class config(baseconfig):
highpass_freq = traits.Float()
lowpass_freq = traits.Float()
filtering_algorithm = traits.Enum("fsl", "IIR", "FIR", "Fourier")
reg_params = traits.BaseTuple(
traits.Bool(desc="motion parameters"),
traits.Bool(desc="norm components"),
traits.Bool(desc="noise components (CompCor)"),
traits.Bool(desc='gloabl signal (NOT RECOMMENDED!)'),
traits.Bool(desc="art_outliers"),
traits.Bool(desc="motion derivatives"))
do_despike = traits.Bool(False, usedefault=True)
do_whitening = traits.Bool(False, usedefault=True)
use_metadata = traits.Bool(True)
update_hash = traits.Bool(False)
class config(BaseWorkflowConfig):
uuid = traits.Str(desc="UUID")
# Directories
base_dir = Directory(
os.path.abspath('.'),
mandatory=True,
desc='Base directory of data. (Should be subject-independent)')
sink_dir = Directory(mandatory=True,
desc="Location where the BIP will store the results")
surf_dir = Directory(
desc="freesurfer directory. subject id's should be the same")
save_script_only = traits.Bool(False)
# DataGrabber
datagrabber = traits.Instance(Data, ())
# segstats
use_reg = traits.Bool(True)
inverse_reg = traits.Bool(True)
use_standard_label = traits.Bool(
False, desc="use same label file for all subjects")
label_file = traits.File()
use_annotation = traits.Bool(
False,
desc=
"use same annotation file for all subjects (will warp to subject space"
)
use_subject_annotation = traits.Bool(
False,
desc="you need to change datragrabber to\
have outputs lh_annotation and rh_annotation"
)
annot_space = traits.String("fsaverage5",
desc="subject space of annot file")
lh_annotation = traits.File()
rh_annotation = traits.File()
color_table_file = traits.Enum("Default", "Color_Table", "GCA_color_table",
"None")
color_file = traits.File()
proj = traits.BaseTuple(("frac", 0, 1, 0.1), traits.Enum("abs", "frac"),
traits.Float(), traits.Float(), traits.Float())
statname = traits.Str('segstats1', desc="description of the segstat")
class config(baseconfig):
highpass_freq = traits.Float()
lowpass_freq = traits.Float()
filtering_algorithm = traits.Enum("fsl", "IIR", "FIR", "Fourier")
reg_params = traits.BaseTuple(
traits.Bool(desc="motion parameters"),
traits.Bool(desc="norm components"),
traits.Bool(desc="noise components (CompCor)"),
traits.Bool(desc='gloabl signal (NOT RECOMMENDED!)'),
traits.Bool(desc="art_outliers"),
traits.Bool(desc="motion derivatives"))
do_despike = traits.Bool(False, usedefault=True)
do_whitening = traits.Bool(False, usedefault=True)
use_metadata = traits.Bool(True)
update_hash = traits.Bool(False)
datagrabber = traits.Instance(Data, ())
segmentation_type = traits.Enum('FAST', 'Atropos')
order = traits.Enum('motion_slicetime',
'slicetime_motion',
use_default=True)
save_script_only = traits.Bool(False)
class config(HasTraits):
uuid = traits.Str(desc="UUID")
# Directories
working_dir = Directory(mandatory=True,
desc="Location of the Nipype working directory")
base_dir = Directory(
os.path.abspath('.'),
mandatory=True,
desc='Base directory of data. (Should be subject-independent)')
sink_dir = Directory(mandatory=True,
desc="Location where the BIP will store the results")
crash_dir = Directory(mandatory=False,
desc="Location to store crash files")
surf_dir = Directory(
desc="freesurfer directory. subject id's should be the same")
# Execution
run_using_plugin = Bool(
False,
usedefault=True,
desc="True to run pipeline with plugin, False to run serially")
plugin = traits.Enum("PBS",
"MultiProc",
"SGE",
"Condor",
usedefault=True,
desc="plugin to use, if run_using_plugin=True")
plugin_args = traits.Dict({"qsub_args": "-q many"},
usedefault=True,
desc='Plugin arguments.')
test_mode = Bool(
False,
mandatory=False,
usedefault=True,
desc='Affects whether where and if the workflow keeps its \
intermediary files. True to keep intermediary files. '
)
timeout = traits.Float(30.0)
# DataGrabber
datagrabber = traits.Instance(Data, ())
# segstats
use_reg = traits.Bool(True)
inverse_reg = traits.Bool(True)
use_standard_label = traits.Bool(
False, desc="use same label file for all subjects")
label_file = traits.File()
use_annotation = traits.Bool(
False,
desc=
"use same annotation file for all subjects (will warp to subject space"
)
use_subject_annotation = traits.Bool(
False,
desc="you need to change datragrabber to\
have outputs lh_annotation and rh_annotation"
)
annot_space = traits.String("fsaverage5",
desc="subject space of annot file")
lh_annotation = traits.File()
rh_annotation = traits.File()
color_table_file = traits.Enum("Default", "Color_Table", "GCA_color_table",
"None")
color_file = traits.File()
proj = traits.BaseTuple(("frac", 0, 1, 0.1), traits.Enum("abs", "frac"),
traits.Float(), traits.Float(), traits.Float())
statname = traits.Str('segstats1', desc="description of the segstat")
class config(BaseWorkflowConfig):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc='Workflow description')
# Directories
base_dir = Directory(
os.path.abspath('.'),
mandatory=True,
desc='Base directory of data. (Should be subject-independent)')
sink_dir = Directory(os.path.abspath('.'),
mandatory=True,
desc="Location where the BIP will store the results")
field_dir = Directory(
desc="Base directory of field-map data (Should be subject-independent) \
Set this value to None if you don't want fieldmap distortion correction"
)
surf_dir = Directory(mandatory=True, desc="Freesurfer subjects directory")
# Subjects
subjects = traits.List(
traits.Str,
mandatory=True,
usedefault=True,
desc="Subject id's. Note: These MUST match the subject id's in the \
Freesurfer directory. For simplicity, the subject id's should \
also match with the location of individual functional files."
)
func_template = traits.String('%s/functional.nii.gz')
run_datagrabber_without_submitting = traits.Bool(
desc="Run the datagrabber without \
submitting to the cluster")
timepoints_to_remove = traits.Int(0, usedefault=True)
# Fieldmap
use_fieldmap = Bool(
False,
mandatory=False,
usedefault=True,
desc='True to include fieldmap distortion correction. Note: field_dir \
must be specified')
magnitude_template = traits.String('%s/magnitude.nii.gz')
phase_template = traits.String('%s/phase.nii.gz')
TE_diff = traits.Float(desc='difference in B0 field map TEs')
sigma = traits.Int(
2, desc='2D spatial gaussing smoothing stdev (default = 2mm)')
echospacing = traits.Float(desc="EPI echo spacing")
# Motion Correction
do_slicetiming = Bool(True,
usedefault=True,
desc="Perform slice timing correction")
SliceOrder = traits.List(traits.Int)
TR = traits.Float(1.0, mandatory=True, desc="TR of functional")
motion_correct_node = traits.Enum(
'nipy',
'fsl',
'spm',
'afni',
desc="motion correction algorithm to use",
usedefault=True,
)
loops = traits.List([5], traits.Int(5), usedefault=True)
#between_loops = traits.Either("None",traits.List([5]),usedefault=True)
speedup = traits.List([5], traits.Int(5), usedefault=True)
# Artifact Detection
norm_thresh = traits.Float(1,
min=0,
usedefault=True,
desc="norm thresh for art")
z_thresh = traits.Float(3, min=0, usedefault=True, desc="z thresh for art")
# Smoothing
fwhm = traits.List(
[0, 5],
traits.Float(),
mandatory=True,
usedefault=True,
desc="Full width at half max. The data will be smoothed at all values \
specified in this list.")
smooth_type = traits.Enum("susan",
"isotropic",
'freesurfer',
usedefault=True,
desc="Type of smoothing to use")
surface_fwhm = traits.Float(
0.0,
desc='surface smoothing kernel, if freesurfer is selected',
usedefault=True)
# CompCor
compcor_select = traits.BaseTuple(
traits.Bool,
traits.Bool,
mandatory=True,
desc="The first value in the list corresponds to applying \
t-compcor, and the second value to a-compcor. Note: \
both can be true")
num_noise_components = traits.Int(
6,
usedefault=True,
desc="number of principle components of the noise to use")
regress_before_PCA = traits.Bool(True)
# Highpass Filter
hpcutoff = traits.Float(128., desc="highpass cutoff", usedefault=True)
#zscore
do_zscore = Bool(False)
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
debug = traits.Bool(False)
# Buttons
check_func_datagrabber = Button("Check")
check_field_datagrabber = Button("Check")
def _check_func_datagrabber_fired(self):
subs = self.subjects
for s in subs:
if not os.path.exists(
os.path.join(self.base_dir, self.func_template % s)):
print "ERROR", os.path.join(self.base_dir, self.func_template %
s), "does NOT exist!"
break
else:
print os.path.join(self.base_dir,
self.func_template % s), "exists!"
def _check_field_datagrabber_fired(self):
subs = self.subjects
for s in subs:
if not os.path.exists(
os.path.join(self.field_dir, self.magnitude_template % s)):
print "ERROR:", os.path.join(self.field_dir,
self.magnitude_template %
s), "does NOT exist!"
break
else:
print os.path.join(self.base_dir,
self.magnitude_template % s), "exists!"
if not os.path.exists(
os.path.join(self.field_dir, self.phase_template % s)):
print "ERROR:", os.path.join(
self.field_dir, self.phase_template % s), "does NOT exist!"
break
else:
print os.path.join(self.base_dir,
self.phase_template % s), "exists!"
