diff --git a/narps_open/core/interfaces.py b/narps_open/core/interfaces/__init__.py similarity index 100% rename from narps_open/core/interfaces.py rename to narps_open/core/interfaces/__init__.py diff --git a/narps_open/core/interfaces/afni.py b/narps_open/core/interfaces/afni.py new file mode 100644 index 00000000..55e4fb85 --- /dev/null +++ b/narps_open/core/interfaces/afni.py @@ -0,0 +1,226 @@ +#!/usr/bin/python +# coding: utf-8 + +""" AFNI interfaces for Nipype """ + +from os.path import abspath + +from nipype.interfaces.base import traits, File, Str, InputMultiPath, isdefined +from nipype.interfaces.afni.base import ( + AFNICommand, + AFNICommandInputSpec, + AFNICommandOutputSpec +) + +class TtestppInputSpec(AFNICommandInputSpec): + """ The input specification of the 3dttest++ interface """ + + set_a_label = Str( + desc='label for setA', + argstr='-labelA %s ', + mandatory=True + ) + set_a = InputMultiPath( + traits.Tuple( + File(desc='3D dataset', exists=True), + traits.Int(desc='Index of the data in the dataset'), + ), + desc='specifies a set of input datasets for setA + their label.', + argstr='-setA %s ', # Data tuples will be formatted in the _format_arg method + requires=['set_a_label'], + mandatory=True + ) + set_b_label = Str( + desc='label for setB', + argstr='-labelB %s ', + ) + set_b = InputMultiPath( + traits.Tuple( + File(desc='3D dataset', exists=True), + traits.Int(desc='Index of the data in the dataset'), + ), + desc='specifies a set of input datasets for setB + their label.', + argstr='-setB %s ', # Data tuples will be formatted in the _format_arg method + requires=['set_b_label'], + ) + set_a_weight = File( + desc='Name of a file with the weights for the -setA datasets.', + argstr='-setweightA %s ', + requires=['set_a'], + exists=True, + ) + set_b_weight = File( + desc='Name of a file with the weights for the -setB datasets.', + argstr='-setweightB %s ', + requires=['set_b'], + exists=True, + ) + covariates = File( + desc='name of a text file with a table for the covariate(s).' + ' Each column in the file is treated as a separate covariate, and each' + ' row contains the values of these covariates for one sample (dataset).', + argstr='-covariates %s ', + exists=True, + ) + center = traits.Enum('NONE', 'DIFF', 'SAME', + desc='how the mean across subjects of a covariate will be processed.', + argstr='-center %s ', + requires=['covariates'], + ) + paired = traits.Bool( + desc='specifies the use of a paired-sample t-test to compare setA and setB.', + argstr='-paired ', + requires=['set_a', 'set_b'], + ) + unpooled = traits.Bool( + desc='specifies that the variance estimates for setA and setB be computed ' + 'separately (not pooled together).', + argstr='-paired ', + requires=['set_a', 'set_b'], + xor=['paired', 'covariates'] + ) + toz = traits.Bool( + desc='convert output t-statistics to z-scores.', + argstr='-toz ', + ) + rankize = traits.Bool( + desc='convert the data (and covariates, if any) into ranks before ' + 'doing the 2-sample analyses.', + argstr='-rankize ', + ) + no1sam = traits.Bool( + desc='do not calculate 1-sample test results when ' + 'doing 2-sample analyses.', + argstr='-no1sam ', + ) + nomeans = traits.Bool( + desc='turn off output of the `mean` sub-bricks.', + argstr='-nomeans ', + ) + notests = traits.Bool( + desc='turn off output of the `test` sub-bricks.', + argstr='-notests ', + ) + mask = File( + desc='Only compute results for voxels in the specified mask.', + argstr='-mask %s ', + exists=True, + ) + exblur = traits.Float( + desc='Before doing the t-test, apply some extra blurring to the input datasets; ' + "parameter 'b' is the Gaussian FWHM of the smoothing kernel (in mm).", + argstr='-exblur %d ', + ) + brickwise = traits.Bool( + desc='carry out t-tests sub-brick by sub-brick', + argstr='-brickwise ', + position=0 + ) + out_file = Str( + desc='the name of the output dataset file.', + argstr='-prefix %s ', + position=-1 + ) + out_residuals = Str( + desc='output the residuals into a dataset with given prefix.', + argstr='-resid %s ', + position=-2 + ) + clustsim = traits.Bool( + desc='run the cluster-size threshold simulation program 3dClustSim.', + argstr='-Clustsim ', + position=-4 + ) + seed = traits.Tuple( + traits.Int, traits.Int, + desc='This option is used to set the random number seed for' + '`-randomsign` to the first positive integer. If a second integer' + 'follows, then that value is used for the random number seed for `-permute`.', + argstr='-seed %d %d ', + requires=['clustsim'], + position=-3 + ) + +class Ttestpp(AFNICommand): + """ Gosset (Student) t-test of sets of 3D datasets. + + For complete details, see the `3dttest++ Documentation. + `_ + """ + + _cmd = '3dttest++' + input_spec = TtestppInputSpec + output_spec = AFNICommandOutputSpec + + def _format_arg(self, name, trait_spec, value): + """ Format arguments before actually building the command line """ + out_value = value + + # For arguments -setA and -setB, we want a list such as : + # dataset1'[index]' dataset2'[index]' dataset3'[index]' ... + if name in ['set_a', 'set_b']: + out_value = '' + for set_tuple in value: + out_value += f'{set_tuple[0]}\'[{set_tuple[1]}]\' ' + out_value = [out_value] # Return a list as the input value + + return super()._format_arg(name, trait_spec, out_value) + + def _list_outputs(self): + outputs = self.output_spec().get() + + for key in outputs.keys(): + if isdefined(self.inputs.get()[key]): + outputs[key] = abspath(self.inputs.get()[key]) + + return outputs + +class SelectSBInputSpec(AFNICommandInputSpec): + """ The input specification for SelectSubBrick """ + in_file = File( + desc = 'File name to select a subbrick from.', + argstr = '%s', + position = -2, + mandatory = True, + copyfile = False, + exists = True + ) + index = traits.Int( + desc = 'Index of the subbrick inside in_file.', + mandatory = True + ) + out_file = File(desc = 'Output image file name.', argstr = '-prefix %s', genfile = True) + +class SelectSubBrick(AFNICommand): + """ Sub-brick selection from AFNI files. + + For complete details, see the `3dTcat Documentation. + + """ + + _cmd = '3dTcat' + input_spec = SelectSBInputSpec + output_spec = AFNICommandOutputSpec + + #def _gen_filename(self, name): + # if name == 'out_file': + # return self._gen_fname(self.inputs.in_file, suffix = '_tcat') + + def _list_outputs(self): + outputs = self.output_spec().get() + + for key in outputs.keys(): + if isdefined(self.inputs.get()[key]): + outputs[key] = abspath(self.inputs.get()[key]) + + return outputs + + def _format_arg(self, name, trait_spec, value): + """ Format arguments before actually building the command line """ + out_value = value + + # For argument in_file, append index, such as : in_file'[index]' + if name == 'in_file': + out_value = f'{value}\'[{self.inputs.index}]\'' + + return super()._format_arg(name, trait_spec, out_value) diff --git a/narps_open/pipelines/__init__.py b/narps_open/pipelines/__init__.py index 2d67eeee..4f3a2693 100644 --- a/narps_open/pipelines/__init__.py +++ b/narps_open/pipelines/__init__.py @@ -33,7 +33,7 @@ '5G9K': None, '6FH5': None, '6VV2': None, - '80GC': None, + '80GC': 'PipelineTeam80GC', '94GU': None, '98BT': 'PipelineTeam98BT', '9Q6R': None, diff --git a/narps_open/pipelines/team_80GC.py b/narps_open/pipelines/team_80GC.py new file mode 100644 index 00000000..b35fdf1d --- /dev/null +++ b/narps_open/pipelines/team_80GC.py @@ -0,0 +1,569 @@ +#!/usr/bin/python +# coding: utf-8 + +""" Write the work of NARPS' team 80GC using Nipype """ + +from os.path import join +from itertools import product + +from nipype import Workflow, Node, MapNode +from nipype.interfaces.utility import IdentityInterface, Function +from nipype.interfaces.io import SelectFiles, DataSink +from nipype.interfaces.afni import Deconvolve, MaskTool + +from narps_open.pipelines import Pipeline +from narps_open.data.participants import get_group +from narps_open.core.common import ( + list_intersection, elements_in_string, clean_list + ) +from narps_open.core.interfaces.afni import Ttestpp, SelectSubBrick + +class PipelineTeam80GC(Pipeline): + """ A class that defines the pipeline of team 80GC. """ + + def __init__(self): + super().__init__() + self.team_id = '80GC' + self.contrast_list = ['gain', 'loss'] + self.contrast_indices = [5, 6] + self.subject_level_contrasts = ['SYM: +gain', 'SYM: +loss'] + + def get_preprocessing(self): + """ No preprocessing has been done by team 80GC """ + return None + + def get_run_level_analysis(self): + """ No run level analysis has been done by team 80GC """ + return None + + def get_events_files(event_files, nb_events, subject_id): + """ + Create a stimuli file to be read by AFNI's 3DDeconvolve + + Parameters : + - event_files: list of str, event files, on for each run of one subject + - nb_events : int, number of events (i.e.: number of data lines in each event file) + WARNING - We assume that all events files have the same number of lines + - subject_id : related subject id + + Returns : + - gain_df_file: str, file containing gain regressor values + - loss_df_file: str, file containing gain regressor values + """ + from os.path import abspath + + from pandas import DataFrame, read_csv + + # Init empty dataframes + gain_df = DataFrame(index=range(0,len(event_files)), columns=range(0,nb_events)) + loss_df = DataFrame(index=range(0,len(event_files)), columns=range(0,nb_events)) + + # Extract info from raw event files + for file_id, event_file in enumerate(event_files): + events_df = read_csv(event_file, sep = '\t') + events_df = events_df[['onset', 'gain', 'loss']].T + + gain_df.loc[file_id] = [ + f"{events_df[i].loc['onset']}*{events_df[i].loc['gain']}"\ + for i in range(0, nb_events) + ] + loss_df.loc[file_id] = [ + f"{events_df[i].loc['onset']}*{events_df[i].loc['loss']}"\ + for i in range(0, nb_events) + ] + + # Create AFNI stimuli files + gain_file = abspath(f'events_sub-{subject_id}_timesxgain.txt') + loss_file = abspath(f'events_sub-{subject_id}_timesxloss.txt') + gain_df.to_csv(gain_file, sep='\t', index=False, header=False) + loss_df.to_csv(loss_file, sep='\t', index=False, header=False) + + return gain_file, loss_file + + def get_events_arguments(gain_event_file, loss_event_file): + """ + Create a stimuli arguments list to be passed to AFNI's 3DDeconvolve + + Parameters : + - gain_event_file: str, file containing gain regressor values + - loss_event_file: str, file containing loss regressor values + + Returns : + - arguments: str, formatted arguments for AFNI's 3DDeconvolve args input + """ + arguments = '-stim_label 1 gain ' + #arguments += f'-stim_times_AM2 1 {gain_event_file} \'BLOCK(4,1)\' ' + #arguments += f'-stim_times_AM2 1 {gain_event_file} \'GAM(8.6,.547,4)\' ' + arguments += f'-stim_times_AM2 1 {gain_event_file} \'GAM(8.6,.547)\' ' + arguments += '-stim_label 2 loss ' + #arguments += f'-stim_times_AM2 2 {loss_event_file} \'BLOCK(4,1)\' ' + #arguments += f'-stim_times_AM2 2 {loss_event_file} \'GAM(8.6,.547,4)\' ' + arguments += f'-stim_times_AM2 2 {loss_event_file} \'GAM(8.6,.547)\' ' + + return arguments + + def get_confounds_file(confounds_files, nb_time_points, subject_id): + """ + Create a new tsv file with only desired confounds for a subject + + Parameters : + - confounds_files : list of str, paths to the subject confounds files + - nb_time_points : int, number of time points + (i.e.: number of data lines in each confounds file) + WARNING - We assume that all confounds files have the same number of lines + - subject_id : related subject id + + Return : + - confounds_file : path to new file containing only desired confounds + """ + from os.path import abspath + + from pandas import DataFrame, read_csv + from numpy import array, transpose + + # Init empty dataframe + parameters_df = DataFrame( + index=range(0,nb_time_points*len(confounds_files)), columns=range(0,6)) + + # Open original confounds file + for file_id, file in enumerate(confounds_files): + data_frame = read_csv(file, sep = '\t', header=0) + + # Extract confounds we want to use for the model + parameters_df[file_id*nb_time_points:(file_id+1)*nb_time_points] = DataFrame( + transpose(array([ + data_frame['X'].sub(data_frame['X'].mean()), + data_frame['Y'].sub(data_frame['Y'].mean()), + data_frame['Z'].sub(data_frame['Z'].mean()), + data_frame['RotX'].sub(data_frame['RotX'].mean()), + data_frame['RotY'].sub(data_frame['RotY'].mean()), + data_frame['RotZ'].sub(data_frame['RotZ'].mean()) + ]))) + + # Write confounds to a file + confounds_file = abspath(f'confounds_file_sub-{subject_id}.tsv') + with open(confounds_file, 'w', encoding = 'utf-8') as writer: + writer.write(parameters_df.to_csv( + sep = '\t', index = False, header = False, na_rep = '0.0')) + + return confounds_file + + def get_confounds_arguments(confounds_file): + """ + Create a confounds arguments list to be passed to AFNI's 3DDeconvolve + + Parameters : + - confounds_file: str, file containing confounds + + Returns : + - arguments: str, formatted arguments for AFNI's 3DDeconvolve args input + """ + + arguments = '-stim_base 3 -stim_label 3 x_motion_regressor ' + arguments += f'-stim_file 3 {confounds_file}\'[0]\' ' + arguments += '-stim_base 4 -stim_label 4 y_motion_regressor ' + arguments += f'-stim_file 4 {confounds_file}\'[1]\' ' + arguments += '-stim_base 5 -stim_label 5 z_motion_regressor ' + arguments += f'-stim_file 5 {confounds_file}\'[2]\' ' + arguments += '-stim_base 6 -stim_label 6 rotx_motion_regressor ' + arguments += f'-stim_file 6 {confounds_file}\'[3]\' ' + arguments += '-stim_base 7 -stim_label 7 roty_motion_regressor ' + arguments += f'-stim_file 7 {confounds_file}\'[4]\' ' + arguments += '-stim_base 8 -stim_label 8 rotz_motion_regressor ' + arguments += f'-stim_file 8 {confounds_file}\'[5]\' ' + + return arguments + + def get_subject_level_analysis(self): + """ + Create the subject level analysis workflow. + + Returns: + - subject_level : nipype.WorkFlow + WARNING: the name attribute of the workflow is 'subject_level_analysis' + """ + # Create subject level analysis workflow + subject_level = Workflow( + base_dir = self.directories.working_dir, + name = 'subject_level_analysis') + + # IDENTITY INTERFACE - To iterate on subjects + information_source = Node(IdentityInterface( + fields = ['subject_id']), + name = 'information_source') + information_source.iterables = [('subject_id', self.subject_list)] + + # SELECT FILES - to select necessary files + templates = { + 'confounds' : join('derivatives', 'fmriprep', 'sub-{subject_id}', 'func', + 'sub-{subject_id}_task-MGT_run-*_bold_confounds.tsv'), + 'func' : join('derivatives', 'fmriprep', 'sub-{subject_id}', 'func', + 'sub-{subject_id}_task-MGT_run-*_bold_space-MNI152NLin2009cAsym_preproc.nii.gz'), + 'event' : join('sub-{subject_id}', 'func', + 'sub-{subject_id}_task-MGT_run-*_events.tsv') + } + select_files = Node(SelectFiles(templates), name = 'select_files') + select_files.inputs.base_directory = self.directories.dataset_dir + subject_level.connect(information_source, 'subject_id', select_files, 'subject_id') + + # FUNCTION get_events_file - generate files with event data + events_information = Node(Function( + function = self.get_events_files, + input_names = ['event_files', 'nb_events', 'subject_id'], + output_names = ['gain_file', 'loss_file']), + name = 'events_information') + events_information.inputs.nb_events = 64 + subject_level.connect(select_files, 'event', events_information, 'event_files') + subject_level.connect(information_source, 'subject_id', events_information, 'subject_id') + + # FUNCTION get_events_arguments - generate arguments for 3ddeconvolve with event data + events_arguments = Node(Function( + function = self.get_events_arguments, + input_names = ['gain_event_file', 'loss_event_file'], + output_names = ['arguments']), + name = 'events_arguments') + subject_level.connect(events_information, 'gain_file', events_arguments, 'gain_event_file') + subject_level.connect(events_information, 'loss_file', events_arguments, 'loss_event_file') + + # FUNCTION get_confounds_file - generate files with event data + confounds_information = Node(Function( + function = self.get_confounds_file, + input_names = ['confounds_files', 'nb_time_points', 'subject_id'], + output_names = ['confounds_file']), + name = 'confounds_information') + confounds_information.inputs.nb_time_points = 453 + subject_level.connect(select_files, 'confounds', confounds_information, 'confounds_files') + subject_level.connect( + information_source, 'subject_id', confounds_information, 'subject_id') + + # FUNCTION get_events_arguments - generate arguments for 3ddeconvolve with confounds data + confounds_arguments = Node(Function( + function = self.get_confounds_arguments, + input_names = ['confounds_file'], + output_names = ['arguments']), + name = 'confounds_arguments') + subject_level.connect( + confounds_information, 'confounds_file', confounds_arguments, 'confounds_file') + + # FUNCTION concatenate strings - merge outputs from get_events_file and get_confounds_file + concat_function = lambda a_str, b_str, c_str: a_str + b_str + c_str + merge_arguments = Node(Function( + function = concat_function, + input_names = ['a_str', 'b_str', 'c_str'], + output_names = ['out_str']), + name = 'merge_arguments') + other_args = '-xjpeg design_matrix.jpg -num_stimts 8 ' + merge_arguments.inputs.a_str = other_args + subject_level.connect(events_arguments, 'arguments', merge_arguments, 'b_str') + subject_level.connect(confounds_arguments, 'arguments', merge_arguments, 'c_str') + + # DECONVOLVE - Run the regression analysis + deconvolve = Node(Deconvolve(), name = 'deconvolve') + deconvolve.inputs.polort = 4 # -float + deconvolve.inputs.fout = False + deconvolve.inputs.tout = False + deconvolve.inputs.goforit = 8 + deconvolve.inputs.num_threads = 1 # Parameter -jobs + deconvolve.inputs.x1D = 'design_matrix.xmat.1D' + deconvolve.inputs.out_file = 'out_deconvolve.nii' + deconvolve.inputs.gltsym = self.subject_level_contrasts + deconvolve.inputs.glt_label = [(1, self.contrast_list[0]), (2, self.contrast_list[1])] + subject_level.connect(select_files, 'func', deconvolve, 'in_files') + subject_level.connect(merge_arguments, 'out_str', deconvolve, 'args') + + # DATA SINK - store the wanted results in the wanted repository + data_sink = Node(DataSink(), name = 'data_sink') + data_sink.inputs.base_directory = self.directories.output_dir + subject_level.connect(deconvolve, 'out_file', data_sink, 'subject_level_analysis.@out_file') + subject_level.connect(deconvolve, 'x1D', data_sink, 'subject_level_analysis.@x1D') + + return subject_level + + def get_subject_level_outputs(self): + """ Return the names of the files the subject level analysis is supposed to generate. """ + + # Deconvolve output file containing contrasts etc. + # Here is the complete list of what the file contains: + # 0 - Full_Fstat + # 1 - gain#0_Coef + # 2 - gain#1_Coef + # 3 - loss#0_Coef + # 4 - loss#1_Coef + # 5 - gain_GLT#0_Coef + # 6 - loss_GLT#0_Coef + templates = [join( + self.directories.output_dir, + 'subject_level_analysis', '_subject_id_{subject_id}', 'out_deconvolve.nii')] + + # Design matrix + templates += [join( + self.directories.output_dir, + 'subject_level_analysis', '_subject_id_{subject_id}', 'design_matrix.xmat.1D')] + + # Format with subject_ids + return_list = [] + for template in templates: + return_list += [template.format(subject_id = s) for s in self.subject_list] + + return return_list + + def select_subbrick(in_file: str, index: int): + """ + Create a tuple (file, index) for AFNI interfaces allowing to select a sub-brick + for input file. + + Parameters : + - in_file: str, files to select the sub-brick from + - index: int, index of the desired sub-brick in in_file + + Returns : + - out: tuple, (in_file, index) + """ + return (in_file, index) + + def get_group_level_analysis(self): + """ + Return a workflow for the group level analysis. + + Returns: + - group_level: nipype.WorkFlow + """ + + # Init the workflow + nb_subjects = len(self.subject_list) + group_level = Workflow( + base_dir = self.directories.working_dir, + name = f'group_level_analysis_nsub_{nb_subjects}') + + # IDENTITY INTERFACE - Iterate over the list of contrasts from the subject level + info_source = Node( + IdentityInterface(fields=['contrast_id', 'contrast_index', 'subject_list']), + name = 'info_source' + ) + info_source.iterables = [ + ('contrast_id', self.contrast_list), + ('contrast_index', self.contrast_indices) + ] + info_source.inputs.subject_list = self.subject_list + info_source.synchronize = True + + # SELECT FILES - Select necessary files + templates = { + 'deconvolve_bucket' : join(self.directories.output_dir, 'subject_level_analysis', + '_subject_id_*', 'out_deconvolve.nii'), + 'masks' : join('derivatives', 'fmriprep', 'sub-*', 'func', + 'sub-*_task-MGT_run-*_bold_space-MNI152NLin2009cAsym_brainmask.nii.gz') + } + select_files = Node(SelectFiles(templates), name = 'select_files') + select_files.inputs.base_directory = self.directories.dataset_dir + select_files.inputs.force_list = True + + # SELECT SUBJECTS + + # Create a function to complete the subject ids out from the get_equal*_subjects node + complete_subject_ids = lambda l : [f'_subject_id_{a}' for a in l] + complete_sub_ids = lambda l : [f'sub-{a}' for a in l] + + # Function Node list_intersection - Get subjects from the subject_list + # that are in the `equalRange` group + equal_range_subjects = Node(Function( + function = list_intersection, + input_names = ['list_1', 'list_2'], + output_names = ['out_list'] + ), + name = 'equal_range_subjects' + ) + equal_range_subjects.inputs.list_1 = get_group('equalRange') + equal_range_subjects.inputs.list_2 = self.subject_list + + # Function Node list_intersection - Get subjects from the subject_list + # that are in the `equalIndifference` group + equal_indifference_subjects = Node(Function( + function = list_intersection, + input_names = ['list_1', 'list_2'], + output_names = ['out_list'] + ), + name = 'equal_indifference_subjects' + ) + equal_indifference_subjects.inputs.list_1 = get_group('equalIndifference') + equal_indifference_subjects.inputs.list_2 = self.subject_list + + # Function Node elements_in_string - Get contrast files for equalRange subjects + # Note : using a MapNode with elements_in_string requires using clean_list to remove + # None values from the out_list + equal_range_contrasts = MapNode(Function( + function = elements_in_string, + input_names = ['input_str', 'elements'], + output_names = ['out_list'] + ), + name = 'equal_range_contrasts', iterfield = 'input_str' + ) + group_level.connect(select_files, 'deconvolve_bucket', equal_range_contrasts, 'input_str') + group_level.connect( + equal_range_subjects, ('out_list', complete_subject_ids), + equal_range_contrasts, 'elements') + + # Function Node elements_in_string - Get contrast files for equalIndifference subjects + # Note : using a MapNode with elements_in_string requires using clean_list to remove + # None values from the out_list + equal_indifference_contrasts = MapNode(Function( + function = elements_in_string, + input_names = ['input_str', 'elements'], + output_names = ['out_list'] + ), + name = 'equal_indifference_contrasts', iterfield = 'input_str' + ) + group_level.connect( + select_files, 'deconvolve_bucket', equal_indifference_contrasts, 'input_str') + group_level.connect( + equal_indifference_subjects, ('out_list', complete_subject_ids), + equal_indifference_contrasts, 'elements') + + # Function Node select_subbrick - Create setA list of input files for 3dttest++ + set_a_arguments = MapNode(Function( + function = self.select_subbrick, + input_names = ['in_file', 'index'], + output_names = ['out'] + ), + name = 'set_a_arguments', + iterfield = 'in_file' + ) + group_level.connect(info_source, 'contrast_index', set_a_arguments, 'index') + group_level.connect( + equal_range_contrasts, ('out_list', clean_list), + set_a_arguments, 'in_file') + + # Function Node select_subbrick - Create setB list of input files for 3dttest++ + set_b_arguments = MapNode(Function( + function = self.select_subbrick, + input_names = ['in_file', 'index'], + output_names = ['out'] + ), + name = 'set_b_arguments', + iterfield = 'in_file' + ) + group_level.connect(info_source, 'contrast_index', set_b_arguments, 'index') + group_level.connect( + equal_indifference_contrasts, ('out_list', clean_list), + set_b_arguments, 'in_file') + + # Function Node elements_in_string - Get masks files for all subjects + # Note : using a MapNode with elements_in_string requires using clean_list to remove + # None values from the out_list + masks = MapNode(Function( + function = elements_in_string, + input_names = ['input_str', 'elements'], + output_names = ['out_list'] + ), + name = 'masks', iterfield = 'input_str' + ) + group_level.connect(select_files, 'masks', masks, 'input_str') + group_level.connect(info_source, ('subject_list', complete_sub_ids), masks, 'elements') + + # MASK TOOL - Create mask intersection + mask_intersection = Node(MaskTool(), name = 'mask_intersection') + mask_intersection.inputs.inter = True + mask_intersection.inputs.outputtype = 'NIFTI' + group_level.connect( + masks, ('out_list', clean_list), mask_intersection, 'in_file') + + # 3DTTEST++ - Perform a one sample t-test + t_test = Node(Ttestpp(), name = 't_test') + t_test.inputs.set_a_label = 'equalRange' + t_test.inputs.set_b_label = 'equalIndifference' + t_test.inputs.toz = True + t_test.inputs.clustsim = False + t_test.inputs.nomeans = True + t_test.inputs.out_file = 'ttestpp_out.nii' + group_level.connect(mask_intersection, 'out_file', t_test, 'mask') + group_level.connect(set_a_arguments, 'out', t_test, 'set_a') + group_level.connect(set_b_arguments, 'out', t_test, 'set_b') + + # Output dataset from t_test consists in 3 sub-bricks : + # #0 equalRange-equalIndiffe_Zscr + # #1 equalRange_Zscr + # #2 equalIndiffe_Zscr + + # SELECT SUB BRICK - Split output of 3dttest++ + select_output = MapNode(SelectSubBrick(), name = 'select_output', iterfield = 'index') + select_output.inputs.out_file = 'group_level_tstat.nii' + select_output.inputs.outputtype = 'NIFTI' + select_output.inputs.index = [0, 1, 2] + group_level.connect(t_test, 'out_file', select_output, 'in_file') + + # DATA SINK - save important files + data_sink = Node(DataSink(), name = 'data_sink') + data_sink.inputs.base_directory = self.directories.output_dir + group_level.connect( + select_output, 'out_file', + data_sink, f'group_level_analysis_nsub_{nb_subjects}.@out') + + return group_level + + def get_group_level_outputs(self): + """ Return all names for the files the group level analysis is supposed to generate. """ + + parameters = { + 'contrast_dir': [ + f'_contrast_id_{c}_contrast_index_{i}' for c, i \ + in zip(self.contrast_list, self.contrast_indices)], + 'nb_subjects' : [str(len(self.subject_list))], + 'method' : ['0', '1', '2'] + } + parameter_sets = product(*parameters.values()) + template = join( + self.directories.output_dir, + 'group_level_analysis_nsub_{nb_subjects}', + '{contrast_dir}', '_select_output{method}', 'group_level_tstat.nii' + ) + + return_list = [template.format(**dict(zip(parameters.keys(), parameter_values)))\ + for parameter_values in parameter_sets] + + return return_list + + def get_hypotheses_outputs(self): + """ Return all hypotheses output file names. + Note that hypotheses 5 to 8 correspond to the maps given by the team in their results ; + but they are not fully consistent with the hypotheses definitions as expected by NARPS. + """ + nb_sub = len(self.subject_list) + files = [ + # Hypothesis 1 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_gain_contrast_index_5', + '_select_output2', 'group_level_tstat.nii'), + # Hypothesis 2 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_gain_contrast_index_5', + '_select_output1', 'group_level_tstat.nii'), + # Hypothesis 3 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_gain_contrast_index_5', + '_select_output2', 'group_level_tstat.nii'), + # Hypothesis 4 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_gain_contrast_index_5', + '_select_output1', 'group_level_tstat.nii'), + # Hypothesis 5 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_loss_contrast_index_6', + '_select_output2', 'group_level_tstat.nii'), + # Hypothesis 6 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_loss_contrast_index_6', + '_select_output1', 'group_level_tstat.nii'), + # Hypothesis 7 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_loss_contrast_index_6', + '_select_output2', 'group_level_tstat.nii'), + # Hypothesis 8 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_loss_contrast_index_6', + '_select_output1', 'group_level_tstat.nii'), + # Hypothesis 9 + '', + join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_loss_contrast_index_6', + '_select_output0', 'group_level_tstat.nii'), + ] + return [join(self.directories.output_dir, f) for f in files] diff --git a/tests/pipelines/test_team_80GC.py b/tests/pipelines/test_team_80GC.py new file mode 100644 index 00000000..3ee83eda --- /dev/null +++ b/tests/pipelines/test_team_80GC.py @@ -0,0 +1,136 @@ +#!/usr/bin/python +# coding: utf-8 + +""" Tests of the 'narps_open.pipelines.team_80GC' module. + +Launch this test with PyTest + +Usage: +====== + pytest -q test_team_80GC.py + pytest -q test_team_80GC.py -k +""" +from os.path import join, exists +from filecmp import cmp + +from pytest import helpers, mark +from nipype import Workflow, Node, Function +from nipype.interfaces.base import Bunch + +from narps_open.utils.configuration import Configuration +from narps_open.pipelines.team_80GC import PipelineTeam80GC + +class TestPipelinesTeam80GC: + """ A class that contains all the unit tests for the PipelineTeam80GC class.""" + + @staticmethod + @mark.unit_test + def test_create(): + """ Test the creation of a PipelineTeam80GC object """ + + pipeline = PipelineTeam80GC() + + # 1 - check the parameters + assert pipeline.team_id == '80GC' + + # 2 - check workflows + assert pipeline.get_preprocessing() is None + assert pipeline.get_run_level_analysis() is None + assert isinstance(pipeline.get_subject_level_analysis(), Workflow) + assert isinstance(pipeline.get_group_level_analysis(), Workflow) + + @staticmethod + @mark.unit_test + def test_outputs(): + """ Test the expected outputs of a PipelineTeam80GC object """ + pipeline = PipelineTeam80GC() + # 1 - 1 subject outputs + pipeline.subject_list = ['001'] + helpers.test_pipeline_outputs(pipeline, [0, 0, 2*1, 0, 18]) + + # 2 - 4 subjects outputs + pipeline.subject_list = ['001', '002', '003', '004'] + helpers.test_pipeline_outputs(pipeline, [0, 0, 2*4, 0, 18]) + + @staticmethod + @mark.unit_test + def test_events_files(temporary_data_dir): + """ Test the get_events_files method """ + + # Get test files + test_file = join(Configuration()['directories']['test_data'], 'pipelines', 'events.tsv') + reference_file_gain = join(Configuration()['directories']['test_data'], + 'pipelines', 'team_80GC', 'events_sub-sid_timesxgain.txt') + reference_file_loss = join(Configuration()['directories']['test_data'], + 'pipelines', 'team_80GC', 'events_sub-sid_timesxloss.txt') + + # Create a Nipype Node using get_events_files + test_get_events_files = Node(Function( + function = PipelineTeam80GC.get_events_files, + input_names = ['event_files', 'nb_events', 'subject_id'], + output_names = ['event_files'] + ), name = 'test_get_events_files') + test_get_events_files.base_dir = temporary_data_dir + test_get_events_files.inputs.event_files = [test_file, test_file] + test_get_events_files.inputs.nb_events = 5 + test_get_events_files.inputs.subject_id = 'sid' + test_get_events_files.run() + + # Check event files were created + event_file_gain = join( + temporary_data_dir, test_get_events_files.name, 'events_sub-sid_timesxgain.txt') + event_file_loss = join( + temporary_data_dir, test_get_events_files.name, 'events_sub-sid_timesxloss.txt') + assert exists(event_file_gain) + assert exists(event_file_loss) + + # Compare output files to expected + assert cmp(reference_file_gain, event_file_gain) + assert cmp(reference_file_loss, event_file_loss) + + @staticmethod + @mark.unit_test + def test_events_arguments(): + """ Test the get_events_arguments method """ + + @staticmethod + @mark.unit_test + def test_confounds_file(temporary_data_dir): + """ Test the get_confounds_file method """ + + confounds_file = join( + Configuration()['directories']['test_data'], 'pipelines', 'confounds.tsv') + reference_file = join( + Configuration()['directories']['test_data'], 'pipelines', + 'team_80GC', 'confounds_file_sub-sid.tsv') + + # Create a Nipype Node using get_confounds_file + test_get_confounds_file = Node(Function( + function = PipelineTeam80GC.get_confounds_file, + input_names = ['confounds_files', 'nb_time_points', 'subject_id'], + output_names = ['confounds_file'] + ), name = 'test_get_confounds_file') + test_get_confounds_file.base_dir = temporary_data_dir + test_get_confounds_file.inputs.confounds_files = [confounds_file, confounds_file] + test_get_confounds_file.inputs.nb_time_points = 3 + test_get_confounds_file.inputs.subject_id = 'sid' + test_get_confounds_file.run() + + # Check confounds file was created + created_confounds_file = join( + temporary_data_dir, test_get_confounds_file.name, 'confounds_file_sub-sid.tsv') + assert exists(created_confounds_file) + + # Check contents + assert cmp(reference_file, created_confounds_file) + + @staticmethod + @mark.unit_test + def test_confounds_arguments(): + """ Test the get_confounds_arguments method """ + + @staticmethod + @mark.pipeline_test + def test_execution(): + """ Test the execution of a PipelineTeam80GC and compare results """ + helpers.test_pipeline_evaluation('80GC') diff --git a/tests/test_data/pipelines/team_80GC/confounds.tsv b/tests/test_data/pipelines/team_80GC/confounds.tsv new file mode 100644 index 00000000..576f524f --- /dev/null +++ b/tests/test_data/pipelines/team_80GC/confounds.tsv @@ -0,0 +1,1812 @@ +0 -0.06886631935540839 -0.2697693266887417 0.04434606222584989 -0.008690918112582779 0.0036983659602648996 -0.0037481627269315664 +1 -0.0788352693554084 -0.3011137266887417 0.04434305291584989 -0.007364048112582779 0.0033141729602648995 -0.003916352726931567 +2 -0.06889201475540839 -0.2790066766887417 0.0993127622258499 -0.007693640112582779 0.0035009159602648996 -0.004147150726931567 +3 -0.06889916575540839 -0.2118282266887417 0.11362876222584989 -0.00857638411258278 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