def spm_tissue_probability_maps():
""" SPM tissue probability maps.
<unit>
<output name="tpm_struct" type="List" content="Any" desc="a struct
containing the spm tissue probability map descriptions."/>
</unit>
"""
# Try to import the resource
try:
from caps.toy_datasets import get_sample_data
except:
raise ImportError("Can't import 'caps'.")
tmp_file = get_sample_data("tpm").all
# Format the tpm for spm
tissue1 = ((tmp_file, 1), 2, (True, True), (False, True))
tissue2 = ((tmp_file, 2), 2, (True, True), (False, True))
tissue3 = ((tmp_file, 3), 2, (True, False), (False, False))
tissue4 = ((tmp_file, 4), 3, (False, False), (False, False))
tissue5 = ((tmp_file, 5), 4, (False, False), (False, False))
tpm_struct = [tissue1, tissue2, tissue3, tissue4, tissue5]
return tpm_struct
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))
"""
output_directory="/volatile/nsap/catalogue/spm_first_level/")
print "Done in {0} seconds".format(datetime.datetime.now() - start_time)
# Create pipeline
start_time = datetime.datetime.now()
print "Start Pipeline Creation", start_time
pipeline = get_process_instance(
"caps.nsap.functional_statistic.pipeline.spm_first_level_pipeline.xml")
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# Set pipeline input parameters
start_time = datetime.datetime.now()
print "Start Parametrization", start_time
localizer_dataset = get_sample_data("localizer")
pipeline.behavioral_data = [localizer_dataset.onsets]
pipeline.fmri_sessions = [localizer_dataset.preproc_fmri]
pipeline.time_repetition = localizer_dataset.TR
pipeline.realignment_parameters = localizer_dataset.mouvment_parameters
pipeline.condition_name = "Conditions"
pipeline.onset_name = "Onsets"
pipeline.duration_name = "Durations"
pipeline.delimiter = ";"
pipeline.start = 0
pipeline.contrasts = [
("Horizontal Checkerboard","T",['damier_H',],[1,]),
("Vertical Checkerboard","T",['damier_V',],[1,]),
("Right Audio Click","T",['clicDaudio',],[1,]),
("Left Audio Click","T",['clicGaudio',],[1,]),
("Right Video Click","T",['clicDvideo',],[1,]),
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))
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################
# System import
import datetime
# CAPS import
from caps.toy_datasets import get_sample_data
# Lib import
from dcmreader import get_sequence_name, get_all_sop_instance_uids
start_time = datetime.datetime.now()
localizer_dataset = get_sample_data("qt1")
#
print "Start Procedure", start_time
print "***************"
print ""
print "Extracting sequence name (first value returned only)..."
seqname = get_sequence_name(localizer_dataset.gre5dcm)
print "sequence name: {0}".format(seqname)
print ""
print(
"Extracting Referenced SOP Instance UID (all values "
"returned in a list)...")
uids = get_all_sop_instance_uids(localizer_dataset.gre20dcm)
print "Referenced SOP Instance UID: {0}".format(uids)
print ""
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_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))
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################
# System import
import datetime
# CAPS import
from caps.toy_datasets import get_sample_data
# Lib import
from dcmreader import get_sequence_name, get_all_sop_instance_uids
start_time = datetime.datetime.now()
localizer_dataset = get_sample_data("qt1")
#
print "Start Procedure", start_time
print "***************"
print ""
print "Extracting sequence name (first value returned only)..."
seqname = get_sequence_name(localizer_dataset.gre5dcm)
print "sequence name: {0}".format(seqname)
print ""
print ("Extracting Referenced SOP Instance UID (all values "
"returned in a list)...")
uids = get_all_sop_instance_uids(localizer_dataset.gre20dcm)
print "Referenced SOP Instance UID: {0}".format(uids)
print ""
print "End of pilots"
