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model.py
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model.py
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# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""The freesurfer module provides basic functions for interfacing with
freesurfer tools.
"""
import os
from ...utils.filemanip import fname_presuffix, split_filename
from ..base import (
TraitedSpec,
File,
traits,
InputMultiPath,
OutputMultiPath,
Directory,
isdefined,
)
from .base import FSCommand, FSTraitedSpec
from .utils import copy2subjdir
__docformat__ = "restructuredtext"
class MRISPreprocInputSpec(FSTraitedSpec):
out_file = File(argstr="--out %s", genfile=True, desc="output filename")
target = traits.Str(
argstr="--target %s", mandatory=True, desc="target subject name"
)
hemi = traits.Enum(
"lh",
"rh",
argstr="--hemi %s",
mandatory=True,
desc="hemisphere for source and target",
)
surf_measure = traits.Str(
argstr="--meas %s",
xor=("surf_measure", "surf_measure_file", "surf_area"),
desc="Use subject/surf/hemi.surf_measure as input",
)
surf_area = traits.Str(
argstr="--area %s",
xor=("surf_measure", "surf_measure_file", "surf_area"),
desc="Extract vertex area from subject/surf/hemi.surfname to use as input.",
)
subjects = traits.List(
argstr="--s %s...",
xor=("subjects", "fsgd_file", "subject_file"),
desc="subjects from who measures are calculated",
)
fsgd_file = File(
exists=True,
argstr="--fsgd %s",
xor=("subjects", "fsgd_file", "subject_file"),
desc="specify subjects using fsgd file",
)
subject_file = File(
exists=True,
argstr="--f %s",
xor=("subjects", "fsgd_file", "subject_file"),
desc="file specifying subjects separated by white space",
)
surf_measure_file = InputMultiPath(
File(exists=True),
argstr="--is %s...",
xor=("surf_measure", "surf_measure_file", "surf_area"),
desc="file alternative to surfmeas, still requires list of subjects",
)
source_format = traits.Str(argstr="--srcfmt %s", desc="source format")
surf_dir = traits.Str(
argstr="--surfdir %s", desc="alternative directory (instead of surf)"
)
vol_measure_file = InputMultiPath(
traits.Tuple(File(exists=True), File(exists=True)),
argstr="--iv %s %s...",
desc="list of volume measure and reg file tuples",
)
proj_frac = traits.Float(
argstr="--projfrac %s", desc="projection fraction for vol2surf"
)
fwhm = traits.Float(
argstr="--fwhm %f",
xor=["num_iters"],
desc="smooth by fwhm mm on the target surface",
)
num_iters = traits.Int(
argstr="--niters %d",
xor=["fwhm"],
desc="niters : smooth by niters on the target surface",
)
fwhm_source = traits.Float(
argstr="--fwhm-src %f",
xor=["num_iters_source"],
desc="smooth by fwhm mm on the source surface",
)
num_iters_source = traits.Int(
argstr="--niterssrc %d",
xor=["fwhm_source"],
desc="niters : smooth by niters on the source surface",
)
smooth_cortex_only = traits.Bool(
argstr="--smooth-cortex-only",
desc="only smooth cortex (ie, exclude medial wall)",
)
class MRISPreprocOutputSpec(TraitedSpec):
out_file = File(desc="preprocessed output file")
class MRISPreproc(FSCommand):
"""Use FreeSurfer mris_preproc to prepare a group of contrasts for
a second level analysis
Examples
--------
>>> preproc = MRISPreproc()
>>> preproc.inputs.target = 'fsaverage'
>>> preproc.inputs.hemi = 'lh'
>>> preproc.inputs.vol_measure_file = [('cont1.nii', 'register.dat'), \
('cont1a.nii', 'register.dat')]
>>> preproc.inputs.out_file = 'concatenated_file.mgz'
>>> preproc.cmdline
'mris_preproc --hemi lh --out concatenated_file.mgz --target fsaverage --iv cont1.nii register.dat --iv cont1a.nii register.dat'
"""
_cmd = "mris_preproc"
input_spec = MRISPreprocInputSpec
output_spec = MRISPreprocOutputSpec
def _list_outputs(self):
outputs = self.output_spec().get()
outfile = self.inputs.out_file
outputs["out_file"] = outfile
if not isdefined(outfile):
outputs["out_file"] = os.path.join(
os.getcwd(), "concat_%s_%s.mgz" % (self.inputs.hemi, self.inputs.target)
)
return outputs
def _gen_filename(self, name):
if name == "out_file":
return self._list_outputs()[name]
return None
class MRISPreprocReconAllInputSpec(MRISPreprocInputSpec):
surf_measure_file = File(
exists=True,
argstr="--meas %s",
xor=("surf_measure", "surf_measure_file", "surf_area"),
desc="file necessary for surfmeas",
)
surfreg_files = InputMultiPath(
File(exists=True),
argstr="--surfreg %s",
requires=["lh_surfreg_target", "rh_surfreg_target"],
desc="lh and rh input surface registration files",
)
lh_surfreg_target = File(
desc="Implicit target surface registration file", requires=["surfreg_files"]
)
rh_surfreg_target = File(
desc="Implicit target surface registration file", requires=["surfreg_files"]
)
subject_id = traits.String(
"subject_id",
argstr="--s %s",
usedefault=True,
xor=("subjects", "fsgd_file", "subject_file", "subject_id"),
desc="subject from whom measures are calculated",
)
copy_inputs = traits.Bool(
desc="If running as a node, set this to True "
"this will copy some implicit inputs to the "
"node directory."
)
class MRISPreprocReconAll(MRISPreproc):
"""Extends MRISPreproc to allow it to be used in a recon-all workflow
Examples
--------
>>> preproc = MRISPreprocReconAll()
>>> preproc.inputs.target = 'fsaverage'
>>> preproc.inputs.hemi = 'lh'
>>> preproc.inputs.vol_measure_file = [('cont1.nii', 'register.dat'), \
('cont1a.nii', 'register.dat')]
>>> preproc.inputs.out_file = 'concatenated_file.mgz'
>>> preproc.cmdline
'mris_preproc --hemi lh --out concatenated_file.mgz --s subject_id --target fsaverage --iv cont1.nii register.dat --iv cont1a.nii register.dat'
"""
input_spec = MRISPreprocReconAllInputSpec
def run(self, **inputs):
if self.inputs.copy_inputs:
self.inputs.subjects_dir = os.getcwd()
if "subjects_dir" in inputs:
inputs["subjects_dir"] = self.inputs.subjects_dir
if isdefined(self.inputs.surf_dir):
folder = self.inputs.surf_dir
else:
folder = "surf"
if isdefined(self.inputs.surfreg_files):
for surfreg in self.inputs.surfreg_files:
basename = os.path.basename(surfreg)
copy2subjdir(self, surfreg, folder, basename)
if basename.startswith("lh."):
copy2subjdir(
self,
self.inputs.lh_surfreg_target,
folder,
basename,
subject_id=self.inputs.target,
)
else:
copy2subjdir(
self,
self.inputs.rh_surfreg_target,
folder,
basename,
subject_id=self.inputs.target,
)
if isdefined(self.inputs.surf_measure_file):
copy2subjdir(self, self.inputs.surf_measure_file, folder)
return super(MRISPreprocReconAll, self).run(**inputs)
def _format_arg(self, name, spec, value):
# mris_preproc looks for these files in the surf dir
if name == "surfreg_files":
basename = os.path.basename(value[0])
return spec.argstr % basename.lstrip("rh.").lstrip("lh.")
if name == "surf_measure_file":
basename = os.path.basename(value)
return spec.argstr % basename.lstrip("rh.").lstrip("lh.")
return super(MRISPreprocReconAll, self)._format_arg(name, spec, value)
class GLMFitInputSpec(FSTraitedSpec):
glm_dir = traits.Str(argstr="--glmdir %s", desc="save outputs to dir", genfile=True)
in_file = File(
desc="input 4D file", argstr="--y %s", mandatory=True, copyfile=False
)
_design_xor = ("fsgd", "design", "one_sample")
fsgd = traits.Tuple(
File(exists=True),
traits.Enum("doss", "dods"),
argstr="--fsgd %s %s",
xor=_design_xor,
desc="freesurfer descriptor file",
)
design = File(
exists=True, argstr="--X %s", xor=_design_xor, desc="design matrix file"
)
contrast = InputMultiPath(
File(exists=True), argstr="--C %s...", desc="contrast file"
)
one_sample = traits.Bool(
argstr="--osgm",
xor=("one_sample", "fsgd", "design", "contrast"),
desc="construct X and C as a one-sample group mean",
)
no_contrast_ok = traits.Bool(
argstr="--no-contrasts-ok", desc="do not fail if no contrasts specified"
)
per_voxel_reg = InputMultiPath(
File(exists=True), argstr="--pvr %s...", desc="per-voxel regressors"
)
self_reg = traits.Tuple(
traits.Int,
traits.Int,
traits.Int,
argstr="--selfreg %d %d %d",
desc="self-regressor from index col row slice",
)
weighted_ls = File(
exists=True,
argstr="--wls %s",
xor=("weight_file", "weight_inv", "weight_sqrt"),
desc="weighted least squares",
)
fixed_fx_var = File(
exists=True, argstr="--yffxvar %s", desc="for fixed effects analysis"
)
fixed_fx_dof = traits.Int(
argstr="--ffxdof %d",
xor=["fixed_fx_dof_file"],
desc="dof for fixed effects analysis",
)
fixed_fx_dof_file = File(
argstr="--ffxdofdat %d",
xor=["fixed_fx_dof"],
desc="text file with dof for fixed effects analysis",
)
weight_file = File(
exists=True, xor=["weighted_ls"], desc="weight for each input at each voxel"
)
weight_inv = traits.Bool(
argstr="--w-inv", desc="invert weights", xor=["weighted_ls"]
)
weight_sqrt = traits.Bool(
argstr="--w-sqrt", desc="sqrt of weights", xor=["weighted_ls"]
)
fwhm = traits.Range(low=0.0, argstr="--fwhm %f", desc="smooth input by fwhm")
var_fwhm = traits.Range(
low=0.0, argstr="--var-fwhm %f", desc="smooth variance by fwhm"
)
no_mask_smooth = traits.Bool(
argstr="--no-mask-smooth", desc="do not mask when smoothing"
)
no_est_fwhm = traits.Bool(
argstr="--no-est-fwhm", desc="turn off FWHM output estimation"
)
mask_file = File(exists=True, argstr="--mask %s", desc="binary mask")
label_file = File(
exists=True,
argstr="--label %s",
xor=["cortex"],
desc="use label as mask, surfaces only",
)
cortex = traits.Bool(
argstr="--cortex",
xor=["label_file"],
desc="use subjects ?h.cortex.label as label",
)
invert_mask = traits.Bool(argstr="--mask-inv", desc="invert mask")
prune = traits.Bool(
argstr="--prune",
desc="remove voxels that do not have a non-zero value at each frame (def)",
)
no_prune = traits.Bool(
argstr="--no-prune", xor=["prunethresh"], desc="do not prune"
)
prune_thresh = traits.Float(
argstr="--prune_thr %f",
xor=["noprune"],
desc="prune threshold. Default is FLT_MIN",
)
compute_log_y = traits.Bool(
argstr="--logy", desc="compute natural log of y prior to analysis"
)
save_estimate = traits.Bool(
argstr="--yhat-save", desc="save signal estimate (yhat)"
)
save_residual = traits.Bool(argstr="--eres-save", desc="save residual error (eres)")
save_res_corr_mtx = traits.Bool(
argstr="--eres-scm",
desc="save residual error spatial correlation matrix (eres.scm). Big!",
)
surf = traits.Bool(
argstr="--surf %s %s %s",
requires=["subject_id", "hemi"],
desc="analysis is on a surface mesh",
)
subject_id = traits.Str(desc="subject id for surface geometry")
hemi = traits.Enum("lh", "rh", desc="surface hemisphere")
surf_geo = traits.Str(
"white", usedefault=True, desc="surface geometry name (e.g. white, pial)"
)
simulation = traits.Tuple(
traits.Enum("perm", "mc-full", "mc-z"),
traits.Int(min=1),
traits.Float,
traits.Str,
argstr="--sim %s %d %f %s",
desc="nulltype nsim thresh csdbasename",
)
sim_sign = traits.Enum(
"abs", "pos", "neg", argstr="--sim-sign %s", desc="abs, pos, or neg"
)
uniform = traits.Tuple(
traits.Float,
traits.Float,
argstr="--uniform %f %f",
desc="use uniform distribution instead of gaussian",
)
pca = traits.Bool(argstr="--pca", desc="perform pca/svd analysis on residual")
calc_AR1 = traits.Bool(
argstr="--tar1", desc="compute and save temporal AR1 of residual"
)
save_cond = traits.Bool(
argstr="--save-cond", desc="flag to save design matrix condition at each voxel"
)
vox_dump = traits.Tuple(
traits.Int,
traits.Int,
traits.Int,
argstr="--voxdump %d %d %d",
desc="dump voxel GLM and exit",
)
seed = traits.Int(argstr="--seed %d", desc="used for synthesizing noise")
synth = traits.Bool(argstr="--synth", desc="replace input with gaussian")
resynth_test = traits.Int(argstr="--resynthtest %d", desc="test GLM by resynthsis")
profile = traits.Int(argstr="--profile %d", desc="niters : test speed")
force_perm = traits.Bool(
argstr="--perm-force",
desc="force perumtation test, even when design matrix is not orthog",
)
diag = traits.Int(argstr="--diag %d", desc="Gdiag_no : set diagnositc level")
diag_cluster = traits.Bool(
argstr="--diag-cluster", desc="save sig volume and exit from first sim loop"
)
debug = traits.Bool(argstr="--debug", desc="turn on debugging")
check_opts = traits.Bool(
argstr="--checkopts", desc="don't run anything, just check options and exit"
)
allow_repeated_subjects = traits.Bool(
argstr="--allowsubjrep",
desc="allow subject names to repeat in the fsgd file (must appear before --fsgd",
)
allow_ill_cond = traits.Bool(
argstr="--illcond", desc="allow ill-conditioned design matrices"
)
sim_done_file = File(
argstr="--sim-done %s", desc="create file when simulation finished"
)
class GLMFitOutputSpec(TraitedSpec):
glm_dir = Directory(exists=True, desc="output directory")
beta_file = File(exists=True, desc="map of regression coefficients")
error_file = File(desc="map of residual error")
error_var_file = File(desc="map of residual error variance")
error_stddev_file = File(desc="map of residual error standard deviation")
estimate_file = File(desc="map of the estimated Y values")
mask_file = File(desc="map of the mask used in the analysis")
fwhm_file = File(desc="text file with estimated smoothness")
dof_file = File(desc="text file with effective degrees-of-freedom for the analysis")
gamma_file = OutputMultiPath(desc="map of contrast of regression coefficients")
gamma_var_file = OutputMultiPath(desc="map of regression contrast variance")
sig_file = OutputMultiPath(desc="map of F-test significance (in -log10p)")
ftest_file = OutputMultiPath(desc="map of test statistic values")
spatial_eigenvectors = File(desc="map of spatial eigenvectors from residual PCA")
frame_eigenvectors = File(desc="matrix of frame eigenvectors from residual PCA")
singular_values = File(desc="matrix singular values from residual PCA")
svd_stats_file = File(desc="text file summarizing the residual PCA")
class GLMFit(FSCommand):
"""Use FreeSurfer's mri_glmfit to specify and estimate a general linear model.
Examples
--------
>>> glmfit = GLMFit()
>>> glmfit.inputs.in_file = 'functional.nii'
>>> glmfit.inputs.one_sample = True
>>> glmfit.cmdline == 'mri_glmfit --glmdir %s --y functional.nii --osgm'%os.getcwd()
True
"""
_cmd = "mri_glmfit"
input_spec = GLMFitInputSpec
output_spec = GLMFitOutputSpec
def _format_arg(self, name, spec, value):
if name == "surf":
_si = self.inputs
return spec.argstr % (_si.subject_id, _si.hemi, _si.surf_geo)
return super(GLMFit, self)._format_arg(name, spec, value)
def _list_outputs(self):
outputs = self.output_spec().get()
# Get the top-level output directory
if not isdefined(self.inputs.glm_dir):
glmdir = os.getcwd()
else:
glmdir = os.path.abspath(self.inputs.glm_dir)
outputs["glm_dir"] = glmdir
# Assign the output files that always get created
outputs["beta_file"] = os.path.join(glmdir, "beta.mgh")
outputs["error_var_file"] = os.path.join(glmdir, "rvar.mgh")
outputs["error_stddev_file"] = os.path.join(glmdir, "rstd.mgh")
outputs["mask_file"] = os.path.join(glmdir, "mask.mgh")
outputs["fwhm_file"] = os.path.join(glmdir, "fwhm.dat")
outputs["dof_file"] = os.path.join(glmdir, "dof.dat")
# Assign the conditional outputs
if isdefined(self.inputs.save_residual) and self.inputs.save_residual:
outputs["error_file"] = os.path.join(glmdir, "eres.mgh")
if isdefined(self.inputs.save_estimate) and self.inputs.save_estimate:
outputs["estimate_file"] = os.path.join(glmdir, "yhat.mgh")
# Get the contrast directory name(s)
if isdefined(self.inputs.contrast):
contrasts = []
for c in self.inputs.contrast:
if split_filename(c)[2] in [".mat", ".dat", ".mtx", ".con"]:
contrasts.append(split_filename(c)[1])
else:
contrasts.append(os.path.split(c)[1])
elif isdefined(self.inputs.one_sample) and self.inputs.one_sample:
contrasts = ["osgm"]
# Add in the contrast images
outputs["sig_file"] = [os.path.join(glmdir, c, "sig.mgh") for c in contrasts]
outputs["ftest_file"] = [os.path.join(glmdir, c, "F.mgh") for c in contrasts]
outputs["gamma_file"] = [
os.path.join(glmdir, c, "gamma.mgh") for c in contrasts
]
outputs["gamma_var_file"] = [
os.path.join(glmdir, c, "gammavar.mgh") for c in contrasts
]
# Add in the PCA results, if relevant
if isdefined(self.inputs.pca) and self.inputs.pca:
pcadir = os.path.join(glmdir, "pca-eres")
outputs["spatial_eigenvectors"] = os.path.join(pcadir, "v.mgh")
outputs["frame_eigenvectors"] = os.path.join(pcadir, "u.mtx")
outputs["singluar_values"] = os.path.join(pcadir, "sdiag.mat")
outputs["svd_stats_file"] = os.path.join(pcadir, "stats.dat")
return outputs
def _gen_filename(self, name):
if name == "glm_dir":
return os.getcwd()
return None
class OneSampleTTest(GLMFit):
def __init__(self, **kwargs):
super(OneSampleTTest, self).__init__(**kwargs)
self.inputs.one_sample = True
class BinarizeInputSpec(FSTraitedSpec):
in_file = File(
exists=True,
argstr="--i %s",
mandatory=True,
copyfile=False,
desc="input volume",
)
min = traits.Float(argstr="--min %f", xor=["wm_ven_csf"], desc="min thresh")
max = traits.Float(argstr="--max %f", xor=["wm_ven_csf"], desc="max thresh")
rmin = traits.Float(argstr="--rmin %f", desc="compute min based on rmin*globalmean")
rmax = traits.Float(argstr="--rmax %f", desc="compute max based on rmax*globalmean")
match = traits.List(
traits.Int, argstr="--match %d...", desc="match instead of threshold"
)
wm = traits.Bool(
argstr="--wm", desc="set match vals to 2 and 41 (aseg for cerebral WM)"
)
ventricles = traits.Bool(
argstr="--ventricles",
desc="set match vals those for aseg ventricles+choroid (not 4th)",
)
wm_ven_csf = traits.Bool(
argstr="--wm+vcsf",
xor=["min", "max"],
desc="WM and ventricular CSF, including choroid (not 4th)",
)
binary_file = File(argstr="--o %s", genfile=True, desc="binary output volume")
out_type = traits.Enum("nii", "nii.gz", "mgz", argstr="", desc="output file type")
count_file = traits.Either(
traits.Bool,
File,
argstr="--count %s",
desc="save number of hits in ascii file (hits, ntotvox, pct)",
)
bin_val = traits.Int(
argstr="--binval %d", desc="set vox within thresh to val (default is 1)"
)
bin_val_not = traits.Int(
argstr="--binvalnot %d", desc="set vox outside range to val (default is 0)"
)
invert = traits.Bool(argstr="--inv", desc="set binval=0, binvalnot=1")
frame_no = traits.Int(
argstr="--frame %s", desc="use 0-based frame of input (default is 0)"
)
merge_file = File(exists=True, argstr="--merge %s", desc="merge with mergevol")
mask_file = File(exists=True, argstr="--mask maskvol", desc="must be within mask")
mask_thresh = traits.Float(argstr="--mask-thresh %f", desc="set thresh for mask")
abs = traits.Bool(
argstr="--abs", desc="take abs of invol first (ie, make unsigned)"
)
bin_col_num = traits.Bool(
argstr="--bincol", desc="set binarized voxel value to its column number"
)
zero_edges = traits.Bool(argstr="--zero-edges", desc="zero the edge voxels")
zero_slice_edge = traits.Bool(
argstr="--zero-slice-edges", desc="zero the edge slice voxels"
)
dilate = traits.Int(argstr="--dilate %d", desc="niters: dilate binarization in 3D")
erode = traits.Int(
argstr="--erode %d",
desc="nerode: erode binarization in 3D (after any dilation)",
)
erode2d = traits.Int(
argstr="--erode2d %d",
desc="nerode2d: erode binarization in 2D (after any 3D erosion)",
)
class BinarizeOutputSpec(TraitedSpec):
binary_file = File(exists=True, desc="binarized output volume")
count_file = File(desc="ascii file containing number of hits")
class Binarize(FSCommand):
"""Use FreeSurfer mri_binarize to threshold an input volume
Examples
--------
>>> binvol = Binarize(in_file='structural.nii', min=10, binary_file='foo_out.nii')
>>> binvol.cmdline
'mri_binarize --o foo_out.nii --i structural.nii --min 10.000000'
"""
_cmd = "mri_binarize"
input_spec = BinarizeInputSpec
output_spec = BinarizeOutputSpec
def _list_outputs(self):
outputs = self.output_spec().get()
outfile = self.inputs.binary_file
if not isdefined(outfile):
if isdefined(self.inputs.out_type):
outfile = fname_presuffix(
self.inputs.in_file,
newpath=os.getcwd(),
suffix=".".join(("_thresh", self.inputs.out_type)),
use_ext=False,
)
else:
outfile = fname_presuffix(
self.inputs.in_file, newpath=os.getcwd(), suffix="_thresh"
)
outputs["binary_file"] = os.path.abspath(outfile)
value = self.inputs.count_file
if isdefined(value):
if isinstance(value, bool):
if value:
outputs["count_file"] = fname_presuffix(
self.inputs.in_file,
suffix="_count.txt",
newpath=os.getcwd(),
use_ext=False,
)
else:
outputs["count_file"] = value
return outputs
def _format_arg(self, name, spec, value):
if name == "count_file":
if isinstance(value, bool):
fname = self._list_outputs()[name]
else:
fname = value
return spec.argstr % fname
if name == "out_type":
return ""
return super(Binarize, self)._format_arg(name, spec, value)
def _gen_filename(self, name):
if name == "binary_file":
return self._list_outputs()[name]
return None
class ConcatenateInputSpec(FSTraitedSpec):
in_files = InputMultiPath(
File(exists=True),
desc="Individual volumes to be concatenated",
argstr="--i %s...",
mandatory=True,
)
concatenated_file = File(desc="Output volume", argstr="--o %s", genfile=True)
sign = traits.Enum(
"abs",
"pos",
"neg",
argstr="--%s",
desc="Take only pos or neg voxles from input, or take abs",
)
stats = traits.Enum(
"sum",
"var",
"std",
"max",
"min",
"mean",
argstr="--%s",
desc="Compute the sum, var, std, max, min or mean of the input volumes",
)
paired_stats = traits.Enum(
"sum",
"avg",
"diff",
"diff-norm",
"diff-norm1",
"diff-norm2",
argstr="--paired-%s",
desc="Compute paired sum, avg, or diff",
)
gmean = traits.Int(
argstr="--gmean %d", desc="create matrix to average Ng groups, Nper=Ntot/Ng"
)
mean_div_n = traits.Bool(
argstr="--mean-div-n", desc="compute mean/nframes (good for var)"
)
multiply_by = traits.Float(
argstr="--mul %f", desc="Multiply input volume by some amount"
)
add_val = traits.Float(
argstr="--add %f", desc="Add some amount to the input volume"
)
multiply_matrix_file = File(
exists=True, argstr="--mtx %s", desc="Multiply input by an ascii matrix in file"
)
combine = traits.Bool(
argstr="--combine", desc="Combine non-zero values into single frame volume"
)
keep_dtype = traits.Bool(
argstr="--keep-datatype", desc="Keep voxelwise precision type (default is float"
)
max_bonfcor = traits.Bool(
argstr="--max-bonfcor",
desc="Compute max and bonferroni correct (assumes -log10(ps))",
)
max_index = traits.Bool(
argstr="--max-index",
desc="Compute the index of max voxel in concatenated volumes",
)
mask_file = File(exists=True, argstr="--mask %s", desc="Mask input with a volume")
vote = traits.Bool(
argstr="--vote",
desc="Most frequent value at each voxel and fraction of occurances",
)
sort = traits.Bool(argstr="--sort", desc="Sort each voxel by ascending frame value")
class ConcatenateOutputSpec(TraitedSpec):
concatenated_file = File(exists=True, desc="Path/name of the output volume")
class Concatenate(FSCommand):
"""Use Freesurfer mri_concat to combine several input volumes
into one output volume. Can concatenate by frames, or compute
a variety of statistics on the input volumes.
Examples
--------
Combine two input volumes into one volume with two frames
>>> concat = Concatenate()
>>> concat.inputs.in_files = ['cont1.nii', 'cont2.nii']
>>> concat.inputs.concatenated_file = 'bar.nii'
>>> concat.cmdline
'mri_concat --o bar.nii --i cont1.nii --i cont2.nii'
"""
_cmd = "mri_concat"
input_spec = ConcatenateInputSpec
output_spec = ConcatenateOutputSpec
def _list_outputs(self):
outputs = self.output_spec().get()
fname = self.inputs.concatenated_file
if not isdefined(fname):
fname = "concat_output.nii.gz"
outputs["concatenated_file"] = os.path.join(os.getcwd(), fname)
return outputs
def _gen_filename(self, name):
if name == "concatenated_file":
return self._list_outputs()[name]
return None
class SegStatsInputSpec(FSTraitedSpec):
_xor_inputs = ("segmentation_file", "annot", "surf_label")
segmentation_file = File(
exists=True,
argstr="--seg %s",
xor=_xor_inputs,
mandatory=True,
desc="segmentation volume path",
)
annot = traits.Tuple(
traits.Str,
traits.Enum("lh", "rh"),
traits.Str,
argstr="--annot %s %s %s",
xor=_xor_inputs,
mandatory=True,
desc="subject hemi parc : use surface parcellation",
)
surf_label = traits.Tuple(
traits.Str,
traits.Enum("lh", "rh"),
traits.Str,
argstr="--slabel %s %s %s",
xor=_xor_inputs,
mandatory=True,
desc="subject hemi label : use surface label",
)
summary_file = File(
argstr="--sum %s",
genfile=True,
position=-1,
desc="Segmentation stats summary table file",
)
partial_volume_file = File(
exists=True, argstr="--pv %s", desc="Compensate for partial voluming"
)
in_file = File(
exists=True,
argstr="--i %s",
desc="Use the segmentation to report stats on this volume",
)
frame = traits.Int(
argstr="--frame %d", desc="Report stats on nth frame of input volume"
)
multiply = traits.Float(argstr="--mul %f", desc="multiply input by val")
calc_snr = traits.Bool(
argstr="--snr", desc="save mean/std as extra column in output table"
)
calc_power = traits.Enum(
"sqr",
"sqrt",
argstr="--%s",
desc="Compute either the sqr or the sqrt of the input",
)
_ctab_inputs = ("color_table_file", "default_color_table", "gca_color_table")
color_table_file = File(
exists=True,
argstr="--ctab %s",
xor=_ctab_inputs,
desc="color table file with seg id names",
)
default_color_table = traits.Bool(
argstr="--ctab-default",
xor=_ctab_inputs,
desc="use $FREESURFER_HOME/FreeSurferColorLUT.txt",
)
gca_color_table = File(
exists=True,
argstr="--ctab-gca %s",
xor=_ctab_inputs,
desc="get color table from GCA (CMA)",
)
segment_id = traits.List(
argstr="--id %s...", desc="Manually specify segmentation ids"
)
exclude_id = traits.Int(argstr="--excludeid %d", desc="Exclude seg id from report")
exclude_ctx_gm_wm = traits.Bool(
argstr="--excl-ctxgmwm", desc="exclude cortical gray and white matter"
)
wm_vol_from_surf = traits.Bool(
argstr="--surf-wm-vol", desc="Compute wm volume from surf"
)
cortex_vol_from_surf = traits.Bool(
argstr="--surf-ctx-vol", desc="Compute cortex volume from surf"
)
non_empty_only = traits.Bool(
argstr="--nonempty", desc="Only report nonempty segmentations"
)
empty = traits.Bool(
argstr="--empty", desc="Report on segmentations listed in the color table"
)
mask_file = File(
exists=True, argstr="--mask %s", desc="Mask volume (same size as seg"
)
mask_thresh = traits.Float(
argstr="--maskthresh %f", desc="binarize mask with this threshold <0.5>"
)
mask_sign = traits.Enum(
"abs",
"pos",
"neg",
"--masksign %s",
desc="Sign for mask threshold: pos, neg, or abs",
)
mask_frame = traits.Int(
"--maskframe %d",
requires=["mask_file"],
desc="Mask with this (0 based) frame of the mask volume",
)
mask_invert = traits.Bool(
argstr="--maskinvert", desc="Invert binarized mask volume"
)
mask_erode = traits.Int(argstr="--maskerode %d", desc="Erode mask by some amount")
brain_vol = traits.Enum(
"brain-vol-from-seg",
"brainmask",
argstr="--%s",
desc="Compute brain volume either with ``brainmask`` or ``brain-vol-from-seg``",
)
brainmask_file = File(
argstr="--brainmask %s",
exists=True,
desc="Load brain mask and compute the volume of the brain as the non-zero voxels in this volume",
)
etiv = traits.Bool(argstr="--etiv", desc="Compute ICV from talairach transform")
etiv_only = traits.Enum(
"etiv",
"old-etiv",
"--%s-only",
desc="Compute etiv and exit. Use ``etiv`` or ``old-etiv``",
)
avgwf_txt_file = traits.Either(
traits.Bool,
File,
argstr="--avgwf %s",
desc="Save average waveform into file (bool or filename)",
)
avgwf_file = traits.Either(
traits.Bool,
File,
argstr="--avgwfvol %s",
desc="Save as binary volume (bool or filename)",
)
sf_avg_file = traits.Either(
traits.Bool, File, argstr="--sfavg %s", desc="Save mean across space and time"
)
vox = traits.List(
traits.Int,
argstr="--vox %s",
desc="Replace seg with all 0s except at C R S (three int inputs)",
)
supratent = traits.Bool(argstr="--supratent", desc="Undocumented input flag")
subcort_gm = traits.Bool(
argstr="--subcortgray", desc="Compute volume of subcortical gray matter"
)
total_gray = traits.Bool(
argstr="--totalgray", desc="Compute volume of total gray matter"
)
euler = traits.Bool(
argstr="--euler",
desc="Write out number of defect holes in orig.nofix based on the euler number",
)
in_intensity = File(
argstr="--in %s --in-intensity-name %s", desc="Undocumented input norm.mgz file"
)
intensity_units = traits.Enum(
"MR",
argstr="--in-intensity-units %s",
requires=["in_intensity"],
desc="Intensity units",
)
class SegStatsOutputSpec(TraitedSpec):
summary_file = File(exists=True, desc="Segmentation summary statistics table")
avgwf_txt_file = File(
desc="Text file with functional statistics averaged over segs"
)
avgwf_file = File(desc="Volume with functional statistics averaged over segs")
sf_avg_file = File(
desc="Text file with func statistics averaged over segs and framss"
)
class SegStats(FSCommand):
"""Use FreeSurfer mri_segstats for ROI analysis
Examples
--------
>>> import nipype.interfaces.freesurfer as fs
>>> ss = fs.SegStats()
>>> ss.inputs.annot = ('PWS04', 'lh', 'aparc')
>>> ss.inputs.in_file = 'functional.nii'
>>> ss.inputs.subjects_dir = '.'
>>> ss.inputs.avgwf_txt_file = 'avgwf.txt'
>>> ss.inputs.summary_file = 'summary.stats'
>>> ss.cmdline
'mri_segstats --annot PWS04 lh aparc --avgwf ./avgwf.txt --i functional.nii --sum ./summary.stats'
"""
_cmd = "mri_segstats"
input_spec = SegStatsInputSpec
output_spec = SegStatsOutputSpec
def _list_outputs(self):
outputs = self.output_spec().get()
if isdefined(self.inputs.summary_file):
outputs["summary_file"] = os.path.abspath(self.inputs.summary_file)
else:
outputs["summary_file"] = os.path.join(os.getcwd(), "summary.stats")
suffices = dict(
avgwf_txt_file="_avgwf.txt",
avgwf_file="_avgwf.nii.gz",
sf_avg_file="sfavg.txt",
)