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[Inria-Empenn#158] Correct preprocessing
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@youennmerel
youennmerel committed Feb 13, 2024
1 parent 24d3438 commit 9bc9701
Showing 1 changed file with 132 additions and 144 deletions.
276 changes: 132 additions & 144 deletions narps_open/pipelines/team_E3B6.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,21 +37,18 @@


class PipelineTeamE3B6(Pipeline):
""" A class that defines the pipeline of team 48CD
""" A class that defines the pipeline of team E3B6
"""

def get_hypotheses_outputs(self):
pass

def __init__(self):
super().__init__()
self._fwhm = 6.0

def get_preprocessing(self):
""" Return a Nipype workflow describing the prerpocessing part of the pipeline """

# [INFO] The following part stays the same for all preprocessing pipelines
"""
Preprocessing order : fMRIprep + smoothing
Smoothing : SPM 12 ; v7487 fixed Gaussian kernel in MNI volume FWHM = 6 mm
"""

# IdentityInterface node - allows to iterate over subjects and runs
info_source = Node(
Expand All @@ -65,17 +62,8 @@ def get_preprocessing(self):

# Templates to select files node
file_templates = {
'anat': join(
'sub-{subject_id}', 'anat', 'sub-{subject_id}_T1w.nii.gz'
),
'func': join(
'sub-{subject_id}', 'func', 'sub-{subject_id}_task-MGT_run-{run_id}_bold.nii.gz'
),
'magnitude': join(
'sub-{subject_id}', 'fmap', 'sub-{subject_id}_magnitude1.nii.gz'
),
'phasediff': join(
'sub-{subject_id}', 'fmap', 'sub-{subject_id}_phasediff.nii.gz'
'derivatives', 'fmriprep', 'sub-{subject_id}', 'func', 'sub-{subject_id}_task-MGT_run-*_bold_space-MNI152NLin2009cAsym_preproc'
)
}

Expand Down Expand Up @@ -103,8 +91,6 @@ def get_preprocessing(self):
name='preprocessing'
)

# [TODO] Add the connections the workflow needs
# [INFO] Input and output names can be found on NiPype documentation
preprocessing.connect(
[
(
Expand All @@ -128,12 +114,13 @@ def get_preprocessing(self):
]
)

# [INFO] Here we simply return the created workflow
return preprocessing

# [INFO] There was no run level analysis for the pipelines using SPM
def get_run_level_analysis(self):
""" Return a Nipype workflow describing the run level analysis part of the pipeline """
"""
No run level analysis has been done by team Q6O0
There was no run level analysis for the pipelines using SPM
"""
return None

# [INFO] This function is used in the subject level analysis pipelines using SPM
Expand Down Expand Up @@ -168,11 +155,12 @@ def get_subject_infos(event_files: list, runs: list):
"""
from nipype.interfaces.base import Bunch

condition_names = ['trial', 'accepting', 'rejecting']
condition_names = ['gamble_trial', 'nointerest_trial']
onset = {}
duration = {}
weights_gain = {}
weights_loss = {}
weights_ev = {}
onset_button = {}
duration_button = {}

Expand Down Expand Up @@ -261,126 +249,126 @@ def get_contrasts(self):
return [trial, effect_gain, effect_loss]

def get_subject_level_analysis(self):
# """ Return a Nipype workflow describing the subject level analysis part of the pipeline """
#
# # [INFO] The following part stays the same for all pipelines
#
# # Infosource Node - To iterate on subjects
# info_source = Node(
# IdentityInterface(
# fields = ['subject_id', 'dataset_dir', 'results_dir', 'working_dir', 'run_list'],
# dataset_dir = self.directories.dataset_dir,
# results_dir = self.directories.results_dir,
# working_dir = self.directories.working_dir,
# run_list = self.run_list
# ),
# name='info_source',
# )
# info_source.iterables = [('subject_id', self.subject_list)]
#
# # Templates to select files node
# # [TODO] Change the name of the files depending on the filenames of results of preprocessing
# templates = {
# 'func': join(
# self.directories.results_dir,
# 'preprocess',
# '_run_id_*_subject_id_{subject_id}',
# 'complete_filename_{subject_id}_complete_filename.nii',
# ),
# 'event': join(
# self.directories.dataset_dir,
# 'sub-{subject_id}',
# 'func',
# 'sub-{subject_id}_task-MGT_run-*_events.tsv',
# )
# }
#
# # SelectFiles node - to select necessary files
# select_files = Node(
# SelectFiles(templates, base_directory = self.directories.dataset_dir),
# name = 'select_files'
# )
#
# # DataSink Node - store the wanted results in the wanted repository
# data_sink = Node(
# DataSink(base_directory = self.directories.output_dir),
# name = 'data_sink'
# )
#
# # [INFO] This is the node executing the get_subject_infos_spm function
# # Subject Infos node - get subject specific condition information
# subject_infos = Node(
# Function(
# input_names = ['event_files', 'runs'],
# output_names = ['subject_info'],
# function = self.get_subject_infos,
# ),
# name = 'subject_infos',
# )
# subject_infos.inputs.runs = self.run_list
#
# # [INFO] This is the node executing the get_contrasts function
# # Contrasts node - to get contrasts
# contrasts = Node(
# Function(
# input_names = ['subject_id'],
# output_names = ['contrasts'],
# function = self.get_contrasts,
# ),
# name = 'contrasts',
# )
#
# # [INFO] The following part has to be modified with nodes of the pipeline
#
# # [TODO] For each node, replace 'node_name' by an explicit name, and use it for both:
# # - the name of the variable in which you store the Node object
# # - the 'name' attribute of the Node
# # [TODO] The node_function refers to a NiPype interface that you must import
# # at the beginning of the file.
# node_name = Node(
# node_function,
# name = 'node_name'
# )
#
# # [TODO] Add other nodes with the different steps of the pipeline
#
# # [INFO] The following part defines the nipype workflow and the connections between nodes
#
# subject_level_analysis = Workflow(
# base_dir = self.directories.working_dir,
# name = 'subject_level_analysis'
# )
# # [TODO] Add the connections the workflow needs
# # [INFO] Input and output names can be found on NiPype documentation
# subject_level_analysis.connect([
# (
# info_source,
# select_files,
# [('subject_id', 'subject_id')]
# ),
# (
# info_source,
# contrasts,
# [('subject_id', 'subject_id')]
# ),
# (
# select_files,
# subject_infos,
# [('event', 'event_files')]
# ),
# (
# select_files,
# node_name,
# [('func', 'node_input_name')]
# ),
# (
# node_name, data_sink,
# [('node_output_name', 'preprocess.@sym_link')]
# ),
# ])
#
# # [INFO] Here we simply return the created workflow
# return subject_level_analysis
""" Return a Nipype workflow describing the subject level analysis part of the pipeline """

# [INFO] The following part stays the same for all pipelines

# Infosource Node - To iterate on subjects
info_source = Node(
IdentityInterface(
fields = ['subject_id', 'dataset_dir', 'results_dir', 'working_dir', 'run_list'],
dataset_dir = self.directories.dataset_dir,
results_dir = self.directories.results_dir,
working_dir = self.directories.working_dir,
run_list = self.run_list
),
name='info_source',
)
info_source.iterables = [('subject_id', self.subject_list)]

# Templates to select files node
# [TODO] Change the name of the files depending on the filenames of results of preprocessing
templates = {
'func': join(
self.directories.results_dir,
'preprocess',
'_run_id_*_subject_id_{subject_id}',
'complete_filename_{subject_id}_complete_filename.nii',
),
'event': join(
self.directories.dataset_dir,
'sub-{subject_id}',
'func',
'sub-{subject_id}_task-MGT_run-*_events.tsv',
)
}

# SelectFiles node - to select necessary files
select_files = Node(
SelectFiles(templates, base_directory = self.directories.dataset_dir),
name = 'select_files'
)

# DataSink Node - store the wanted results in the wanted repository
data_sink = Node(
DataSink(base_directory = self.directories.output_dir),
name = 'data_sink'
)

# [INFO] This is the node executing the get_subject_infos_spm function
# Subject Infos node - get subject specific condition information
subject_infos = Node(
Function(
input_names = ['event_files', 'runs'],
output_names = ['subject_info'],
function = self.get_subject_infos,
),
name = 'subject_infos',
)
subject_infos.inputs.runs = self.run_list

# [INFO] This is the node executing the get_contrasts function
# Contrasts node - to get contrasts
contrasts = Node(
Function(
input_names = ['subject_id'],
output_names = ['contrasts'],
function = self.get_contrasts,
),
name = 'contrasts',
)

# [INFO] The following part has to be modified with nodes of the pipeline

# [TODO] For each node, replace 'node_name' by an explicit name, and use it for both:
# - the name of the variable in which you store the Node object
# - the 'name' attribute of the Node
# [TODO] The node_function refers to a NiPype interface that you must import
# at the beginning of the file.
node_name = Node(
node_function,
name = 'node_name'
)

# [TODO] Add other nodes with the different steps of the pipeline

# [INFO] The following part defines the nipype workflow and the connections between nodes

subject_level_analysis = Workflow(
base_dir = self.directories.working_dir,
name = 'subject_level_analysis'
)
# [TODO] Add the connections the workflow needs
# [INFO] Input and output names can be found on NiPype documentation
subject_level_analysis.connect([
(
info_source,
select_files,
[('subject_id', 'subject_id')]
),
(
info_source,
contrasts,
[('subject_id', 'subject_id')]
),
(
select_files,
subject_infos,
[('event', 'event_files')]
),
(
select_files,
node_name,
[('func', 'node_input_name')]
),
(
node_name, data_sink,
[('node_output_name', 'preprocess.@sym_link')]
),
])

# [INFO] Here we simply return the created workflow
return subject_level_analysis
return None

# [INFO] This function returns the list of ids and files of each group of participants
Expand Down

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