part of the background", argstr="--inSignal %f")
    inMaximum = traits.Int(desc="Maximum number of Iterations", argstr="--inMaximum %d")
    inEnd = traits.Float(desc="Usually 0.01-0.00001, The measure used to terminate the iterations is the coefficient of variation of change in field estimates between successive iterations.", argstr="--inEnd %f")
    inField = traits.Float(desc="Characteristic distance over which the field varies. The distance between adjacent knots in bspline fitting with at least 4 knots going in every dimension. The default in the dialog is one third the distance (resolution * extents) of the smallest dimension.", argstr="--inField %f")
    inSubsample = traits.Float(desc="Usually between 1-32, The factor by which the data is subsampled to a lower resolution in estimating the slowly varying non-uniformity field. Reduce sampling in the finest sampling direction by the shrink factor.", argstr="--inSubsample %f")
    inKernel = traits.Float(desc="Usually between 0.05-0.50, Width of deconvolution kernel used to sharpen the histogram. Larger values give faster convergence while smaller values give greater accuracy.", argstr="--inKernel %f")
    inWeiner = traits.Float(desc="Usually between 0.0-1.0", argstr="--inWeiner %f")
    inAutomatic = traits.Enum("true", "false", desc="If true determines the threshold by histogram analysis. If true a VOI cannot be used and the input threshold is ignored.", argstr="--inAutomatic %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outInhomogeneity = traits.Either(traits.Bool, File(), hash_files=False, desc="Inhomogeneity Corrected Volume", argstr="--outInhomogeneity %s")
    outInhomogeneity2 = traits.Either(traits.Bool, File(), hash_files=False, desc="Inhomogeneity Field", argstr="--outInhomogeneity2 %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class MedicAlgorithmN3OutputSpec(TraitedSpec):
    outInhomogeneity = File(desc="Inhomogeneity Corrected Volume", exists=True)
    outInhomogeneity2 = File(desc="Inhomogeneity Field", exists=True)


class MedicAlgorithmN3(SEMLikeCommandLine):
    """title: N3 Correction

category: Developer Tools

description: Non-parametric Intensity Non-uniformity Correction, N3, originally by J.G. Sled.

version: 1.8.R

"""

    input_spec = MedicAlgorithmN3InputSpec
    output_spec = MedicAlgorithmN3OutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.classification.MedicAlgorithmN3 "
    _outputs_filenames = {'outInhomogeneity2':'outInhomogeneity2.nii','outInhomogeneity':'outInhomogeneity.nii'}
    _redirect_x = True


class JistLaminarROIAveragingInputSpec(CommandLineInputSpec):
    inIntensity = File(desc="Intensity Profile Image", exists=True, argstr="--inIntensity %s")
    inROI = File(desc="ROI Mask", exists=True, argstr="--inROI %s")
    inROI2 = traits.Str(desc="ROI Name", argstr="--inROI2 %s")
    inMask = File(desc="Mask Image (opt, 3D or 4D)", exists=True, argstr="--inMask %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outROI3 = traits.Either(traits.Bool, File(), hash_files=False, desc="ROI Average", argstr="--outROI3 %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistLaminarROIAveragingOutputSpec(TraitedSpec):
    outROI3 = File(desc="ROI Average", exists=True)


class JistLaminarROIAveraging(SEMLikeCommandLine):
    """title: Profile ROI Averaging

category: Developer Tools

description: Compute an average profile over a given ROI.

version: 3.0.RC

"""

    input_spec = JistLaminarROIAveragingInputSpec
    output_spec = JistLaminarROIAveragingOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarROIAveraging "
    _outputs_filenames = {'outROI3':'outROI3'}
    _redirect_x = True


class MedicAlgorithmLesionToadsInputSpec(CommandLineInputSpec):
    inT1_MPRAGE = File(desc="T1_MPRAGE Image", exists=True, argstr="--inT1_MPRAGE %s")
    inT1_SPGR = File(desc="T1_SPGR Image", exists=True, argstr="--inT1_SPGR %s")
    inFLAIR = File(desc="FLAIR Image", exists=True, argstr="--inFLAIR %s")
    inAtlas = traits.Enum("With Lesion", "No Lesion", desc="Atlas to Use", argstr="--inAtlas %s")
    inOutput = traits.Enum("hard segmentation", "hard segmentation+memberships", "cruise inputs", "dura removal inputs", desc="Output images", argstr="--inOutput %s")
    inOutput2 = traits.Enum("true", "false", desc="Output the hard classification using maximum membership (not neceesarily topologically correct)", argstr="--inOutput2 %s")
    inCorrect = traits.Enum("true", "false", desc="Correct MR field inhomogeneity.", argstr="--inCorrect %s")
    inOutput3 = traits.Enum("true", "false", desc="Output the estimated inhomogeneity field", argstr="--inOutput3 %s")
    inAtlas2 = File(desc="Atlas File - With Lesions", exists=True, argstr="--inAtlas2 %s")
    inAtlas3 = File(desc="Atlas File - No Lesion - T1 and FLAIR", exists=True, argstr="--inAtlas3 %s")
    inAtlas4 = File(desc="Atlas File - No Lesion - T1 Only", exists=True, argstr="--inAtlas4 %s")
    inMaximum = traits.Int(desc="Maximum distance from the interventricular WM boundary to downweight the lesion membership to avoid false postives", argstr="--inMaximum %d")
    inMaximum2 = traits.Int(desc="Maximum Ventircle Distance", argstr="--inMaximum2 %d")
    inMaximum3 = traits.Int(desc="Maximum InterVentricular Distance", argstr="--inMaximum3 %d")
    inInclude = traits.Enum("true", "false", desc="Include lesion in WM class in hard classification", argstr="--inInclude %s")
    inAtlas5 = traits.Float(desc="Controls the effect of the statistical atlas on the segmentation", argstr="--inAtlas5 %f")
    inSmooting = traits.Float(desc="Controls the effect of neighberhood voxels on the membership", argstr="--inSmooting %f")
    inMaximum4 = traits.Float(desc="Maximum amount of relative change in the energy function considered as the convergence criteria", argstr="--inMaximum4 %f")
    inMaximum5 = traits.Int(desc="Maximum iterations", argstr="--inMaximum5 %d")
    inAtlas6 = traits.Enum("rigid", "multi_fully_affine", desc="Atlas alignment", argstr="--inAtlas6 %s")
    inConnectivity = traits.Enum("(26,6)", "(6,26)", "(6,18)", "(18,6)", desc="Connectivity (foreground,background)", argstr="--inConnectivity %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outHard = traits.Either(traits.Bool, File(), hash_files=False, desc="Hard segmentation", argstr="--outHard %s")
    outHard2 = traits.Either(traits.Bool, File(), hash_files=False, desc="Hard segmentationfrom memberships", argstr="--outHard2 %s")
    outInhomogeneity = traits.Either(traits.Bool, File(), hash_files=False, desc="Inhomogeneity Field", argstr="--outInhomogeneity %s")
    outMembership = traits.Either(traits.Bool, File(), hash_files=False, desc="Membership Functions", argstr="--outMembership %s")
    outLesion = traits.Either(traits.Bool, File(), hash_files=False, desc="Lesion Segmentation", argstr="--outLesion %s")
    outSulcal = traits.Either(traits.Bool, File(), hash_files=False, desc="Sulcal CSF Membership", argstr="--outSulcal %s")
    outCortical = traits.Either(traits.Bool, File(), hash_files=False, desc="Cortical GM Membership", argstr="--outCortical %s")
    outFilled = traits.Either(traits.Bool, File(), hash_files=False, desc="Filled WM Membership", argstr="--outFilled %s")
    outWM = traits.Either(traits.Bool, File(), hash_files=False, desc="WM Mask", argstr="--outWM %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class MedicAlgorithmLesionToadsOutputSpec(TraitedSpec):
    outHard = File(desc="Hard segmentation", exists=True)
    outHard2 = File(desc="Hard segmentationfrom memberships", exists=True)
    outInhomogeneity = File(desc="Inhomogeneity Field", exists=True)
    outMembership = File(desc="Membership Functions", exists=True)
    outLesion = File(desc="Lesion Segmentation", exists=True)
    outSulcal = File(desc="Sulcal CSF Membership", exists=True)
    outCortical = File(desc="Cortical GM Membership", exists=True)
    outFilled = File(desc="Filled WM Membership", exists=True)
    outWM = File(desc="WM Mask", exists=True)


class MedicAlgorithmLesionToads(SEMLikeCommandLine):
    """title: Lesion TOADS

category: Developer Tools

description: Algorithm for simulataneous brain structures and MS lesion segmentation of MS Brains. The brain segmentation is topologically consistent and the algorithm can use multiple MR sequences as input data.
N. Shiee, P.-L. Bazin, A.Z. Ozturk, P.A. Calabresi, D.S. Reich, D.L. Pham, "A Topology-Preserving Approach to the Segmentation of Brain Images with Multiple Sclerosis", NeuroImage, vol. 49, no. 2, pp. 1524-1535, 2010.

version: 1.9.R

contributor: Navid Shiee (navid.shiee@nih.gov) http://iacl.ece.jhu.edu/~nshiee/

"""

    input_spec = MedicAlgorithmLesionToadsInputSpec
    output_spec = MedicAlgorithmLesionToadsOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.classification.MedicAlgorithmLesionToads "
    _outputs_filenames = {'outWM':'outWM.nii','outHard':'outHard.nii','outFilled':'outFilled.nii','outMembership':'outMembership.nii','outInhomogeneity':'outInhomogeneity.nii','outCortical':'outCortical.nii','outHard2':'outHard2.nii','outLesion':'outLesion.nii','outSulcal':'outSulcal.nii'}
    _redirect_x = True


class JistBrainMp2rageSkullStrippingInputSpec(CommandLineInputSpec):
    inSecond = File(desc="Second inversion (Inv2) Image", exists=True, argstr="--inSecond %s")
    inT1 = File(desc="T1 Map (T1_Images) Image (opt)", exists=True, argstr="--inT1 %s")
    inT1weighted = File(desc="T1-weighted (UNI) Image (opt)", exists=True, argstr="--inT1weighted %s")
    inFilter = File(desc="Filter Image (opt)", exists=True, argstr="--inFilter %s")
    inSkip = traits.Enum("true", "false", desc="Skip zero values", argstr="--inSkip %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outBrain = traits.Either(traits.Bool, File(), hash_files=False, desc="Brain Mask Image", argstr="--outBrain %s")
    outMasked = traits.Either(traits.Bool, File(), hash_files=False, desc="Masked T1 Map Image", argstr="--outMasked %s")
    outMasked2 = traits.Either(traits.Bool, File(), hash_files=False, desc="Masked T1-weighted Image", argstr="--outMasked2 %s")
    outMasked3 = traits.Either(traits.Bool, File(), hash_files=False, desc="Masked Filter Image", argstr="--outMasked3 %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistBrainMp2rageSkullStrippingOutputSpec(TraitedSpec):
    outBrain = File(desc="Brain Mask Image", exists=True)
    outMasked = File(desc="Masked T1 Map Image", exists=True)
    outMasked2 = File(desc="Masked T1-weighted Image", exists=True)
    outMasked3 = File(desc="Masked Filter Image", exists=True)


class JistBrainMp2rageSkullStripping(SEMLikeCommandLine):
    """title: MP2RAGE Skull Stripping

category: Developer Tools

description: Estimate a brain mask for a MP2RAGE dataset. At least a T1-weighted or a T1 map image is required.

version: 3.0.RC

"""

    input_spec = JistBrainMp2rageSkullStrippingInputSpec
    output_spec = JistBrainMp2rageSkullStrippingOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainMp2rageSkullStripping "
    _outputs_filenames = {'outBrain':'outBrain.nii','outMasked3':'outMasked3.nii','outMasked2':'outMasked2.nii','outMasked':'outMasked.nii'}
    _redirect_x = True


class JistCortexSurfaceMeshInflationInputSpec(CommandLineInputSpec):
    inLevelset = File(desc="Levelset Image", exists=True, argstr="--inLevelset %s")
    inSOR = traits.Float(desc="SOR Parameter", argstr="--inSOR %f")
    inMean = traits.Float(desc="Mean Curvature Threshold", argstr="--inMean %f")
    inStep = traits.Int(desc="Step Size", argstr="--inStep %d")
    inMax = traits.Int(desc="Max Iterations", argstr="--inMax %d")
    inLorentzian = traits.Enum("true", "false", desc="Lorentzian Norm", argstr="--inLorentzian %s")
    inTopology = traits.Enum("26/6", "6/26", "18/6", "6/18", "6/6", "wcs", "wco", "no", desc="Topology", argstr="--inTopology %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outOriginal = traits.Either(traits.Bool, File(), hash_files=False, desc="Original Surface", argstr="--outOriginal %s")
    outInflated = traits.Either(traits.Bool, File(), hash_files=False, desc="Inflated Surface", argstr="--outInflated %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistCortexSurfaceMeshInflationOutputSpec(TraitedSpec):
    outOriginal = File(desc="Original Surface", exists=True)
    outInflated = File(desc="Inflated Surface", exists=True)


class JistCortexSurfaceMeshInflation(SEMLikeCommandLine):
    """title: Surface Mesh Inflation

category: Developer Tools

description: Inflates a cortical surface mesh.
D. Tosun, M. E. Rettmann, X. Han, X. Tao, C. Xu, S. M. Resnick, D. Pham, and J. L. Prince, Cortical Surface Segmentation and Mapping, NeuroImage, vol. 23, pp. S108--S118, 2004.

version: 3.0.RC

contributor: Duygu Tosun

"""

    input_spec = JistCortexSurfaceMeshInflationInputSpec
    output_spec = JistCortexSurfaceMeshInflationOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.cortex.JistCortexSurfaceMeshInflation "
    _outputs_filenames = {'outOriginal':'outOriginal','outInflated':'outInflated'}
    _redirect_x = True


class RandomVolInputSpec(CommandLineInputSpec):
    inSize = traits.Int(desc="Size of Volume in X direction", argstr="--inSize %d")
    inSize2 = traits.Int(desc="Size of Volume in Y direction", argstr="--inSize2 %d")
    inSize3 = traits.Int(desc="Size of Volume in Z direction", argstr="--inSize3 %d")
    inSize4 = traits.Int(desc="Size of Volume in t direction", argstr="--inSize4 %d")
    inStandard = traits.Int(desc="Standard Deviation for Normal Distribution", argstr="--inStandard %d")
    inLambda = traits.Float(desc="Lambda Value for Exponential Distribution", argstr="--inLambda %f")
    inMaximum = traits.Int(desc="Maximum Value", argstr="--inMaximum %d")
    inMinimum = traits.Int(desc="Minimum Value", argstr="--inMinimum %d")
    inField = traits.Enum("Uniform", "Normal", "Exponential", desc="Field", argstr="--inField %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outRand1 = traits.Either(traits.Bool, File(), hash_files=False, desc="Rand1", argstr="--outRand1 %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class RandomVolOutputSpec(TraitedSpec):
    outRand1 = File(desc="Rand1", exists=True)


class RandomVol(SEMLikeCommandLine):
    """title: Random Volume Generator

category: Developer Tools

description: Generate a random scalar volume.

version: 1.12.RC

documentation-url: http://www.nitrc.org/projects/jist/

"""

    input_spec = RandomVolInputSpec
    output_spec = RandomVolOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.bme.smile.demo.RandomVol "
    _outputs_filenames = {'outRand1':'outRand1.nii'}
    _redirect_x = True


class MedicAlgorithmImageCalculatorInputSpec(CommandLineInputSpec):
    inVolume = File(desc="Volume 1", exists=True, argstr="--inVolume %s")
    inVolume2 = File(desc="Volume 2", exists=True, argstr="--inVolume2 %s")
    inOperation = traits.Enum("Add", "Subtract", "Multiply", "Divide", "Min", "Max", desc="Operation", argstr="--inOperation %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outResult = traits.Either(traits.Bool, File(), hash_files=False, desc="Result Volume", argstr="--outResult %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class MedicAlgorithmImageCalculatorOutputSpec(TraitedSpec):
    outResult = File(desc="Result Volume", exists=True)


class MedicAlgorithmImageCalculator(SEMLikeCommandLine):
    """title: Image Calculator

category: Developer Tools

description: Perform simple image calculator operations on two images. The operations include 'Add', 'Subtract', 'Multiply', and 'Divide'

version: 1.10.RC

documentation-url: http://www.iacl.ece.jhu.edu/

"""

    input_spec = MedicAlgorithmImageCalculatorInputSpec
    output_spec = MedicAlgorithmImageCalculatorOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.utilities.math.MedicAlgorithmImageCalculator "
    _outputs_filenames = {'outResult':'outResult.nii'}
    _redirect_x = True


class JistBrainMp2rageDuraEstimationInputSpec(CommandLineInputSpec):
    inSecond = File(desc="Second inversion (Inv2) Image", exists=True, argstr="--inSecond %s")
    inSkull = File(desc="Skull Stripping Mask", exists=True, argstr="--inSkull %s")
    inDistance = traits.Float(desc="Distance to background (mm)", argstr="--inDistance %f")
    inoutput = traits.Enum("dura_region", "boundary", "dura_prior", "bg_prior", "intens_prior", desc="Outputs an estimate of the dura / CSF boundary or an estimate of the entire dura region.", argstr="--inoutput %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outDura = traits.Either(traits.Bool, File(), hash_files=False, desc="Dura Image", argstr="--outDura %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistBrainMp2rageDuraEstimationOutputSpec(TraitedSpec):
    outDura = File(desc="Dura Image", exists=True)


class JistBrainMp2rageDuraEstimation(SEMLikeCommandLine):
    """title: MP2RAGE Dura Estimation

category: Developer Tools

description: Filters a MP2RAGE brain image to obtain a probability map of dura matter.

version: 3.0.RC

"""

    input_spec = JistBrainMp2rageDuraEstimationInputSpec
    output_spec = JistBrainMp2rageDuraEstimationOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainMp2rageDuraEstimation "
    _outputs_filenames = {'outDura':'outDura.nii'}
    _redirect_x = True


class JistLaminarProfileSamplingInputSpec(CommandLineInputSpec):
    inProfile = File(desc="Profile Surface Image", exists=True, argstr="--inProfile %s")
    inIntensity = File(desc="Intensity Image", exists=True, argstr="--inIntensity %s")
    inCortex = File(desc="Cortex Mask (opt)", exists=True, argstr="--inCortex %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outProfilemapped = traits.Either(traits.Bool, File(), hash_files=False, desc="Profile-mapped Intensity Image", argstr="--outProfilemapped %s")
    outProfile2 = traits.Either(traits.Bool, File(), hash_files=False, desc="Profile 4D Mask", argstr="--outProfile2 %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistLaminarProfileSamplingOutputSpec(TraitedSpec):
    outProfilemapped = File(desc="Profile-mapped Intensity Image", exists=True)
    outProfile2 = File(desc="Profile 4D Mask", exists=True)


class JistLaminarProfileSampling(SEMLikeCommandLine):
    """title: Profile Sampling

category: Developer Tools

description: Sample some intensity image along a cortical profile across layer surfaces.

version: 3.0.RC

"""

    input_spec = JistLaminarProfileSamplingInputSpec
    output_spec = JistLaminarProfileSamplingOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarProfileSampling "
    _outputs_filenames = {'outProfile2':'outProfile2.nii','outProfilemapped':'outProfilemapped.nii'}
    _redirect_x = True


class MedicAlgorithmMipavReorientInputSpec(CommandLineInputSpec):
    inSource = InputMultiPath(File, desc="Source", sep=";", argstr="--inSource %s")
    inTemplate = File(desc="Template", exists=True, argstr="--inTemplate %s")
    inNew = traits.Enum("Dicom axial", "Dicom coronal", "Dicom sagittal", "User defined", desc="New image orientation", argstr="--inNew %s")
    inUser = traits.Enum("Unknown", "Patient Right to Left", "Patient Left to Right", "Patient Posterior to Anterior", "Patient Anterior to Posterior", "Patient Inferior to Superior", "Patient Superior to Inferior", desc="User defined X-axis orientation (image left to right)", argstr="--inUser %s")
    inUser2 = traits.Enum("Unknown", "Patient Right to Left", "Patient Left to Right", "Patient Posterior to Anterior", "Patient Anterior to Posterior", "Patient Inferior to Superior", "Patient Superior to Inferior", desc="User defined Y-axis orientation (image top to bottom)", argstr="--inUser2 %s")
    inUser3 = traits.Enum("Unknown", "Patient Right to Left", "Patient Left to Right", "Patient Posterior to Anterior", "Patient Anterior to Posterior", "Patient Inferior to Superior", "Patient Superior to Inferior", desc="User defined Z-axis orientation (into the screen)", argstr="--inUser3 %s")
    inUser4 = traits.Enum("Axial", "Coronal", "Sagittal", "Unknown", desc="User defined Image Orientation", argstr="--inUser4 %s")
    inInterpolation = traits.Enum("Nearest Neighbor", "Trilinear", "Bspline 3rd order", "Bspline 4th order", "Cubic Lagrangian", "Quintic Lagrangian", "Heptic Lagrangian", "Windowed Sinc", desc="Interpolation", argstr="--inInterpolation %s")
    inResolution = traits.Enum("Unchanged", "Finest cubic", "Coarsest cubic", "Same as template", desc="Resolution", argstr="--inResolution %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outReoriented = InputMultiPath(File, desc="Reoriented Volume", sep=";", argstr="--outReoriented %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class MedicAlgorithmMipavReorientOutputSpec(TraitedSpec):
    pass


class MedicAlgorithmMipavReorient(SEMLikeCommandLine):
    """title: Reorient Volume

category: Developer Tools

description: Reorient a volume to a particular anatomical orientation.

version: .alpha

"""

    input_spec = MedicAlgorithmMipavReorientInputSpec
    output_spec = MedicAlgorithmMipavReorientOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.utilities.volume.MedicAlgorithmMipavReorient "
    _outputs_filenames = {}
    _redirect_x = True


class MedicAlgorithmSPECTRE2010InputSpec(CommandLineInputSpec):
    inInput = File(desc="Input volume to be skullstripped.", exists=True, argstr="--inInput %s")
    inAtlas = File(desc="SPECTRE atlas description file. A text file enumerating atlas files and landmarks.", exists=True, argstr="--inAtlas %s")
    inInitial = traits.Int(desc="Erosion of the inital mask, which is based on the probability mask and the classification., The initial mask is ouput as the d0 volume at the conclusion of SPECTRE.", argstr="--inInitial %d")
    inImage = traits.Enum("T1_SPGR", "T1_ALT", "T1_MPRAGE", "T2", "FLAIR", desc="Set the image modality. MP-RAGE is recommended for most T1 sequence images.", argstr="--inImage %s")
    inOutput = traits.Enum("true", "false", desc="Determines if the output results are transformed back into the space of the original input image.", argstr="--inOutput %s")
    inFind = traits.Enum("true", "false", desc="Find Midsaggital Plane", argstr="--inFind %s")
    inRun = traits.Enum("true", "false", desc="Run Smooth Brain Mask", argstr="--inRun %s")
    inResample = traits.Enum("true", "false", desc="Determines if the data is resampled to be isotropic during the processing.", argstr="--inResample %s")
    inInitial2 = traits.Float(desc="Initial probability threshold", argstr="--inInitial2 %f")
    inMinimum = traits.Float(desc="Minimum probability threshold", argstr="--inMinimum %f")
    inMMC = traits.Int(desc="The size of the dilation step within the Modified Morphological Closing.", argstr="--inMMC %d")
    inMMC2 = traits.Int(desc="The size of the erosion step within the Modified Morphological Closing.", argstr="--inMMC2 %d")
    inInhomogeneity = traits.Enum("true", "false", desc="Set to false by default, this parameter will make FANTASM try to do inhomogeneity correction during it's iterative cycle.", argstr="--inInhomogeneity %s")
    inSmoothing = traits.Float(argstr="--inSmoothing %f")
    inBackground = traits.Float(argstr="--inBackground %f")
    inOutput2 = traits.Enum("true", "false", desc="Output Plane?", argstr="--inOutput2 %s")
    inOutput3 = traits.Enum("true", "false", desc="Output Split-Halves?", argstr="--inOutput3 %s")
    inOutput4 = traits.Enum("true", "false", desc="Output Segmentation on Plane?", argstr="--inOutput4 %s")
    inDegrees = traits.Enum("Rigid - 6", "Global rescale - 7", "Specific rescale - 9", "Affine - 12", desc="Degrees of freedom", argstr="--inDegrees %s")
    inCost = traits.Enum("Correlation ratio", "Least squares", "Normalized cross correlation", "Normalized mutual information", desc="Cost function", argstr="--inCost %s")
    inRegistration = traits.Enum("Trilinear", "Bspline 3rd order", "Bspline 4th order", "Cubic Lagrangian", "Quintic Lagrangian", "Heptic Lagrangian", "Windowed sinc", desc="Registration interpolation", argstr="--inRegistration %s")
    inOutput5 = traits.Enum("Trilinear", "Bspline 3rd order", "Bspline 4th order", "Cubic Lagrangian", "Quintic Lagrangian", "Heptic Lagrangian", "Windowed sinc", "Nearest Neighbor", desc="Output interpolation", argstr="--inOutput5 %s")
    inApply = traits.Enum("All", "X", "Y", "Z", desc="Apply rotation", argstr="--inApply %s")
    inMinimum2 = traits.Float(desc="Minimum angle", argstr="--inMinimum2 %f")
    inMaximum = traits.Float(desc="Maximum angle", argstr="--inMaximum %f")
    inCoarse = traits.Float(desc="Coarse angle increment", argstr="--inCoarse %f")
    inFine = traits.Float(desc="Fine angle increment", argstr="--inFine %f")
    inMultiple = traits.Int(desc="Multiple of tolerance to bracket the minimum", argstr="--inMultiple %d")
    inNumber = traits.Int(desc="Number of iterations", argstr="--inNumber %d")
    inNumber2 = traits.Int(desc="Number of minima from Level 8 to test at Level 4", argstr="--inNumber2 %d")
    inUse = traits.Enum("true", "false", desc="Use the max of the min resolutions of the two datasets when resampling", argstr="--inUse %s")
    inSubsample = traits.Enum("true", "false", desc="Subsample image for speed", argstr="--inSubsample %s")
    inSkip = traits.Enum("true", "false", desc="Skip multilevel search (Assume images are close to alignment)", argstr="--inSkip %s")
    inMultithreading = traits.Enum("true", "false", desc="Set to false by default, this parameter controls the multithreaded behavior of the linear registration.", argstr="--inMultithreading %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outOriginal = traits.Either(traits.Bool, File(), hash_files=False, desc="If Output in Original Space Flag is true then outputs the original input volume. Otherwise outputs the axialy reoriented input volume.", argstr="--outOriginal %s")
    outStripped = traits.Either(traits.Bool, File(), hash_files=False, desc="Skullstripped result of the input volume with just the brain.", argstr="--outStripped %s")
    outMask = traits.Either(traits.Bool, File(), hash_files=False, desc="Binary Mask of the skullstripped result with just the brain", argstr="--outMask %s")
    outPrior = traits.Either(traits.Bool, File(), hash_files=False, desc="Probability prior from the atlas registrations", argstr="--outPrior %s")
    outFANTASM = traits.Either(traits.Bool, File(), hash_files=False, desc="Tissue classification of of the whole input volume.", argstr="--outFANTASM %s")
    outd0 = traits.Either(traits.Bool, File(), hash_files=False, desc="Initial Brainmask", argstr="--outd0 %s")
    outMidsagittal = traits.Either(traits.Bool, File(), hash_files=False, desc="Plane dividing the brain hemispheres", argstr="--outMidsagittal %s")
    outSplitHalves = traits.Either(traits.Bool, File(), hash_files=False, desc="Skullstripped mask of the brain with the hemispheres divided.", argstr="--outSplitHalves %s")
    outSegmentation = traits.Either(traits.Bool, File(), hash_files=False, desc="2D image showing the tissue classification on the midsagittal plane", argstr="--outSegmentation %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class MedicAlgorithmSPECTRE2010OutputSpec(TraitedSpec):
    outOriginal = File(desc="If Output in Original Space Flag is true then outputs the original input volume. Otherwise outputs the axialy reoriented input volume.", exists=True)
    outStripped = File(desc="Skullstripped result of the input volume with just the brain.", exists=True)
    outMask = File(desc="Binary Mask of the skullstripped result with just the brain", exists=True)
    outPrior = File(desc="Probability prior from the atlas registrations", exists=True)
    outFANTASM = File(desc="Tissue classification of of the whole input volume.", exists=True)
    outd0 = File(desc="Initial Brainmask", exists=True)
    outMidsagittal = File(desc="Plane dividing the brain hemispheres", exists=True)
    outSplitHalves = File(desc="Skullstripped mask of the brain with the hemispheres divided.", exists=True)
    outSegmentation = File(desc="2D image showing the tissue classification on the midsagittal plane", exists=True)


class MedicAlgorithmSPECTRE2010(SEMLikeCommandLine):
    """title: SPECTRE 2010

category: Developer Tools

description: Simple Paradigm for Extra-Cranial Tissue REmoval
################################################
Algorithm Version: 1.6
GUI Version: 1.10

A. Carass, M.B. Wheeler, J. Cuzzocreo, P.-L. Bazin, S.S. Bassett, and J.L. Prince, 'A Joint Registration and Segmentation Approach to Skull Stripping', Fourth IEEE International Symposium on Biomedical Imaging (ISBI 2007), Arlington, VA, April 12-15, 2007.
A. Carass, J. Cuzzocreo, M.B. Wheeler, P.-L. Bazin, S.M. Resnick, and J.L. Prince, 'Simple paradigm for extra-cerebral tissue removal: Algorithm and analysis', NeuroImage 56(4):1982-1992, 2011.

version: 1.6.R

documentation-url: http://www.iacl.ece.jhu.edu/

contributor: Aaron Carass (aaron_carass@jhu.edu) http://www.iacl.ece.jhu.edu/
Hanlin Wan (hanlinwan@gmail.com)

"""

    input_spec = MedicAlgorithmSPECTRE2010InputSpec
    output_spec = MedicAlgorithmSPECTRE2010OutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.segmentation.skull_strip.MedicAlgorithmSPECTRE2010 "
    _outputs_filenames = {'outd0':'outd0.nii','outOriginal':'outOriginal.nii','outMask':'outMask.nii','outSplitHalves':'outSplitHalves.nii','outMidsagittal':'outMidsagittal.nii','outPrior':'outPrior.nii','outFANTASM':'outFANTASM.nii','outSegmentation':'outSegmentation.nii','outStripped':'outStripped.nii'}
    _redirect_x = True


class JistBrainPartialVolumeFilterInputSpec(CommandLineInputSpec):
    inInput = File(desc="Input Image", exists=True, argstr="--inInput %s")
    inPV = traits.Enum("bright", "dark", "both", desc="Outputs the raw intensity values or a probability score for the partial volume regions.", argstr="--inPV %s")
    inoutput = traits.Enum("probability", "intensity", desc="output", argstr="--inoutput %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outPartial = traits.Either(traits.Bool, File(), hash_files=False, desc="Partial Volume Image", argstr="--outPartial %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistBrainPartialVolumeFilterOutputSpec(TraitedSpec):
    outPartial = File(desc="Partial Volume Image", exists=True)


class JistBrainPartialVolumeFilter(SEMLikeCommandLine):
    """title: Partial Volume Filter

category: Developer Tools

description: Filters an image for regions of partial voluming assuming a ridge-like model of intensity.

version: 2.0.RC

"""

    input_spec = JistBrainPartialVolumeFilterInputSpec
    output_spec = JistBrainPartialVolumeFilterOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainPartialVolumeFilter "
    _outputs_filenames = {'outPartial':'outPartial.nii'}
    _redirect_x = True


class JistIntensityMp2rageMaskingInputSpec(CommandLineInputSpec):
    inSecond = File(desc="Second inversion (Inv2) Image", exists=True, argstr="--inSecond %s")
    inQuantitative = File(desc="Quantitative T1 Map (T1_Images) Image", exists=True, argstr="--inQuantitative %s")
    inT1weighted = File(desc="T1-weighted (UNI) Image", exists=True, argstr="--inT1weighted %s")
    inBackground = traits.Enum("exponential", "half-normal", desc="Model distribution for background noise (default is half-normal, exponential is more stringent).", argstr="--inBackground %s")
    inSkip = traits.Enum("true", "false", desc="Skip zero values", argstr="--inSkip %s")
    inMasking = traits.Enum("binary", "proba", desc="Whether to use a binary threshold or a weighted average based on the probability.", argstr="--inMasking %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outSignal = traits.Either(traits.Bool, File(), hash_files=False, desc="Signal Proba Image", argstr="--outSignal %s")
    outSignal2 = traits.Either(traits.Bool, File(), hash_files=False, desc="Signal Mask Image", argstr="--outSignal2 %s")
    outMasked = traits.Either(traits.Bool, File(), hash_files=False, desc="Masked T1 Map Image", argstr="--outMasked %s")
    outMasked2 = traits.Either(traits.Bool, File(), hash_files=False, desc="Masked Iso Image", argstr="--outMasked2 %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class JistIntensityMp2rageMaskingOutputSpec(TraitedSpec):
    outSignal = File(desc="Signal Proba Image", exists=True)
    outSignal2 = File(desc="Signal Mask Image", exists=True)
    outMasked = File(desc="Masked T1 Map Image", exists=True)
    outMasked2 = File(desc="Masked Iso Image", exists=True)


class JistIntensityMp2rageMasking(SEMLikeCommandLine):
    """title: MP2RAGE Background Masking

category: Developer Tools

description: Estimate a background signal mask for a MP2RAGE dataset.

version: 3.0.RC

"""

    input_spec = JistIntensityMp2rageMaskingInputSpec
    output_spec = JistIntensityMp2rageMaskingOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.intensity.JistIntensityMp2rageMasking "
    _outputs_filenames = {'outSignal2':'outSignal2.nii','outSignal':'outSignal.nii','outMasked2':'outMasked2.nii','outMasked':'outMasked.nii'}
    _redirect_x = True


class MedicAlgorithmThresholdToBinaryMaskInputSpec(CommandLineInputSpec):
    inLabel = InputMultiPath(File, desc="Input volumes", sep=";", argstr="--inLabel %s")
    inMinimum = traits.Float(desc="Minimum threshold value.", argstr="--inMinimum %f")
    inMaximum = traits.Float(desc="Maximum threshold value.", argstr="--inMaximum %f")
    inUse = traits.Enum("true", "false", desc="Use the images max intensity as the max value of the range.", argstr="--inUse %s")
    xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
    outBinary = InputMultiPath(File, desc="Binary Mask", sep=";", argstr="--outBinary %s")
    null = traits.Str(desc="Execution Time", argstr="--null %s")
    xDefaultMem = traits.Int(desc="Set default maximum heap size", argstr="-xDefaultMem %d")
    xMaxProcess = traits.Int(1, desc="Set default maximum number of processes.", argstr="-xMaxProcess %d", usedefault=True)


class MedicAlgorithmThresholdToBinaryMaskOutputSpec(TraitedSpec):
    pass


class MedicAlgorithmThresholdToBinaryMask(SEMLikeCommandLine):
    """title: Threshold to Binary Mask

category: Developer Tools

description: Given a volume and an intensity range create a binary mask for values within that range.

version: 1.2.RC

documentation-url: http://www.iacl.ece.jhu.edu/

"""

    input_spec = MedicAlgorithmThresholdToBinaryMaskInputSpec
    output_spec = MedicAlgorithmThresholdToBinaryMaskOutputSpec
    _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.utilities.volume.MedicAlgorithmThresholdToBinaryMask "
    _outputs_filenames = {}
    _redirect_x = True
