class EntryBlock(traits.HasTraits):
fieldName = traits.String(
"fieldName",
desc=
"describes what the information to be entered in the text block is referring to"
)
textBlock = traits.String()
traits_view = traitsui.View(
traitsui.VGroup(traitsui.Item("fieldName",
show_label=False,
style="readonly"),
traitsui.Item("textBlock",
show_label=False,
style="custom"),
show_border=True,
label="information"))
def __init__(self, **traitsDict):
"""user supplies arguments in init to supply class attributes defined above """
super(EntryBlock, self).__init__(**traitsDict)
def clearTextBlock(self):
self.textBlock = ""
class EntryBlock(traits.HasTraits):
fieldName = traits.String("fieldName",desc = "describes what the information to be entered in the text block is referring to")
textBlock = traits.String()
commitButton = traits.Button("save",desc="commit information in text block to logFile")
traits_view = traitsui.View(traitsui.VGroup(
traitsui.Item("fieldName",show_label=False, style="readonly"),
traitsui.Item("textBlock",show_label=False, style="custom"),
traitsui.Item("commitButton",show_label=False), show_border=True, label="information"
))
def __init__(self, **traitsDict):
"""user supplies arguments in init to supply class attributes defined above """
super(EntryBlock,self).__init__(**traitsDict)
def _commitButton_fired(self):
logger.info("saving %s info starting" % self.fieldName)
timeStamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
blockDelimiterStart = "__"+self.fieldName+"__<start>"
blockDelimiterEnd = "__"+self.fieldName+"__<end>"
fullString = "\n"+blockDelimiterStart+"\n"+timeStamp+"\n"+self.textBlock+"\n"+blockDelimiterEnd+"\n"
with open(self.commentFile, "a+") as writeFile:
writeFile.write(fullString)
logger.info("saving %s info finished" % self.fieldName)
def clearTextBlock(self):
self.textBlock = ""
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")
# 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.")
fwhm=traits.List(traits.Float())
inputs_template = traits.String('%s/preproc/output/fwhm_%s/*.nii.gz')
meanfunc_template = traits.String('%s/preproc/mean/*_mean.nii.gz')
fsl_mat_template = traits.String('%s/preproc/bbreg/*.mat')
unwarped_brain_template = traits.String('%s/smri/unwarped_brain/*.nii*')
affine_transformation_template = traits.String('%s/smri/affine_transformation/*.nii*')
warp_field_template = traits.String('%s/smri/warped_field/*.nii*')
#Normalization
standard_transform_template = traits.File(mandatory=True,desc='Standard template to warp to')
standard_warp_field_template = traits.String()
standard_affine_transformation_template = traits.String()
standard_norm_template = traits.File()
standard_warp_field_template = traits.File()
standard_affine_transformation_template = traits.File()
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
# Buttons
check_func_datagrabber = Button("Check")
def getNode(_type,tr,config):
from bips.workflows.flexible_datagrabber import Data, DataBase
if _type == type(traits.Int()):
col_type = colander.SchemaNode(colander.Int(),
name=tr,description=config.trait(tr).desc)
elif _type == type(traits.Float()):
col_type = colander.SchemaNode(colander.Decimal(),name=tr)
elif _type == type(traits.String()) or _type==type(traits.Str()):
col_type = colander.SchemaNode(colander.String(),name=tr)
elif _type == type(traits.Enum('')):
values=config.trait(tr).trait_type.values
the_values = []
for v in values:
the_values.append((v,v))
col_type = colander.SchemaNode(
deform.Set(),
widget=deform.widget.SelectWidget(values=the_values),
name=tr)
elif _type == type(traits.Bool()):
col_type = colander.SchemaNode(colander.Boolean(),widget=deform.widget.CheckboxWidget(),name=tr)
elif _type == type(traits.Code()):
col_type = colander.SchemaNode(colander.String(),name=tr,widget=deform.widget.TextAreaWidget(cols=100,rows=20))
elif _type == type(traits.Instance(Data,())):
from bips.workflows.flexible_datagrabber import create_datagrabber_html_view
col_type = create_datagrabber_html_view()
elif _type == type(traits.List()):
col_type =get_list(_type,tr,config)
else:
print "type: ", _type, "not found!"
col_type = colander.SchemaNode(colander.String(),name=tr)
return col_type
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")
datagrabber = traits.Instance(Data, ())
run_mode = traits.Enum("flame1","ols","flame12")
save_script_only = traits.Bool(False)
#Normalization
brain_mask = traits.File(mandatory=True,desc='Brain Mask')
name_of_project = traits.String("group_analysis",usedefault=True)
do_randomize = traits.Bool(True)
num_iterations = traits.Int(5000)
#Correction:
run_correction = traits.Bool(True)
z_threshold = traits.Float(2.3)
p_threshold = traits.Float(0.05)
connectivity = traits.Int(26)
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
# Buttons
check_func_datagrabber = Button("Check")
class config(HasTraits):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc='Workflow description')
# Directories
working_dir = Directory(mandatory=True, desc="Location of the Nipype working directory")
crash_dir = Directory(mandatory=False, desc="Location to store crash files")
sink_dir = Directory(mandatory=True, desc="Location to store results")
save_script_only = traits.Bool(False)
# Execution
run_using_plugin = Bool(False, usedefault=True, desc="True to run pipeline with plugin, False to run serially")
plugin = traits.Enum("PBS", "PBSGraph","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.')
# Subjects
interpolation = traits.Enum('trilinear','nearestneighbour','sinc',usedefault=True)
name = traits.String('flirt_output',desc='name of folder to store flirt mats')
datagrabber_create = traits.Instance(Data, ())
datagrabber_apply = traits.Instance(Data, ())
create_transform = traits.Bool(True)
apply_transform = traits.Bool(False)
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
class config(BaseWorkflowConfig):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc="Workflow Description")
# Directories
sink_dir = Directory(os.path.abspath('.'),
mandatory=True,
desc="Location where the BIP will store the results")
surf_dir = Directory(mandatory=True, desc="Freesurfer subjects directory")
save_script_only = traits.Bool(False)
datagrabber = traits.Instance(Data, ())
projection_stem = traits.Str('-projfrac-avg 0 1 0.1',
desc='how to project data onto the surface')
out_type = traits.Enum('mat', 'hdf5', desc='mat or hdf5')
hdf5_package = traits.Enum('h5py',
'pytables',
desc='which hdf5 package to use')
target_surf = traits.Enum('fsaverage4',
'fsaverage3',
'fsaverage5',
'fsaverage6',
'fsaverage',
'subject',
desc='which average surface to map to')
surface_fwhm = traits.List([5],
traits.Float(),
mandatory=True,
usedefault=True,
desc="How much to smooth on target surface")
roiname = traits.String('amygdala')
use_advanced_options = Bool(False)
advanced_options = traits.Code()
class Element(traits.HasTraits):
"""parent class for a defined element. Element can be chosen as
a physics property in the physics tab and allow fits to calculate
properties of atomic clouds"""
nameID = traits.String(
desc="name of element for dictionary key (no superscripts etc)")
massATU = traits.Float(22.9897692807,
label="mass (u)",
desc="mass in atomic mass units")
decayRateMHz = traits.Float(
9.7946,
label=u"Decay Rate \u0393 (MHz)",
desc="decay rate/ natural line width of 2S1/2 -> 2P3/2")
crossSectionSigmaPlus = traits.Float(
1.6573163925E-13,
label=u"cross section \u03C3 + (m^2)",
desc=
"resonant cross section 2S1/2 -> 2P3/2. Warning not accurate for 6Li yet"
)
scatteringLength = traits.Float(62.0, label="scattering length (a0)")
IsatSigmaPlus = traits.Float(6.260021,
width=10,
label=u"Isat (mW/cm^2)",
desc="I sat sigma + 2S1/2 -> 2P3/2")
traits_view = traitsui.View(
traitsui.VGroup(
traitsui.Item("nameID", style="readonly"),
traitsui.Item("massATU", style="readonly"),
traitsui.Item("decayRateMHz", style="readonly"),
traitsui.Item("crossSectionSigmaPlus", style="readonly"),
traitsui.Item("scatteringLength"),
traitsui.Item("IsatSigmaPlus", style="readonly")))
class config(HasTraits):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc="Workflow Description")
# Directories
working_dir = Directory(mandatory=True, desc="Location of the Nipype working directory")
sink_dir = Directory(os.path.abspath('.'), 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(mandatory=True, desc= "Freesurfer subjects directory")
save_script_only = traits.Bool(False)
# 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(14.0)
datagrabber = traits.Instance(Data, ())
surface_template = traits.Enum("fsaverage","fsaverage5","fsaverage6","fsaverage4","subject")
test_name = traits.String('FS_one_sample_t_test')
# First Level
#advanced_options
use_advanced_options = Bool(False)
advanced_options = traits.Code()
class config(BaseWorkflowConfig):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc='Workflow description')
# Directories
base_dir = Directory(
exists=True,
desc='Base directory of data. (Should be subject-independent)')
sink_dir = Directory(mandatory=True,
desc="Location where the BIP will store the results")
field_dir = Directory(
exists=True,
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")
save_script_only = traits.Bool(False)
# Subjects
datagrabber = traits.Instance(Data, ())
TR = traits.Float(6.0)
preproc_config = traits.File(desc="preproc config file")
json_name = traits.String('preproc_metrics')
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
class config(HasTraits):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc='Workflow description')
# Directories
working_dir = Directory(mandatory=True,
desc="Location of the Nipype working directory")
crash_dir = Directory(mandatory=False,
desc="Location to store crash files")
save_script_only = traits.Bool(False)
sink_dir = Directory(mandatory=True, desc="Location to store results")
# Execution
run_using_plugin = Bool(
False,
usedefault=True,
desc="True to run pipeline with plugin, False to run serially")
plugin = traits.Enum("PBS",
"PBSGraph",
"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.')
# Subjects
datagrabber = traits.Instance(Data, ())
name = traits.String('mean')
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
class OrNode(OpNode):
"""Parallel evaluation semantics
Children of :class:`OrNode` will be evaluated in parallel, sparked
in the order in which they were added as children of this node.
Example:
>>> @delayed()
... def foo(a): return 42
>>> foo(42) | foo(24)
<cloudmesh_workflow.workflow.OrNode object at ...>
"""
# Implementation notes:
#
# Evaluation is done by sparking all children, then waiting for
# all children.
name = T.String('|')
def start(self):
for child in self.children_iter:
child.start()
def wait(self):
for child in self.children_iter:
child.wait()
self.result.set_result(None)
class config(HasTraits):
# Directories
working_dir = Directory(mandatory=True, desc="Location of the Nipype working directory")
base_dir = Directory(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")
# Execution
run_using_plugin = Bool(False, usedefault=True, desc="True to run pipeline with plugin, False to run serially")
plugin = traits.Enum("PBS", "PBSGraph","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(14.0)
#Subjects
subjects= traits.List(traits.Str, mandatory=True, usedefault=True,
desc="Subject id's. Bips expects dicoms to be organized by subject id's")
dicom_dir_template = traits.String('%s/dicoms/')
#Conversion Options
embed_meta = traits.Bool(True)
info_only = traits.Bool(True)
no_moco = traits.Bool(False,desc="only convert non-moco files")
use_heuristic = traits.Bool(False)
heuristic_file = traits.File(desc="heuristic file")
class InputData(T.HasTraits):
dwi_images = nifti_file
fa_file = nifti_file
bvec_file = T.File(filter=['*.bvec'])
bvec_orientation = T.String('IMG', minlen=3, maxlen=3)
min_signal = T.Float(1)
@T.on_trait_change('dwi_images')
def update_files(self):
dir, file = path.split(self.dwi_images)
base = string.split(file, path.extsep, 1)[0]
if self.fa_file == '':
self.fa_file = path.join(dir, base + '_fa.nii.gz')
if self.bvec_file == '':
self.bvec_file = path.join(dir, base + '.bvec')
def read_data(self):
data_img = nib.load(self.dwi_images)
affine = data_img.get_affine()
voxel_size = data_img.get_header().get_zooms()
voxel_size = voxel_size[:3]
fa_img = nib.load(self.fa_file)
assert data_img.shape[:-1] == fa_img.shape
bvec, bval = read_bvec_file(self.bvec_file)
data_ornt = nib.io_orientation(affine)
if self.bvec_orientation != 'IMG':
bvec = reorient_vectors(bvec, self.bvec_orientation, data_ornt)
fa = fa_img.get_data()
data = data_img.get_data()
return data, voxel_size, affine, fa, bvec, bval
class config(HasTraits):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc="Workflow Description")
# Directories
working_dir = Directory(mandatory=True,
desc="Location of the Nipype working directory")
sink_dir = Directory(os.path.abspath('.'),
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(mandatory=True, desc="Freesurfer subjects directory")
# 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(14.0)
datagrabber = traits.Instance(Data, ())
projection_stem = traits.Str('-projfrac-avg 0 1 0.1',
desc='how to project data onto the surface')
out_type = traits.Enum('mat', 'hdf5', desc='mat or hdf5')
hdf5_package = traits.Enum('h5py',
'pytables',
desc='which hdf5 package to use')
target_surf = traits.Enum('fsaverage4',
'fsaverage3',
'fsaverage5',
'fsaverage6',
'fsaverage',
'subject',
desc='which average surface to map to')
surface_fwhm = traits.List([5],
traits.Float(),
mandatory=True,
usedefault=True,
desc="How much to smooth on target surface")
roiname = traits.String('amygdala')
use_advanced_options = Bool(False)
advanced_options = traits.Code()
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")
# 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(14.0)
datagrabber = traits.Instance(Data, ())
run_mode = traits.Enum("flame1", "ols", "flame12")
save_script_only = traits.Bool(False)
#Normalization
brain_mask = traits.File(mandatory=True, desc='Brain Mask')
name_of_project = traits.String("group_analysis", usedefault=True)
do_randomize = traits.Bool(True)
num_iterations = traits.Int(5000)
#Correction:
run_correction = traits.Bool(True)
z_threshold = traits.Float(2.3)
p_threshold = traits.Float(0.05)
connectivity = traits.Int(26)
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
# Buttons
check_func_datagrabber = Button("Check")
def edition_widget(engine, environment):
''' Edition GUI for FSL config - see
:class:`~capsul.qt_gui.widgets.settings_editor.SettingsEditor`
'''
from soma.qt_gui.controller_widget import ScrollControllerWidget
from soma.controller import Controller
import types
import traits.api as traits
def validate_config(widget):
controller = widget.controller_widget.controller
with widget.engine.settings as session:
conf = session.config('fsl', widget.environment)
values = {'config_id': 'fsl'}
for k in ('directory', 'config', 'prefix'):
value = getattr(controller, k)
if value is traits.Undefined:
value = None
values[k] = value
if conf is None:
session.new_config('fsl', widget.environment, values)
else:
for k, value in values.items():
if k == 'config_id':
continue
setattr(conf, k, values[k])
controller = Controller()
controller.add_trait(
'directory',
traits.Directory(traits.Undefined,
desc='Directory where FSL is installed'))
controller.add_trait(
'config',
traits.File(traits.Undefined,
output=False,
desc='Parameter to specify the fsl.sh path'))
controller.add_trait(
'prefix',
traits.String(traits.Undefined, desc='Prefix to add to FSL commands'))
conf = engine.settings.select_configurations(environment, {'fsl': 'any'})
if conf:
fconf = conf.get('capsul.engine.module.fsl', {})
controller.directory = fconf.get('directory', traits.Undefined)
controller.config = fconf.get('config', traits.Undefined)
controller.prefix = fconf.get('prefix', traits.Undefined)
widget = ScrollControllerWidget(controller, live=True)
widget.engine = engine
widget.environment = environment
widget.accept = types.MethodType(validate_config, widget)
return widget
class newDarkPictureDialog(traits.HasTraits):
# pathSourceImages = traits.Directory( os.path.join("\\\\192.168.16.71","Humphry","Data","eagleLogs") )
pathSourceImages = traits.Directory( eagleLogsFolder )
pathNewDarkPicture = traits.File( defaultDarkPictureFilename, editor = traitsui.FileEditor(dialog_style='save') )
cancelButton = traitsui.Action(name = 'Cancel', action = '_cancel')
okButton = traitsui.Action(name = 'Calculate dark picture', action = '_ok')
date = traits.String( time.strftime('%Y %m %d'), desc='Date' )
camera = traits.String( "Andor1" )
interval = traits.Float(0.003)
filterCountLi = traits.Int(1)
temperature = traits.Float(-40.0)
autoFilename = traits.Button('Auto Filename')
traits_view = traitsui.View(
traitsui.Group(
traitsui.Item('pathSourceImages'),
traitsui.Group(
traitsui.Item('date'),
traitsui.Item('camera'),
traitsui.Item('interval'),
traitsui.Item('temperature'),
traitsui.Item('autoFilename'),
label='Auto Filename', show_border=True
),
traitsui.Item('pathNewDarkPicture')
),
buttons = [cancelButton, okButton],
handler = newDarkPictureDialogHandler()
)
def _autoFilename_fired(self):
filename = self.date + ' - dark ' + self.camera + ' - '
filename += 'interval {} '.format(self.interval)
filename += 'temperature {} '.format(self.temperature)
filename = filename.replace('.','_')
# filename += '.gz'
filename += '.npy'
path = os.path.join( defaultDarkPictureFilename, self.camera)
if not os.path.exists( path ):
os.mkdir( path )
self.pathNewDarkPicture = os.path.join( path, filename )
class Model(traits.HasTraits):
model = traits.Instance(lmfit.Model)
function = None #python function for fitting
guessFunction = None
definitionString = traits.String()
modelName = traits.String("")
def __init__(self, function, **traitsDict):
super(Model, self).__init__(**traitsDict)
self.function = function
self.model = lmfit.Model(function)
self.parameters = self.model.make_params()
try:
self.definitionString = inspect.getsource(function)
except IOError as e:
self.definitionString = e.message
if hasattr(fittingFunctions, function.__name__ + "_guess"):
self.guessFunction = getattr(fittingFunctions,
function.__name__ + "_guess")
class config(BaseWorkflowConfig):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc='Workflow description')
# Directories
base_dir = Directory(
os.path.abspath('.'),
exists=True,
desc='Base directory of data. (Should be subject-independent)')
sink_dir = Directory(mandatory=True,
desc="Location where the BIP will store the results")
field_dir = Directory(
exists=True,
desc="Base directory of field-map data (Should be subject-independent) \
Set this value to None if you don't want fieldmap distortion correction"
)
json_sink = Directory(mandatory=False, desc="Location to store json_files")
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 = Bool(True, usedefault=True)
# Motion Correction
do_slicetiming = Bool(True,
usedefault=True,
desc="Perform slice timing correction")
SliceOrder = traits.List(traits.Int)
TR = traits.Float(mandatory=True, desc="TR of functional")
save_script_only = traits.Bool(False)
# Buttons
check_func_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!"
class OrNode(OpNode):
name = T.String('|')
def start(self):
for child in self.children_iter:
child.start()
def wait(self):
for child in self.children_iter:
child.wait()
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(mandatory=True, desc="Freesurfer subjects directory")
# 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(14.0)
# 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.")
fwhm=traits.List(traits.Float())
inputs_template = traits.String('%s/preproc/output/fwhm_%s/*.nii.gz')
meanfunc_template = traits.String('%s/preproc/mean/*_mean.nii.gz')
fsl_mat_template = traits.String('%s/preproc/bbreg/*.mat')
#Normalization
norm_template = traits.File(mandatory=True,desc='Template to warp to')
do_segment = traits.Bool(False)
do_anatomical_only = traits.Bool(True)
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
save_script_only = traits.Bool(False)
# Buttons
check_func_datagrabber = Button("Check")
class Option(traits.HasTraits):
name = traits.String(
desc="key from options dictionary. describes the option")
value = traits.Any()
traits_view = traitsui.View(
traitsui.HGroup(
traitsui.Item("name",
style="readonly",
springy=True,
show_label=False),
traitsui.Item("value", show_label=False, springy=True)))
class config(HasTraits):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc='Workflow description')
# Directories
working_dir = Directory(mandatory=True,
desc="Location of the Nipype working directory")
base_dir = Directory(
exists=True,
desc='Base directory of data. (Should be subject-independent)')
sink_dir = Directory(mandatory=True,
desc="Location where the BIP will store the results")
field_dir = Directory(
exists=True,
desc="Base directory of field-map data (Should be subject-independent) \
Set this value to None if you don't want fieldmap distortion correction"
)
crash_dir = Directory(mandatory=False,
desc="Location to store crash files")
surf_dir = Directory(mandatory=True, desc="Freesurfer subjects directory")
save_script_only = traits.Bool(False)
# Execution
run_using_plugin = Bool(
False,
usedefault=True,
desc="True to run pipeline with plugin, False to run serially")
plugin = traits.Enum("PBS",
"PBSGraph",
"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. '
)
# Subjects
datagrabber = traits.Instance(Data, ())
TR = traits.Float(6.0)
preproc_config = traits.File(desc="preproc config file")
json_name = traits.String('preproc_metrics')
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
class SBFindOptParamInputSpec(CommandLineInputSpec):
freq_loc = File(exists=True, desc='Input freq filename',
argstr="%s", position=1,
copyfile=False, mandatory=True)
reliability_mask_loc = File(exists=True, desc='Phase voxels to leave out of data fidelity term',
argstr="%s", position=3,
copyfile=False, mandatory=False)
mask_loc = File(exists=True, desc = 'Mask (3D)', argstr="%s",
position=2,copyfile=False,mandatory=True)
result_mat_file_loc = File(desc="Output mat filename where optimal param is stored",argstr='%s',position=4,
mandatory=True)
quit_matlab = traits.String(';quit;"',desc='needed to quit matlab',argstr='%s',
position=7,mandatory=False,usedefault=True)
class config(BaseWorkflowConfig):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc='Workflow description')
# Directories
save_script_only = traits.Bool(False)
sink_dir = Directory(mandatory=True, desc="Location to store results")
# Subjects
datagrabber = traits.Instance(Data, ())
name = traits.String('mean')
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
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)
do_slicetiming = Bool(True, usedefault=True, desc="Perform slice timing correction")
SliceOrder = traits.List(traits.Int)
order = traits.Enum('motion_slicetime','slicetime_motion',use_default=True)
TR = traits.Float(mandatory=True, desc = "TR of functional")
motion_correct_node = traits.Enum('nipy','fsl','spm','afni',
desc="motion correction algorithm to use",
usedefault=True,)
csf_prob = traits.File(desc='CSF_prob_map')
grey_prob = traits.File(desc='grey_prob_map')
white_prob = traits.File(desc='white_prob_map')
# 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.Float(6.0,usedefault=True)
save_script_only = traits.Bool(False)
check_func_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!"
class ImHistMatchInputSpec(CommandLineInputSpec):
in_file = File(exists=True, desc='Input filename',
argstr='%s', position=1,
copyFile=False, mandatory=True)
ref = File(exists=True, desc = 'Reference filename', argstr='%s',
position=2,copyFile=False,mandatory=True)
result_filename = File(desc="Output LFS filename",argstr='%s',position=3,
mandatory=True)
quit_matlab = traits.String(';quit;"',desc='needed to quit matlab',argstr='%s',
position=4,mandatory=False,usedefault=True)
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(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."
)
fwhm = traits.List(traits.Float())
inputs_template = traits.String('%s/preproc/output/fwhm_%s/*.nii.gz')
meanfunc_template = traits.String('%s/preproc/mean/*_mean.nii.gz')
fsl_mat_template = traits.String('%s/preproc/bbreg/*.mat')
#Normalization
norm_template = traits.File(mandatory=True, desc='Template to warp to')
do_segment = traits.Bool(False)
do_anatomical_only = traits.Bool(True)
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
save_script_only = traits.Bool(False)
# Buttons
check_func_datagrabber = Button("Check")
class config(BaseWorkflowConfig):
uuid = traits.Str(desc="UUID")
desc = traits.Str(desc="Workflow Description")
# Directories
sink_dir = Directory(os.path.abspath('.'), mandatory=True, desc="Location where the BIP will store the results")
surf_dir = Directory(mandatory=True, desc= "Freesurfer subjects directory")
save_script_only = traits.Bool(False)
datagrabber = traits.Instance(Data, ())
surface_template = traits.Enum("fsaverage","fsaverage5","fsaverage6","fsaverage4","subject")
test_name = traits.String('FS_one_sample_t_test')
# First Level
#advanced_options
use_advanced_options = Bool(False)
advanced_options = traits.Code()