class PlotConfig(t.HasTraits):
saturated_pixels = t.CFloat(0.05,
label='Saturated pixels',
desc='Set the default saturated pixels value '
'for plotting images.'
)
cmap_navigator = t.Enum(list(cmap_d.keys()),
label='Color map navigator',
desc='Set the default color map for the navigator.',
)
cmap_signal = t.Enum(list(cmap_d.keys()),
label='Color map signal',
desc='Set the default color map for the signal plot.',
)
dims_024_increase = t.Str('right',
label='Navigate right'
)
dims_024_decrease = t.Str('left',
label='Navigate left',
)
dims_135_increase = t.Str('down',
label='Navigate down',
)
dims_135_decrease = t.Str('up',
label='Navigate up',
)
modifier_dims_01 = t.Enum(['ctrl', 'alt', 'shift', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'], label='Modifier key for 1st and 2nd dimensions') # 0 elem is default
modifier_dims_23 = t.Enum(['shift', 'alt', 'ctrl', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'], label='Modifier key for 3rd and 4th dimensions') # 0 elem is default
modifier_dims_45 = t.Enum(['alt', 'ctrl', 'shift', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'], label='Modifier key for 5th and 6th dimensions') # 0 elem is default
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 GeneralConfig(t.HasTraits):
default_file_format = t.Enum(
'hdf5',
'rpl',
desc='Using the hdf5 format is highly reccomended because is the '
'only one fully supported. The Ripple (rpl) format it is useful '
'tk is provided for when none of the other toolkits are'
' available. However, when using this toolkit the '
'user interface elements are not available. '
'to export data to other software that do not support hdf5')
default_export_format = t.Enum(
*default_write_ext,
desc='Using the hdf5 format is highly reccomended because is the '
'only one fully supported. The Ripple (rpl) format it is useful '
'to export data to other software that do not support hdf5')
hspy_extension = t.CBool(
False,
desc='If enabled, HyperSpy will use the "hspy" extension when saving '
'to HDF5 instead of the "hdf5" extension. "hspy" will be the default'
'extension from HyperSpy v1.3')
interactive = t.CBool(
True,
desc='If enabled, HyperSpy will prompt the user when options are '
'available, otherwise it will use the default values if possible')
logger_on = t.CBool(
False,
label='Automatic logging',
desc='If enabled, HyperSpy will store a log in the current directory '
'of all the commands typed')
show_progressbar = t.CBool(
True,
label='Show progress bar',
desc='If enabled, show a progress bar when available')
dtb_expand_structures = t.CBool(
True,
label='Expand structures in DictionaryTreeBrowser',
desc='If enabled, when printing DictionaryTreeBrowser (e.g. '
'metadata), long lists and tuples will be expanded and any '
'dictionaries in them will be printed similar to '
'DictionaryTreeBrowser, but with double lines')
logging_level = t.Enum([
'CRITICAL',
'ERROR',
'WARNING',
'INFO',
'DEBUG',
],
desc='the log level of all hyperspy modules.')
parallel = t.CBool(
True, desc='Use parallel threads for computations by default.')
lazy = t.CBool(False, desc='Load data lazily by default.')
def _logger_on_changed(self, old, new):
if new is True:
turn_logging_on()
else:
turn_logging_off()
class GeneralConfig(t.HasTraits):
default_file_format = t.Enum(
'hdf5',
'rpl',
desc='Using the hdf5 format is highly reccomended because is the '
'only one fully supported. The Ripple (rpl) format it is useful '
'to export data to other software that do not support hdf5')
default_export_format = t.Enum(
*default_write_ext,
desc='Using the hdf5 format is highly reccomended because is the '
'only one fully supported. The Ripple (rpl) format it is useful '
'to export data to other software that do not support hdf5')
plot_on_load = t.CBool(
False,
desc='If enabled, the object will be plot automatically on loading')
interactive = t.CBool(
True,
desc='If enabled, Hyperspy will prompt the user when optios are '
'available, otherwise it will use the default values if possible')
logger_on = t.CBool(
False,
label='Automatic logging',
desc='If enabled, Hyperspy will store a log in the current directory '
'of all the commands typed')
def _logger_on_changed(self, old, new):
if new is True:
turn_logging_on()
else:
turn_logging_off()
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 PlotConfig(t.HasTraits):
dims_024_increase = t.Str('right', label='Navigate right')
dims_024_decrease = t.Str(
'left',
label='Navigate left',
)
dims_135_increase = t.Str(
'down',
label='Navigate down',
)
dims_135_decrease = t.Str(
'up',
label='Navigate up',
)
modifier_dims_01 = t.Enum(
[
'ctrl', 'alt', 'shift', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'
],
label='Modifier key for 1st and 2nd dimensions') # 0 elem is default
modifier_dims_23 = t.Enum(
[
'shift', 'alt', 'ctrl', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'
],
label='Modifier key for 3rd and 4th dimensions') # 0 elem is default
modifier_dims_45 = t.Enum(
[
'alt', 'ctrl', 'shift', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'
],
label='Modifier key for 5th and 6th dimensions') # 0 elem is default
class AxisSelector(traits.HasTraits):
"""here we select what axes the user should use when plotting this data """
masterList = traits.List
masterListWithNone = traits.List
xAxis = traits.Enum(values="masterList")
yAxis = traits.Enum(values="masterList")
series = traits.Enum(values="masterListWithNone")
traits_view=traitsui.View(traitsui.VGroup(traitsui.Item("xAxis",label="x axis"),traitsui.Item("yAxis",label="y axis"),
traitsui.Item("series",label="series"),show_border=True, label="axes selection"))
def __init__(self, **traitsDict):
"""allows user to select which axes are useful for plotting in this log"""
super(AxisSelector, self).__init__(**traitsDict)
def _masterList_default(self):
"""gets the header row of log file which are interpreted as the column
names that can be plotted."""
logger.info("updating master list of axis choices")
logger.debug("comment file = %s" % self.logFile)
logger.debug( "comment file = %s" % self.logFile)
if not os.path.exists(self.logFile):
return []
try:
with open(self.logFile) as csvfile:
headerReader = csv.reader(csvfile)
headerRow=headerReader.next()
return headerRow
except IOError:
return []
def _masterListWithNone_default(self):
return ["None"]+self._masterList_default()
class config(HasTraits):
uuid = traits.Str(desc="UUID")
# Directories
working_dir = Directory(mandatory=True, desc="Location of the Nipype working directory")
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")
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. ')
# DataGrabber
datagrabber = datagrabber = traits.Instance(Data, ())
# filter
highpass_freq = traits.Float()
lowpass_freq = traits.Float()
filtering_algorithm = traits.Enum("fsl","IIR","FIR","Fourier")
tr = traits.Float()
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(HasTraits):
files = traits.List(traits.File)
pattern = traits.Str()
base_directory = Directory
use_pattern = traits.Bool(False)
hemi = traits.Enum('lh', 'rh')
surface = traits.Enum('white', 'inflated')
target = traits.Enum('fsaverage5', 'fsaverage4', 'fsaverage')
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")
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=False,
desc="Location to store BIPS results")
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.')
# Subjects
datagrabber = traits.Instance(Data, ())
# 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,
)
use_metadata = traits.Bool(True)
order = traits.Enum('motion_slicetime',
'slicetime_motion',
use_default=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)
# Advanced Options
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
class MultiMeshMorpher(ta.HasTraits):
visible = ta.Enum(values='_names')
morph_target = ta.Enum(values='_names')
morph_alpha = ta.Range(0.0, 1.0, 0.0)
show_edges = ta.Bool(False)
_names = ta.List()
def __init__(self, list_verts, tris, names=None, fig=None, **kw):
super(MultiMeshMorpher, self).__init__(**kw)
self._list_verts = list_verts
self._tris = tris
if fig is None:
self._fig = mlab.figure(bgcolor=(1, 1, 1))
else:
self._fig = fig
if names is None:
names = map(str, range(len(list_verts)))
self._names = list(names)
self._verts_by_name = dict(zip(self._names, list_verts))
self._actor, self._pd = mesh_as_vtk_actor(list_verts[0], tris, return_polydata=True)
self._actor.property.set(
ambient=0.0,
specular=0.15,
specular_power=128.,
diffuse=0.8,
)
self._fig.scene.add_actor(self._actor)
self.visible = self._names[0]
if len(self._names) > 1:
self.morph_target = self._names[1]
@ta.on_trait_change('visible, show_edges, morph_target, morph_alpha')
def _update(self):
self._actor.property.edge_visibility = self.show_edges
v1 = self._verts_by_name[self.visible]
if self.morph_alpha > 0:
v2 = self._verts_by_name[self.morph_target]
self._pd.points = v1 * (1 - self.morph_alpha) + v2 * self.morph_alpha
else:
self._pd.points = v1
self._fig.scene.render()
view = tu.View(
tu.Group(
tu.Item('visible'),
tu.Item('morph_target'),
tu.Item('morph_alpha'),
tu.Item('show_edges', name='Wireframe'),
label="Viewer"),
title="MultiMeshMorpher"
)
class PlotConfig(t.HasTraits):
saturated_pixels = t.CFloat(
0.,
label='Saturated pixels (deprecated)',
desc='Warning: this is deprecated and will be removed in HyperSpy v2.0'
)
# Don't use t.Enum to list all possible matplotlib colormap to
# avoid importing matplotlib and building the list of colormap
# when importing hyperpsy
cmap_navigator = t.Str(
'gray',
label='Color map navigator',
desc='Set the default color map for the navigator.',
)
cmap_signal = t.Str(
'gray',
label='Color map signal',
desc='Set the default color map for the signal plot.',
)
dims_024_increase = t.Str('right', label='Navigate right')
dims_024_decrease = t.Str(
'left',
label='Navigate left',
)
dims_135_increase = t.Str(
'down',
label='Navigate down',
)
dims_135_decrease = t.Str(
'up',
label='Navigate up',
)
modifier_dims_01 = t.Enum(
[
'ctrl', 'alt', 'shift', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'
],
label='Modifier key for 1st and 2nd dimensions') # 0 elem is default
modifier_dims_23 = t.Enum(
[
'shift', 'alt', 'ctrl', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'
],
label='Modifier key for 3rd and 4th dimensions') # 0 elem is default
modifier_dims_45 = t.Enum(
[
'alt', 'ctrl', 'shift', 'ctrl+alt', 'ctrl+shift', 'alt+shift',
'ctrl+alt+shift'
],
label='Modifier key for 5th and 6th dimensions') # 0 elem is default
pick_tolerance = t.CFloat(
7.5,
label='Pick tolerance',
desc='The pick tolerance of ROIs in screen pixels.')
class MachineLearningConfig(t.HasTraits):
export_factors_default_file_format = t.Enum(*default_write_ext)
export_loadings_default_file_format = t.Enum(*default_write_ext)
multiple_files = t.Bool(True,
label = 'Export to multiple files',
desc = 'If enabled, on exporting the PCA or ICA results one file'
'per factor and loading will be created. Otherwise only two files'
'will contain the factors and loadings')
same_window = t.Bool(True,
label = 'Plot components in the same window',
desc = 'If enabled the principal and independent components will all'
' be plotted in the same window')
class GeneralConfig(t.HasTraits):
default_file_format = t.Enum(
'hdf5',
'rpl',
desc='Using the hdf5 format is highly reccomended because is the '
'only one fully supported. The Ripple (rpl) format it is useful '
'to export data to other software that do not support hdf5')
default_toolkit = t.Enum(
"qt4",
"gtk",
"wx",
"tk",
"None",
desc="Default toolkit for matplotlib and the user interface "
"elements. "
"When using gtk and tk the user interface elements are not"
" available."
"None is suitable to run headless. "
"HyperSpy must be restarted for changes to take effect")
default_export_format = t.Enum(
*default_write_ext,
desc='Using the hdf5 format is highly reccomended because is the '
'only one fully supported. The Ripple (rpl) format it is useful '
'to export data to other software that do not support hdf5')
interactive = t.CBool(
True,
desc='If enabled, HyperSpy will prompt the user when options are '
'available, otherwise it will use the default values if possible')
logger_on = t.CBool(
False,
label='Automatic logging',
desc='If enabled, HyperSpy will store a log in the current directory '
'of all the commands typed')
show_progressbar = t.CBool(
True,
label='Show progress bar',
desc='If enabled, show a progress bar when available')
dtb_expand_structures = t.CBool(
True,
label='Expand structures in DictionaryTreeBrowser',
desc='If enabled, when printing DictionaryTreeBrowser (e.g. metadata), '
'long lists and tuples will be expanded and any dictionaries in them will be '
'printed similar to DictionaryTreeBrowser, but with double lines')
def _logger_on_changed(self, old, new):
if new is True:
turn_logging_on()
else:
turn_logging_off()
class PlotOptions(tr.HasTraits):
columns_headers_list = tr.List([])
x_axis = tr.Enum(values='columns_headers_list')
y_axis = tr.Enum(values='columns_headers_list')
x_axis_multiplier = tr.Enum(1, -1)
y_axis_multiplier = tr.Enum(-1, 1)
plot = tr.Button
view = ui.View(
ui.HGroup(ui.Item('x_axis'), ui.Item('x_axis_multiplier')),
ui.HGroup(ui.Item('y_axis'), ui.Item('y_axis_multiplier')),
ui.Item('plot', show_label=False)
)
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 GeneralConfig(t.HasTraits):
default_file_format = t.Enum(
'hdf5',
'rpl',
desc='Using the hdf5 format is highly reccomended because is the '
'only one fully supported. The Ripple (rpl) format it is useful '
'tk is provided for when none of the other toolkits are'
' available. However, when using this toolkit the '
'user interface elements are not available. '
'to export data to other software that do not support hdf5')
default_toolkit = t.Enum(
"qt4",
"gtk",
"wx",
"tk",
"None",
desc="Default toolkit for matplotlib and the user interface "
"elements. "
"When using gtk and tk the user interface elements are not"
" available."
"user interface elements are not available. "
"None is suitable to run headless. "
"HyperSpy must be restarted for changes to take effect")
default_export_format = t.Enum(
*default_write_ext,
desc='Using the hdf5 format is highly reccomended because is the '
'only one fully supported. The Ripple (rpl) format it is useful '
'to export data to other software that do not support hdf5')
interactive = t.CBool(
True,
desc='If enabled, HyperSpy will prompt the user when optios are '
'available, otherwise it will use the default values if possible')
logger_on = t.CBool(
False,
label='Automatic logging',
desc='If enabled, HyperSpy will store a log in the current directory '
'of all the commands typed')
show_progressbar = t.CBool(
True,
label='Show progress bar',
desc='If enabled, show a progress bar when available')
def _logger_on_changed(self, old, new):
if new is True:
turn_logging_on()
else:
turn_logging_off()
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 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(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. ')
# 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.")
# Preprocessing info
preproc_config = traits.File(desc="preproc json file")
#Advanced
use_advanced_options = traits.Bool()
advanced_script = traits.Code()
save_script_only = traits.Bool(False)
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 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 Options(t.HasTraits):
options = t.Enum(('a'))
def __init__(self, options=None):
if not options:
options = ['a', 'b', 'c']
self.options = options
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)
subjects = traits.List(desc="subjects")
split_files = traits.List(traits.File(),desc="""list of split files""")
# First Level
#advanced_options
use_advanced_options = Bool(False)
advanced_options = traits.Code()
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 LineValueVi3D(Viz3D):
glyph_source = t.Enum('cone_source')
def plot(self):
m = self.ftv.mlab
x, y, z, u, v, w, scalars, scale_factor = self.get_values()
vectors = m.quiver3d(x, y, z, u, v, w, name='Bending moments')
vectors.glyph.glyph_source.glyph_source = \
vectors.glyph.glyph_source.glyph_dict[self.glyph_source]
vectors.glyph.glyph.scale_factor = scale_factor
vectors.glyph.glyph.clamping = False
vectors.glyph.color_mode = 'color_by_scalar'
vectors.glyph.glyph_source.glyph_source.center = np.array([0., 0., 0.])
ds = vectors.mlab_source.dataset
ds.point_data.scalars = scalars
ds.point_data.scalars.name = 'bending moments'
ds.point_data.vectors = np.c_[u, v, w]
ds.point_data.vectors.name = 'directions'
self.pipes['vectors'] = vectors
def update(self, vot=0.0):
x, y, z, u, v, w, scalars, scale_factor = self.get_values()
vectors = self.pipes['vectors']
vectors.mlab_source.set(x=x, y=y, z=z, u=u, v=v, w=w)
vectors.mlab_source.set(scalars=scalars)
vectors.mlab_source.set(vectors=np.c_[u, v, w])
vectors.glyph.glyph.scale_factor = scale_factor
lut = vectors.module_manager.scalar_lut_manager
lut.set(show_scalar_bar=True, show_legend=True, data_name='moment')
class ButterworthFilter(Smoothing):
cutoff_frequency_ratio = t.Range(0., 1., 0.05)
type = t.Enum('low', 'high')
order = t.Int(2)
view = tu.View(
tu.Group('cutoff_frequency_ratio', 'order', 'type'),
kind='live',
handler=SmoothingHandler,
buttons=OKCancelButtons,
title='Butterworth filter',
)
def _cutoff_frequency_ratio_changed(self, old, new):
self.update_lines()
def _type_changed(self, old, new):
self.update_lines()
def _order_changed(self, old, new):
self.update_lines()
def model2plot(self, axes_manager=None):
b, a = sp.signal.butter(self.order, self.cutoff_frequency_ratio,
self.type)
smoothed = sp.signal.filtfilt(b, a, self.signal())
return smoothed
class ButterworthFilter(Smoothing):
cutoff_frequency_ratio = t.Range(0.01, 1., 0.01)
type = t.Enum('low', 'high')
order = t.Int(2)
def _cutoff_frequency_ratio_changed(self, old, new):
self.update_lines()
def _type_changed(self, old, new):
self.update_lines()
def _order_changed(self, old, new):
self.update_lines()
def model2plot(self, axes_manager=None):
b, a = sp.signal.butter(self.order, self.cutoff_frequency_ratio,
self.type)
smoothed = sp.signal.filtfilt(b, a, self.signal())
return smoothed
def apply(self):
b, a = sp.signal.butter(self.order, self.cutoff_frequency_ratio,
self.type)
f = functools.partial(sp.signal.filtfilt, b, a)
self.signal.map(f)
