def run_spm_preprocessing(funcfile,
outdir,
repetition_time,
ref_slice,
slice_order,
template,
timings_corr_algo,
normalization,
spm_bin,
fsl_config,
enable_display=False):
"""
"""
print "Study_config init..."
study_config = StudyConfig(
modules=["MatlabConfig", "SPMConfig", "FSLConfig", "NipypeConfig"],
use_smart_caching=False,
fsl_config=fsl_config,
use_fsl=True,
use_matlab=False,
use_spm=True,
spm_exec=spm_bin,
spm_standalone=True,
use_nipype=True,
output_directory=outdir,
)
print " ... done."
# Processing definition: create the <clinfmri.preproc.FmriPreproc> that
# define the different step of the processings.
pipeline = get_process_instance(
"clinfmri.preproc.converted_fmri_preproc.xml")
# It is possible to display the pipeline.
if enable_display:
import sys
from PySide import QtGui
from capsul.qt_gui.widgets import PipelineDevelopperView
app = QtGui.QApplication(sys.argv)
view = PipelineDevelopperView(pipeline)
view.show()
app.exec_()
# Now to parametrize the pipeline pipeline.
pipeline.fmri_file = funcfile
pipeline.realign_register_to_mean = True
pipeline.select_slicer = timings_corr_algo
pipeline.select_normalization = normalization
pipeline.force_repetition_time = repetition_time
pipeline.force_slice_orders = slice_order
pipeline.realign_wrap = [0, 1, 0]
pipeline.realign_write_wrap = [0, 1, 0]
pipeline.ref_slice = ref_slice
if template is not None:
pipeline.template_file = template
# The pipeline is now ready to be executed.
study_config.run(pipeline, executer_qc_nodes=False, verbose=1)
class TestQCNodes(unittest.TestCase):
""" Test pipeline node types.
"""
def setUp(self):
""" Initialize the TestQCNodes class
"""
self.pipeline = MyPipeline()
self.output_directory = tempfile.mkdtemp()
self.study_config = StudyConfig(output_directory=self.output_directory)
def __del__(self):
""" Remove temporary items.
"""
shutil.rmtree(self.output_directory)
def test_qc_active(self):
""" Method to test if the run qc option works properly.
"""
# Execute all the pipeline nodes
self.study_config.run(self.pipeline, executer_qc_nodes=True)
# Get the list of all the nodes that havec been executed
execution_list = self.pipeline.workflow_ordered_nodes()
# Go through all the executed nodes
for process_node in execution_list:
# Get the process instance that has been executed
process_instance = process_node.process
# Check that the node has been executed
self.assertEqual(process_instance.log_file, "in")
def test_qc_inactive(self):
""" Method to test if the run qc option works properly.
"""
# Execute all the pipeline nodes
self.study_config.run(self.pipeline, executer_qc_nodes=False)
# Get the list of all the nodes
execution_list = self.pipeline.workflow_ordered_nodes()
# Go through all the nodes
for process_node in execution_list:
# Get the process instance that may have been executed
process_instance = process_node.process
# Check that view nodes are not executed
if process_node.node_type == "view_node":
self.assertEqual(process_instance.log_file, None)
else:
self.assertEqual(process_instance.log_file, "in")
def setUp(self):
default_config = SortedDictionary(
("use_soma_workflow", True)
)
# use a custom temporary soma-workflow dir to avoid concurrent
# access problems
tmpdb = tempfile.mkstemp('', prefix='soma_workflow')
os.close(tmpdb[0])
os.unlink(tmpdb[1])
self.soma_workflow_temp_dir = tmpdb[1]
os.mkdir(self.soma_workflow_temp_dir)
swf_conf = StringIO.StringIO('[%s]\nSOMA_WORKFLOW_DIR = %s\n' \
% (socket.gethostname(), tmpdb[1]))
swconfig.Configuration.search_config_path \
= staticmethod(lambda : swf_conf)
self.study_config = StudyConfig(init_config=default_config)
self.atomic_pipeline = MyAtomicPipeline()
self.composite_pipeline = MyCompositePipeline()
def pilot(working_dir="/volatile/nsap/caps", **kwargs):
"""
===============================
Diffusion Brain Extraction Tool
===============================
.. topic:: Objective
We propose to extract the brain mask from a diffusion sequence.
Import
------
First we load the function that enables us to access the toy datasets
"""
from caps.toy_datasets import get_sample_data
"""
From capsul we then load the class to configure the study we want to
perform
"""
from capsul.study_config import StudyConfig
"""
Here two utility tools are loaded. The first one enables the creation
of ordered dictionary and the second ensure that a directory exist.
Note that the directory will be created if necessary.
"""
from capsul.utils.sorted_dictionary import SortedDictionary
from nsap.lib.base import ensure_is_dir
"""
Load the toy dataset
--------------------
We want to perform BET on a diffusion sequence.
To do so, we use the *get_sample_data* function to load this
dataset.
.. seealso::
For a complete description of the *get_sample_data* function, see the
:ref:`Toy Datasets documentation <toy_datasets_guide>`
"""
toy_dataset = get_sample_data("dwi")
"""
The *toy_dataset* is an Enum structure with some specific
elements of interest *dwi*, *bvals* that contain the nifti diffusion
image and the b-values respectively.
"""
print(toy_dataset.dwi, toy_dataset.bvals)
"""
Will return:
.. code-block:: python
/home/ag239446/git/nsap-src/nsap/data/DTI30s010.nii
/home/ag239446/git/nsap-src/nsap/data/DTI30s010.bval
We can see that the image has been found in a local directory
Processing definition
---------------------
Now we need to define the processing step that will perform BET on
diffusion sequence.
"""
bet_pipeline = dBET()
"""
It is possible to access the ipeline input specification.
"""
print(bet_pipeline.get_input_spec())
"""
Will return the input parameters the user can set:
.. code-block:: python
INPUT SPECIFICATIONS
dw_image: ['File']
bvals: ['File']
specified_index_of_ref_image: ['Int']
terminal_output: ['Enum']
generate_binary_mask: ['Bool']
use_4d_input: ['Bool']
generate_mesh: ['Bool']
generate_skull: ['Bool']
bet_threshold: ['Float']
We can now tune the pipeline parameters.
We first set the input dwi file:
"""
bet_pipeline.dw_image = toy_dataset.dwi
"""
And set the b-values file
"""
bet_pipeline.bvals = toy_dataset.bvals
"""
Study Configuration
-------------------
The pipeline is now set up and ready to be executed.
For a complete description of a study execution, see the
:ref:`Study Configuration description <study_configuration_guide>`
"""
bet_working_dir = os.path.join(working_dir, "diffusion_bet")
ensure_is_dir(bet_working_dir)
default_config = SortedDictionary(
("output_directory", bet_working_dir),
("fsl_config", "/etc/fsl/4.1/fsl.sh"),
("use_fsl", True),
("use_smart_caching", True),
("generate_logging", True)
)
study = StudyConfig(default_config)
study.run(bet_pipeline)
"""
Results
-------
Finally, we print the pipeline outputs
"""
print("\nOUTPUTS\n")
for trait_name, trait_value in bet_pipeline.get_outputs().iteritems():
print("{0}: {1}".format(trait_name, trait_value))
"""
# Create the subject output directory
soutdir = os.path.join(args.outdir, args.sid, "EPI_stop_signal")
capsulwd = os.path.join(soutdir, "capsul")
if args.erase and os.path.isdir(soutdir):
shutil.rmtree(soutdir)
if not os.path.isdir(capsulwd):
os.makedirs(capsulwd)
# Create the study configuration
print "Study_config init..."
study_config = StudyConfig(
modules=["MatlabConfig", "SPMConfig", "FSLConfig", "NipypeConfig"],
use_smart_caching=False,
use_fsl=True,
fsl_config=args.fslconfig,
use_matlab=True,
matlab_exec=args.matlabexec,
use_spm=True,
spm_directory=args.spmdir,
use_nipype=True,
output_directory=capsulwd)
print " ... done."
# Get the pipeline
pipeline = get_process_instance(
"clinfmri.statistics.spm_first_level_pipeline.xml")
# unzip nifti file (to be destroyed after)
fmri_session_unizp = os.path.join(
capsulwd,
os.path.basename(args.inputvolume).replace(".gz", ""))
def __init__(self, pipeline_menu, ui_file, default_study_config=None):
""" Method to initialize the Capsul main window class.
Parameters
----------
pipeline_menu: hierachic dict
each key is a sub module of the module. Leafs contain a list with
the url to the documentation.
ui_file: str (mandatory)
a filename containing the user interface description
default_study_config: ordered dict (madatory)
some parameters for the study configuration
"""
# Inheritance: load user interface window
MyQUiLoader.__init__(self, ui_file)
# Class parameters
self.pipeline_menu = pipeline_menu
self.pipelines = {}
self.pipeline = None
self.path_to_pipeline_doc = {}
# Define dynamic controls
self.controls = {
QtGui.QAction: ["actionHelp", "actionQuit", "actionBrowse",
"actionLoad", "actionChangeView",
"actionParameters", "actionRun",
"actionStudyConfig", "actionQualityControl"],
QtGui.QTabWidget: ["display", ],
QtGui.QDockWidget: ["dockWidgetBrowse", "dockWidgetParameters",
"dockWidgetStudyConfig", "dockWidgetBoard"],
QtGui.QWidget: ["dock_browse", "dock_parameters",
"dock_study_config", "dock_board"],
QtGui.QTreeWidget: ["menu_treectrl", ],
QtGui.QLineEdit: ["search", ],
}
# Add ui class parameter with the dynamic controls and initialize
# default values
self.add_controls_to_ui()
self.ui.display.setTabsClosable(True)
# Create the study configuration
self.study_config = StudyConfig(default_study_config)
# Create the controller widget associated to the study
# configuration controller
self.study_config_widget = ScrollControllerWidget(
self.study_config, live=True)
self.ui.dockWidgetStudyConfig.setWidget(self.study_config_widget)
# Create the pipeline menu
fill_treectrl(self.ui.menu_treectrl, self.pipeline_menu)
# Signal for window interface
self.ui.actionHelp.triggered.connect(self.onHelpClicked)
self.ui.actionChangeView.triggered.connect(self.onChangeViewClicked)
# Signal for tab widget
self.ui.display.currentChanged.connect(self.onCurrentTabChanged)
self.ui.display.tabCloseRequested.connect(self.onCloseTabClicked)
# Signal for dock widget
self.ui.actionBrowse.triggered.connect(self.onBrowseClicked)
self.ui.actionParameters.triggered.connect(self.onParametersClicked)
self.ui.actionStudyConfig.triggered.connect(self.onStudyConfigClicked)
self.ui.actionQualityControl.triggered.connect(self.onQualityControlClicked)
# Initialize properly the visibility of each dock widget
self.onBrowseClicked()
self.onParametersClicked()
self.onStudyConfigClicked()
self.onQualityControlClicked()
# Signal for the pipeline creation
self.ui.search.textChanged.connect(self.onSearchClicked)
self.ui.menu_treectrl.currentItemChanged.connect(
self.onTreeSelectionChanged)
self.ui.actionLoad.triggered.connect(self.onLoadClicked)
# Signal for the execution
self.ui.actionRun.triggered.connect(self.onRunClicked)
class CapsulMainWindow(MyQUiLoader):
""" Capsul main window.
"""
def __init__(self, pipeline_menu, ui_file, default_study_config=None):
""" Method to initialize the Capsul main window class.
Parameters
----------
pipeline_menu: hierachic dict
each key is a sub module of the module. Leafs contain a list with
the url to the documentation.
ui_file: str (mandatory)
a filename containing the user interface description
default_study_config: ordered dict (madatory)
some parameters for the study configuration
"""
# Inheritance: load user interface window
MyQUiLoader.__init__(self, ui_file)
# Class parameters
self.pipeline_menu = pipeline_menu
self.pipelines = {}
self.pipeline = None
self.path_to_pipeline_doc = {}
# Define dynamic controls
self.controls = {
QtGui.QAction: ["actionHelp", "actionQuit", "actionBrowse",
"actionLoad", "actionChangeView",
"actionParameters", "actionRun",
"actionStudyConfig", "actionQualityControl"],
QtGui.QTabWidget: ["display", ],
QtGui.QDockWidget: ["dockWidgetBrowse", "dockWidgetParameters",
"dockWidgetStudyConfig", "dockWidgetBoard"],
QtGui.QWidget: ["dock_browse", "dock_parameters",
"dock_study_config", "dock_board"],
QtGui.QTreeWidget: ["menu_treectrl", ],
QtGui.QLineEdit: ["search", ],
}
# Add ui class parameter with the dynamic controls and initialize
# default values
self.add_controls_to_ui()
self.ui.display.setTabsClosable(True)
# Create the study configuration
self.study_config = StudyConfig(default_study_config)
# Create the controller widget associated to the study
# configuration controller
self.study_config_widget = ScrollControllerWidget(
self.study_config, live=True)
self.ui.dockWidgetStudyConfig.setWidget(self.study_config_widget)
# Create the pipeline menu
fill_treectrl(self.ui.menu_treectrl, self.pipeline_menu)
# Signal for window interface
self.ui.actionHelp.triggered.connect(self.onHelpClicked)
self.ui.actionChangeView.triggered.connect(self.onChangeViewClicked)
# Signal for tab widget
self.ui.display.currentChanged.connect(self.onCurrentTabChanged)
self.ui.display.tabCloseRequested.connect(self.onCloseTabClicked)
# Signal for dock widget
self.ui.actionBrowse.triggered.connect(self.onBrowseClicked)
self.ui.actionParameters.triggered.connect(self.onParametersClicked)
self.ui.actionStudyConfig.triggered.connect(self.onStudyConfigClicked)
self.ui.actionQualityControl.triggered.connect(self.onQualityControlClicked)
# Initialize properly the visibility of each dock widget
self.onBrowseClicked()
self.onParametersClicked()
self.onStudyConfigClicked()
self.onQualityControlClicked()
# Signal for the pipeline creation
self.ui.search.textChanged.connect(self.onSearchClicked)
self.ui.menu_treectrl.currentItemChanged.connect(
self.onTreeSelectionChanged)
self.ui.actionLoad.triggered.connect(self.onLoadClicked)
# Signal for the execution
self.ui.actionRun.triggered.connect(self.onRunClicked)
# Set default values
# Set some tooltips
def show(self):
""" Shows the widget and its child widgets.
"""
self.ui.show()
def add_controls_to_ui(self):
""" Method to find dynamic controls
"""
# Error message template
error_message = "{0} has no attribute '{1}'"
# Got through the class dynamic controls
for control_type, control_item in self.controls.iteritems():
# Get the dynamic control name
for control_name in control_item:
# Try to set the control value to the ui class parameter
try:
value = self.ui.findChild(control_type, control_name)
if value is None:
logger.error(error_message.format(
type(self.ui), control_name))
setattr(self.ui, control_name, value)
except:
logger.error(error_message.format(
type(self.ui), control_name))
###########################################################################
# Slots
###########################################################################
def onRunClicked(self):
""" Event to execute the process/pipeline.
"""
self.study_config.run(self.pipeline, executer_qc_nodes=True, verbose=1)
def onBrowseClicked(self):
""" Event to show / hide the browse dock widget.
"""
# Show browse dock widget
if self.ui.actionBrowse.isChecked():
self.ui.dockWidgetBrowse.show()
# Hide browse dock widget
else:
self.ui.dockWidgetBrowse.hide()
def onParametersClicked(self):
""" Event to show / hide the parameters dock widget.
"""
# Show parameters dock widget
if self.ui.actionParameters.isChecked():
self.ui.dockWidgetParameters.show()
# Hide parameters dock widget
else:
self.ui.dockWidgetParameters.hide()
def onStudyConfigClicked(self):
""" Event to show / hide the study config dock widget.
"""
# Show study configuration dock widget
if self.ui.actionStudyConfig.isChecked():
self.ui.dockWidgetStudyConfig.show()
# Hide study configuration dock widget
else:
self.ui.dockWidgetStudyConfig.hide()
def onQualityControlClicked(self):
""" Event to show / hide the board dock widget.
"""
# Create and show board dock widget
if self.ui.actionQualityControl.isChecked():
# Create the board widget associated to the pipeline controller
# Create on the fly in order to get the last status
# ToDo: add callbacks
if self.pipeline is not None:
# board_widget = BoardWidget(
# self.pipeline, parent=self.ui.dockWidgetParameters,
# name="board")
board_widget = ScrollControllerWidget(
self.pipeline, name="outputs", live=True,
hide_labels=False, select_controls="outputs",
disable_controller_widget=True)
#board_widget.setEnabled(False)
self.ui.dockWidgetBoard.setWidget(board_widget)
# Show the board widget
self.ui.dockWidgetBoard.show()
# Hide board dock widget
else:
self.ui.dockWidgetBoard.hide()
def onSearchClicked(self):
""" Event to refresh the menu tree control that contains the pipeline
modules.
"""
# Clear the current tree control
self.ui.menu_treectrl.clear()
# Build the new filtered tree control
fill_treectrl(self.ui.menu_treectrl, self.pipeline_menu,
self.ui.search.text().lower())
def onTreeSelectionChanged(self):
""" Event to refresh the pipeline load button status.
"""
# Get the cuurent item
item = self.ui.menu_treectrl.currentItem()
if item is None:
return
# Check if we have selected a pipeline in the tree and enable / disable
# the load button
url = item.text(2)
if url == "None":
self.ui.actionLoad.setEnabled(False)
else:
self.ui.actionLoad.setEnabled(True)
def onRunStatus(self):
""" Event to refresh the run button status.
When all the controller widget controls are correctly filled, enable
the user to execute the pipeline.
"""
# Get the controller widget
controller_widget = self.ui.dockWidgetParameters.widget().controller_widget
# Get the controller widget status
is_valid = controller_widget.is_valid()
# Depending on the controller widget status enable / disable
# the run button
self.ui.actionRun.setEnabled(is_valid)
def onLoadClicked(self):
""" Event to load and display a pipeline.
"""
# Get the pipeline instance from its string description
item = self.ui.menu_treectrl.currentItem()
description_list = [str(x) for x in [item.text(1), item.text(0)]
if x != ""]
process_description = ".".join(description_list)
self.pipeline = get_process_instance(process_description)
# Create the controller widget associated to the pipeline
# controller
pipeline_widget = ScrollControllerWidget(
self.pipeline, live=True, select_controls="inputs")
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Add observer to refresh the run button
controller_widget = pipeline_widget.controller_widget
for control_name, control in controller_widget._controls.iteritems():
# Unpack the control item
trait, control_class, control_instance, control_label = control
# Add the new callback
control_class.add_callback(self.onRunStatus, control_instance)
# Refresh manually the run button status the first time
self.onRunStatus()
# Store the pipeline documentation root path
self.path_to_pipeline_doc[self.pipeline.id] = item.text(2)
# Store the pipeline instance
self.pipelines[self.pipeline.name] = (
self.pipeline, pipeline_widget)
# Create the widget
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# Connect the subpipeline clicked signal to the
# onLoadSubPipelineClicked slot
widget.subpipeline_clicked.connect(self.onLoadSubPipelineClicked)
def onLoadSubPipelineClicked(self, name, sub_pipeline, modifiers):
""" Event to load and display a sub pipeline.
"""
# Store the pipeline instance in class parameters
self.pipeline = self.pipeline.nodes[name].process
# Create the controller widget associated to the sub pipeline
# controller: if the sub pipeline is an IterativePipeline, disable
# the correspondind controller widget since this pipeline is generated
# on the fly an is not directly synchronized with the rest of the
# pipeline.
is_iterative_pipeline = False
if isinstance(self.pipeline, IterativePipeline):
is_iterative_pipeline = True
pipeline_widget = ScrollControllerWidget(
self.pipeline, live=True, select_controls="inputs",
disable_controller_widget=is_iterative_pipeline)
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Store the sub pipeline instance
self.pipelines[self.pipeline.name] = (
self.pipeline, pipeline_widget)
# Create the widget
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# Connect the subpipeline clicked signal to the
# onLoadSubPipelineClicked slot
widget.subpipeline_clicked.connect(self.onLoadSubPipelineClicked)
def onCloseTabClicked(self, index):
""" Event to close a pipeline view.
"""
# Remove the pipeline from the intern pipeline list
pipeline, pipeline_widget = self.pipelines[
self.ui.display.tabText(index)]
pipeline_widget.close()
pipeline_widget.deleteLater()
del self.pipelines[self.ui.display.tabText(index)]
# Remove the table that contains the pipeline
self.ui.display.removeTab(index)
def onCurrentTabChanged(self, index):
""" Event to refresh the controller controller widget when a new
tab is selected
"""
# If no valid tab index has been passed
if index < 0:
self.ui.actionRun.setEnabled(False)
# A new valid tab is selected
else:
# Get the selected pipeline widget
self.pipeline, pipeline_widget = self.pipelines[
self.ui.display.tabText(index)]
# Set the controller widget associated to the pipeline
# controller
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Refresh manually the run button status the first time
self.onRunStatus()
def onHelpClicked(self):
""" Event to display the documentation of the active pipeline.
"""
# Create a dialog box to display the html documentation
win = QtGui.QDialog()
win.setWindowTitle("Pipeline Help")
# Build the pipeline documentation location
# Possible since common tools generate the sphinx documentation
if self.pipeline:
# Generate the url to the active pipeline documentation
path_to_active_pipeline_doc = os.path.join(
self.path_to_pipeline_doc[self.pipeline.id], "generated",
self.pipeline.id.split(".")[1], "pipeline",
self.pipeline.id + ".html")
# Create and fill a QWebView
help = QtWebKit.QWebView()
help.load(QtCore.QUrl(path_to_active_pipeline_doc))
help.show()
# Create and set a layout with the web view
layout = QtGui.QHBoxLayout()
layout.addWidget(help)
win.setLayout(layout)
# Display the window
win.exec_()
# No Pipeline loaded, cant't show the documentation message
# Display a message box
else:
QtGui.QMessageBox.information(
self.ui, "Information", "First load a pipeline!")
def onChangeViewClicked(self):
""" Event to switch between simple and full pipeline views.
"""
# Check if a pipeline has been loaded
if self._is_active_pipeline_valid():
# Check the current display mode
# Case PipelineDevelopperView
if isinstance(self.ui.display.currentWidget(),
PipelineDevelopperView):
# Switch to PipelineUserView display mode
widget = PipelineUserView(self.pipeline)
self._insert_widget_in_tab(widget)
# Case PipelineUserView
else:
# Switch to PipelineDevelopperView display mode
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# No pipeline loaded error
else:
logger.error("No active pipeline selected. "
"Have you forgotten to click the load pipeline "
"button?")
#####################
# Private interface #
#####################
def _insert_widget_in_tab(self, widget):
""" Insert a new widget or replace an existing widget.
Parameters
----------
widget: a widget (mandatory)
the widget we want to draw
"""
# Search if the tab corresponding to the widget has already been created
already_created = False
index = 0
# Go through all the tabs
for index in range(self.ui.display.count()):
# Check if we have a match: the tab name is equal to the current
#pipeline name
if (self.ui.display.tabText(index) == self.pipeline.name):
already_created = True
break
# If no match found, add a new tab with the widget
if not already_created:
self.ui.display.addTab(
widget, unicode(self.pipeline.name))
self.ui.display.setCurrentIndex(
self.ui.display.count() - 1)
# Otherwise, replace the widget from the match tab
else:
# Delete the tab
self.ui.display.removeTab(index)
# Insert the new tab
self.ui.display.insertTab(
index, widget, unicode(self.pipeline.name))
# Set the corresponding index
self.ui.display.setCurrentIndex(index)
def _is_active_pipeline_valid(self):
""" Method to ceack that the active pipeline is valid
Returns
-------
is_valid: bool
True if the active pipeline is valid
"""
return self.pipeline is not None
def setUp(self):
""" Initialize the TestQCNodes class
"""
self.pipeline = MyPipeline()
self.output_directory = tempfile.mkdtemp()
self.study_config = StudyConfig(output_directory=self.output_directory)
def pilot_bet(enable_display=False):
"""
Brain extractio Tool
====================
"""
import os
from mmutils.toy_datasets import get_sample_data
from capsul.study_config import StudyConfig
from capsul.api import get_process_instance
working_dir = "/volatile/nsap/catalogue/pclinfmri/fmri_bet"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
Then define the study configuration:
"""
study_config = StudyConfig(
modules=["MatlabConfig", "SPMConfig", "FSLConfig", "NipypeConfig"],
use_smart_caching=False,
fsl_config="/etc/fsl/4.1/fsl.sh",
use_fsl=True,
matlab_exec="/neurospin/local/bin/matlab",
use_matlab=False,
spm_directory="/i2bm/local/spm8",
use_spm=False,
output_directory=working_dir)
"""
Load the toy dataset
--------------------
To do so, we use the get_sample_data function to download the toy
dataset on the local file system (here localizer data):
"""
template_dataset = get_sample_data("mni_1mm")
"""
Processing definition
---------------------
"""
pipeline = get_process_instance("clinfmri.utils.converted_fsl_bet")
print pipeline.get_input_spec()
"""
Now we need now to parametrize this pipeline:
"""
pipeline.input_image_file = template_dataset.brain
pipeline.generate_binary_mask = True
"""
It is possible to display the pipeline.
"""
if enable_display:
import sys
from PySide import QtGui
from capsul.qt_gui.widgets import PipelineDevelopperView
app = QtGui.QApplication(sys.argv)
view = PipelineDevelopperView(pipeline)
view.show()
app.exec_()
"""
The pipeline is now ready to be run:
"""
study_config.run(pipeline, executer_qc_nodes=False, verbose=1)
"""
Results
-------
Finally, we print the pipeline outputs:
"""
print("\nOUTPUTS\n")
for trait_name, trait_value in pipeline.get_outputs().items():
print("{0}: {1}".format(trait_name, trait_value))
def pilot_new_segment(enable_display=False):
"""
New Segment
===========
Unifed SPM segmentation: segments, bias corrects and spatially normalises.
Start to import required modules:
"""
import os
from mmutils.toy_datasets import get_sample_data
from capsul.study_config import StudyConfig
from capsul.process import get_process_instance
"""
Study configuration
-------------------
We first define the working directory and guarantee this folder exists on
the file system:
"""
working_dir = "/volatile/nsap/catalogue/pclinfmri/spm_newsegment"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
And then define the study configuration:
"""
study_config = StudyConfig(
modules=["MatlabConfig", "SPMConfig", "FSLConfig", "NipypeConfig"],
use_smart_caching=False,
matlab_exec="/neurospin/local/bin/matlab",
use_matlab=True,
spm_directory="/i2bm/local/spm8",
use_spm=True,
output_directory=working_dir,
number_of_cpus=1,
generate_logging=True,
use_scheduler=True)
"""
Load the toy dataset
--------------------
To do so, we use the get_sample_data function to download the toy
dataset on the local file system (here localizer data):
"""
toy_dataset = get_sample_data("localizer")
template_dataset = get_sample_data("mni_1mm")
"""
The toy_dataset is an Enum structure with some specific elements of
interest:
* fmri: the functional volume.
* anat: the structural volume.
* TR: the repetition time.
Processing definition
---------------------
First create the
:ref:`slice timing pipeline <clinfmri.utils.SpmNewSegment>`
that define the different step of the processings:
"""
pipeline = get_process_instance("clinfmri.utils.spm_new_segment.xml")
print pipeline.get_input_spec()
"""
It is possible to display the pipeline.
"""
if enable_display:
import sys
from PySide import QtGui
from capsul.qt_gui.widgets import PipelineDevelopperView
app = QtGui.QApplication(sys.argv)
view = PipelineDevelopperView(pipeline)
view.show()
app.exec_()
"""
Now we need now to parametrize this pipeline:
"""
pipeline.channel_files = [toy_dataset.mean]
pipeline.reference_volume = template_dataset.brain
"""
The pipeline is now ready to be run:
"""
study_config.run(pipeline, executer_qc_nodes=False, verbose=1)
"""
Results
-------
Finally, we print the pipeline outputs:
"""
print("\nOUTPUTS\n")
for trait_name, trait_value in pipeline.get_outputs().items():
print("{0}: {1}".format(trait_name, trait_value))
os.mkdir(logdir)
elif args.erase:
shutil.rmtree(logdir)
os.mkdir(logdir)
"""
First create a study configuration.
"""
study_config = StudyConfig(
modules=["MatlabConfig", "SPMConfig", "FSLConfig", "NipypeConfig"],
use_smart_caching=False,
fsl_config=args.fslconfig,
use_fsl=True,
use_matlab=False,
use_spm=False,
spm_exec=args.spmbin,
spm_standalone=True,
use_nipype=True,
output_directory=tmp_capsul,
number_of_cpus=1,
generate_logging=True,
use_scheduler=True,
)
"""
Processing definition: create the <clinfmri.preproc.FmriPreproc> that
define the different step of the processings.
"""
pipeline = get_process_instance("clinfmri.preproc.fmri_preproc.xml")
"""
# Create the subject output directory
soutdir = os.path.join(args.outdir, args.sid)
capsulwd = os.path.join(soutdir, "capsul")
if args.erase and os.path.isdir(soutdir):
shutil.rmtree(soutdir)
if not os.path.isdir(capsulwd):
os.makedirs(capsulwd)
# Create the study configuration
print "Study_config init..."
study_config = StudyConfig(
modules=["MatlabConfig", "SPMConfig", "NipypeConfig"],
use_smart_caching=False,
use_matlab=False,
use_spm=True,
spm_exec=args.spmbin,
spm_standalone=True,
use_nipype=True,
output_directory=capsulwd)
print " ... done."
# Get the pipeline
pipeline = get_process_instance("clinfmri.utils.spm_new_segment_only.xml")
# Configure the pipeline
pipeline.channel_files = [args.t1file]
#to find the template TPM.nii from the standalone distrib
pipeline.spm_dir = args.spmdir
# Execute the pipeline
def pilot_preproc():
"""
FMRI preprocessings
===================
"""
# Pilot imports
import os
from caps.toy_datasets import get_sample_data
from capsul.study_config import StudyConfig
from capsul.process import get_process_instance
"""
Study configuration
-------------------
We first define the working directory and guarantee this folder exists on
the file system:
"""
working_dir = "/volatile/nsap/catalogue/pclinfmri/fmri_preproc"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
Now we get the pipeline from its definition (xml file)
"""
pipeline = get_process_instance(
"clinfmri.preproc.pipeline.fmri_preproc.xml")
"""
And then define the study configuration (here we activate the smart
caching module that will be able to remember which process has already been
processed):
"""
study_config = StudyConfig(
modules=["SmartCachingConfig", "MatlabConfig", "SPMConfig",
"FSLConfig",
"NipypeConfig"],
use_smart_caching=True,
fsl_config="/etc/fsl/4.1/fsl.sh",
use_fsl=True,
matlab_exec="/neurospin/local/bin/matlab",
use_matlab=True,
spm_directory="/i2bm/local/spm8",
use_spm=True,
output_directory=working_dir)
"""
Load the toy dataset
--------------------
To do so, we use the get_sample_data function to download the toy
dataset on the local file system (here localizer data):
"""
toy_dataset = get_sample_data("localizer")
template_dataset = get_sample_data("mni_1mm")
"""
The toy_dataset is an Enum structure with some specific elements of
interest:
* **??**: ??.
Processing definition
---------------------
First create the
:ref:`slice timing pipeline <pclinfmri.preproc.pipeline.SliceTiming>` that
define the different step of the processings:
"""
pipeline = get_process_instance("pclinfmri.preproc.fmri_preproc.xml")
print pipeline.get_input_spec()
"""
Now we need now to parametrize this pipeline:
"""
pipeline.fmri_file = toy_dataset.fmri
pipeline.structural_file = toy_dataset.anat
pipeline.realign_register_to_mean = True
pipeline.select_slicer = "none"
pipeline.select_registration = "template"
pipeline.template_file = template_dataset.brain
pipeline.force_repetition_time = toy_dataset.TR
pipeline.force_slice_orders = [index + 1 for index in range(40)]
"""
The pipeline is now ready to be run:
"""
study_config.run(pipeline, executer_qc_nodes=True, verbose=1)
"""
Results
-------
Finally, we print the pipeline outputs:
"""
print("\nOUTPUTS\n")
for trait_name, trait_value in pipeline.get_outputs().items():
print("{0}: {1}".format(trait_name, trait_value))
def pilot(working_dir="/volatile/nsap/caps", **kwargs):
"""
===============================
Diffusion Brain Extraction Tool
===============================
.. topic:: Objective
We propose to extract the brain mask from a diffusion sequence.
Import
------
First we load the function that enables us to access the toy datasets
"""
from caps.toy_datasets import get_sample_data
"""
From capsul we then load the class to configure the study we want to
perform
"""
from capsul.study_config import StudyConfig
"""
Here two utility tools are loaded. The first one enables the creation
of ordered dictionary and the second ensure that a directory exist.
Note that the directory will be created if necessary.
"""
from capsul.utils.sorted_dictionary import SortedDictionary
from nsap.lib.base import ensure_is_dir
"""
Load the toy dataset
--------------------
We want to perform BET on a diffusion sequence.
To do so, we use the *get_sample_data* function to load this
dataset.
.. seealso::
For a complete description of the *get_sample_data* function, see the
:ref:`Toy Datasets documentation <toy_datasets_guide>`
"""
toy_dataset = get_sample_data("dwi")
"""
The *toy_dataset* is an Enum structure with some specific
elements of interest *dwi*, *bvals* that contain the nifti diffusion
image and the b-values respectively.
"""
print(toy_dataset.dwi, toy_dataset.bvals)
"""
Will return:
.. code-block:: python
/home/ag239446/git/nsap-src/nsap/data/DTI30s010.nii
/home/ag239446/git/nsap-src/nsap/data/DTI30s010.bval
We can see that the image has been found in a local directory
Processing definition
---------------------
Now we need to define the processing step that will perform BET on
diffusion sequence.
"""
bet_pipeline = dBET()
"""
It is possible to access the ipeline input specification.
"""
print(bet_pipeline.get_input_spec())
"""
Will return the input parameters the user can set:
.. code-block:: python
INPUT SPECIFICATIONS
dw_image: ['File']
bvals: ['File']
specified_index_of_ref_image: ['Int']
terminal_output: ['Enum']
generate_binary_mask: ['Bool']
use_4d_input: ['Bool']
generate_mesh: ['Bool']
generate_skull: ['Bool']
bet_threshold: ['Float']
We can now tune the pipeline parameters.
We first set the input dwi file:
"""
bet_pipeline.dw_image = toy_dataset.dwi
"""
And set the b-values file
"""
bet_pipeline.bvals = toy_dataset.bvals
"""
Study Configuration
-------------------
The pipeline is now set up and ready to be executed.
For a complete description of a study execution, see the
:ref:`Study Configuration description <study_configuration_guide>`
"""
bet_working_dir = os.path.join(working_dir, "diffusion_bet")
ensure_is_dir(bet_working_dir)
default_config = SortedDictionary(
("output_directory", bet_working_dir),
("fsl_config", "/etc/fsl/4.1/fsl.sh"), ("use_fsl", True),
("use_smart_caching", True), ("generate_logging", True))
study = StudyConfig(default_config)
study.run(bet_pipeline)
"""
Results
-------
Finally, we print the pipeline outputs
"""
print("\nOUTPUTS\n")
for trait_name, trait_value in bet_pipeline.get_outputs().iteritems():
print("{0}: {1}".format(trait_name, trait_value))
"""
def pilot_preproc_spm_fmri(enable_display=False):
"""
FMRI preprocessings
===================
Preprocessing with the SPM slice timing and a normalization to a given
template.
Start to import required modules:
"""
import os
from mmutils.toy_datasets import get_sample_data
from capsul.study_config import StudyConfig
from capsul.api import get_process_instance
"""
Study configuration
-------------------
We first define the working directory and guarantee this folder exists on
the file system:
"""
working_dir = "/volatile/nsap/catalogue/pclinfmri/fmri_preproc_spm_fmri"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
Then define the study configuration:
"""
study_config = StudyConfig(
modules=["MatlabConfig", "SPMConfig", "FSLConfig", "NipypeConfig"],
use_smart_caching=False,
fsl_config="/etc/fsl/4.1/fsl.sh",
use_fsl=True,
matlab_exec="/neurospin/local/bin/matlab",
use_matlab=True,
spm_directory="/i2bm/local/spm8",
use_spm=True,
output_directory=working_dir,
number_of_cpus=1,
generate_logging=True,
use_scheduler=True,)
"""
Load the toy dataset
--------------------
To do so, we use the get_sample_data function to download the toy
dataset on the local file system (here localizer data):
"""
toy_dataset = get_sample_data("localizer")
template_dataset = get_sample_data("mni_1mm")
"""
The toy_dataset is an Enum structure with some specific elements of
interest:
* fmri: the functional volume.
* anat: the structural volume.
* TR: the repetition time.
Processing definition
---------------------
First create the
:ref:`slice timing pipeline <clinfmri.preproc.FmriPreproc>` that
define the different step of the processings:
"""
pipeline = get_process_instance("clinfmri.preproc.converted_fmri_preproc")
print pipeline.get_input_spec()
"""
Now we need now to parametrize this pipeline:
"""
pipeline.fmri_file = toy_dataset.fmri
pipeline.structural_file = toy_dataset.anat
pipeline.realign_register_to_mean = True
pipeline.select_slicer = "spm"
pipeline.select_normalization = "fmri"
pipeline.template_file = template_dataset.brain
pipeline.force_repetition_time = toy_dataset.TR
pipeline.force_slice_orders = [index + 1 for index in range(40)]
"""
It is possible to display the pipeline.
"""
if enable_display:
import sys
from PySide import QtGui
from capsul.qt_gui.widgets import PipelineDevelopperView
app = QtGui.QApplication(sys.argv)
view = PipelineDevelopperView(pipeline)
view.show()
app.exec_()
"""
The pipeline is now ready to be run:
"""
study_config.run(pipeline, executer_qc_nodes=False, verbose=1)
"""
Results
-------
Finally, we print the pipeline outputs:
"""
print("\nOUTPUTS\n")
for trait_name, trait_value in pipeline.get_outputs().items():
print("{0}: {1}".format(trait_name, trait_value))
class TestSomaWorkflow(unittest.TestCase):
def setUp(self):
default_config = SortedDictionary(
("use_soma_workflow", True)
)
# use a custom temporary soma-workflow dir to avoid concurrent
# access problems
tmpdb = tempfile.mkstemp('', prefix='soma_workflow')
os.close(tmpdb[0])
os.unlink(tmpdb[1])
self.soma_workflow_temp_dir = tmpdb[1]
os.mkdir(self.soma_workflow_temp_dir)
swf_conf = StringIO.StringIO('[%s]\nSOMA_WORKFLOW_DIR = %s\n' \
% (socket.gethostname(), tmpdb[1]))
swconfig.Configuration.search_config_path \
= staticmethod(lambda : swf_conf)
self.study_config = StudyConfig(init_config=default_config)
self.atomic_pipeline = MyAtomicPipeline()
self.composite_pipeline = MyCompositePipeline()
def tearDown(self):
shutil.rmtree(self.soma_workflow_temp_dir)
def test_atomic_dependencies(self):
workflow = workflow_from_pipeline(self.atomic_pipeline)
dependencies = [(x.name, y.name) for x, y in workflow.dependencies]
self.assertTrue(len(dependencies) == 4)
self.assertTrue(("node1", "node2") in dependencies)
self.assertTrue(("node1", "node3") in dependencies)
self.assertTrue(("node2", "node4") in dependencies)
self.assertTrue(("node3", "node4") in dependencies)
self.assertEqual(workflow.groups, [])
def test_atomic_execution(self):
self.atomic_pipeline.workflow_ordered_nodes()
if sys.version_info >= (2, 7):
self.assertIn(self.atomic_pipeline.workflow_repr,
('node1->node3->node2->node4',
'node1->node2->node3->node4'))
else: # python 2.6 unittest does not have assertIn()
self.assertTrue(self.atomic_pipeline.workflow_repr in \
('node1->node3->node2->node4',
'node1->node2->node3->node4'))
self.study_config.run(self.atomic_pipeline)
def test_composite_dependencies(self):
workflow = workflow_from_pipeline(self.composite_pipeline)
dependencies = [(x.name, y.name) for x, y in workflow.dependencies]
self.assertTrue(len(dependencies) == 16)
self.assertEqual(dependencies.count(("node1", "node2")), 1)
self.assertEqual(dependencies.count(("node1", "node3")), 2)
self.assertEqual(dependencies.count(("node2", "node4")), 1)
self.assertEqual(dependencies.count(("node3", "node4")), 2)
self.assertEqual(dependencies.count(("node1", "node2_input")), 1)
self.assertEqual(dependencies.count(("node2_output", "node4")), 1)
self.assertTrue(len(workflow.groups) == 1)
def test_composite_execution(self):
self.composite_pipeline.workflow_ordered_nodes()
self.assertEqual(self.composite_pipeline.workflow_repr,
"node1->node3->node2->node4")
self.study_config.run(self.composite_pipeline)
def pilot_bet(enable_display=False):
"""
BET
===
Brain extraction with FSL.
Start to import required modules:
"""
import os
from mmutils.toy_datasets import get_sample_data
from capsul.study_config import StudyConfig
from capsul.process import get_process_instance
"""
Study configuration
-------------------
We first define the working directory and guarantee this folder exists on
the file system:
"""
working_dir = "/volatile/nsap/catalogue/pclinfmri/fsl_bet"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
And then define the study configuration:
"""
study_config = StudyConfig(
modules=["MatlabConfig", "SPMConfig", "FSLConfig", "NipypeConfig"],
use_smart_caching=False,
fsl_config="/etc/fsl/4.1/fsl.sh",
use_fsl=True,
output_directory=working_dir,
number_of_cpus=1,
generate_logging=True,
use_scheduler=True)
"""
Load the toy dataset
--------------------
To do so, we use the get_sample_data function to download the toy
dataset on the local file system (here localizer data):
"""
toy_dataset = get_sample_data("localizer")
"""
The toy_dataset is an Enum structure with some specific elements of
interest:
* fmri: the functional volume.
* anat: the structural volume.
* TR: the repetition time.
Processing definition
---------------------
First create the
:ref:`slice timing pipeline <clinfmri.preproc.FslBet>` that
define the different step of the processings:
"""
pipeline = get_process_instance("clinfmri.utils.fsl_bet.xml")
print pipeline.get_input_spec()
"""
It is possible to display the pipeline.
"""
if enable_display:
import sys
from PySide import QtGui
from capsul.qt_gui.widgets import PipelineDevelopperView
app = QtGui.QApplication(sys.argv)
view = PipelineDevelopperView(pipeline)
view.show()
app.exec_()
"""
Now we need now to parametrize this pipeline:
"""
pipeline.input_image_file = toy_dataset.anat
pipeline.generate_binary_mask = True
pipeline.bet_threshold = 0.5
"""
The pipeline is now ready to be run:
"""
study_config.run(pipeline, executer_qc_nodes=False, verbose=1)
"""
Results
-------
Finally, we print the pipeline outputs:
"""
print("\nOUTPUTS\n")
for trait_name, trait_value in pipeline.get_outputs().items():
print("{0}: {1}".format(trait_name, trait_value))
def pilot_newsegment():
"""
New Segment
===========
"""
# Pilot imports
import os
from caps.toy_datasets import get_sample_data
from capsul.study_config import StudyConfig
from pclinfmri.utils.pipeline import SpmNewSegment
"""
Study configuration
-------------------
We first define the working directory and guarantee this folder exists on
the file system:
"""
working_dir = "/volatile/nsap/pclinfmri/spmnewsegment"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
And then define the study configuration (here we activate the smart
caching module that will be able to remember which process has already been
processed):
"""
study_config = StudyConfig(
modules=["SmartCachingConfig", "MatlabConfig", "SPMConfig",
"NipypeConfig"],
use_smart_caching=True,
matlab_exec="/neurospin/local/bin/matlab",
use_matlab=True,
spm_directory="/i2bm/local/spm8",
use_spm=True,
output_directory=working_dir)
"""
Load the toy dataset
--------------------
To do so, we use the get_sample_data function to download the toy
dataset on the local file system (here localizer data):
"""
toy_dataset = get_sample_data("localizer")
"""
The toy_dataset is an Enum structure with some specific elements of
interest:
* **??**: ??.
Processing definition
---------------------
First create the
:ref:`slice timing pipeline <pclinfmri.preproc.pipeline.SliceTiming>` that
define the different step of the processings:
"""
pipeline = SpmNewSegment()
print pipeline.get_input_spec()
"""
Now we need now to parametrize this pipeline:
"""
pipeline.coregistered_struct_file = toy_dataset.mean
"""
The pipeline is now ready to be run:
"""
study_config.run(pipeline, executer_qc_nodes=True, verbose=1)
"""
Results
-------
Finally, we print the pipeline outputs:
"""
print("\nOUTPUTS\n")
for trait_name, trait_value in pipeline.get_outputs().items():
print("{0}: {1}".format(trait_name, trait_value))
