def __init__(self, datasink, TR, num_vol): # specify input and output nodes self.datasink = datasink self.TR = TR self.num_vol = num_vol # specify nodes # SpecifyModel - Generates SPM-specific Model self.modelspec = pe.Node(interface=model.SpecifySPMModel(), name='model_specification') self.modelspec.inputs.input_units = 'secs' self.modelspec.inputs.output_units = 'secs' self.modelspec.inputs.time_repetition = self.TR self.modelspec.inputs.high_pass_filter_cutoff = 128 subjectinfo = [ Bunch(conditions=['None'], onsets=[list(range(self.num_vol))], durations=[[0.5]]) ] self.modelspec.inputs.subject_info = subjectinfo # Level1Design - Generates an SPM design matrix self.level1design = pe.Node(interface=spm.Level1Design(), name='first_level_design') self.level1design.inputs.bases = {'hrf': {'derivs': [1, 1]}} self.level1design.inputs.interscan_interval = self.TR self.level1design.inputs.timing_units = 'secs' # EstimateModel - estimate the parameters of the model # method can be 'Classical', 'Bayesian' or 'Bayesian2' self.level1estimate = pe.Node(interface=spm.EstimateModel(), name="first_level_estimate") self.level1estimate.inputs.estimation_method = {'Classical': 1} self.threshold = pe.Node(interface=spm.Threshold(), name="threshold") self.threshold.inputs.contrast_index = 1 # EstimateContrast - estimates contrasts self.contrast_estimate = pe.Node(interface=spm.EstimateContrast(), name="contrast_estimate") cont1 = ('active > rest', 'T', ['None'], [1]) contrasts = [cont1] self.contrast_estimate.inputs.contrasts = contrasts # specify workflow instance self.workflow = pe.Workflow(name='first_level_analysis_workflow') # connect nodes self.workflow.connect([ (self.modelspec, self.level1design, [('session_info', 'session_info')]), (self.level1design, self.level1estimate, [('spm_mat_file', 'spm_mat_file')]), (self.level1estimate, self.contrast_estimate, [('spm_mat_file', 'spm_mat_file'), ('beta_images', 'beta_images'), ('residual_image', 'residual_image')]), # (self.contrast_estimate, self.threshold, [('spm_mat_file', 'spm_mat_file'), ('spmT_images', 'stat_image')]), (self.contrast_estimate, self.datasink, [('con_images', 'contrast_img'), ('spmT_images', 'contrast_T')]) ])
def build_pipeline(scans, vectors, names, contrasts, destdir, explicitmask, analysis_name='analysis', verbose=True): ''' Build a Nipype pipeline for Multiple Regression analysis over a given type of parametric maps (param), using data from an Excel sheet as regressors (columns in 'names') and a given explicit mask. The whole analysis will be performed in the directory 'destdir'.''' print(['Analysis name:', analysis_name]) centering = [1] * len(names) if verbose: print(['Scans (%s):' % len(scans), scans]) print(['Vectors (%s)' % len(vectors)]) print(['Names (%s):' % len(names), names]) print(['Contrasts (%s):' % len(contrasts), contrasts]) covariates = [] for name, v, c in zip(names, vectors, centering): covariates.append(dict(name=name, centering=c, vector=v)) model = spm.model.MultipleRegressionDesign(in_files=scans, user_covariates=covariates, explicit_mask_file=explicitmask, use_implicit_threshold=True) est = spm.EstimateModel(estimation_method={'Classical': 1}) con = spm.EstimateContrast(contrasts=contrasts, group_contrast=True) # Creating Workflow a = pe.Workflow(name=analysis_name) a.base_dir = destdir n1 = pe.Node(model, name='modeldesign') n2 = pe.Node(est, name='estimatemodel') n3 = pe.Node(con, name='estimatecontrasts') a.connect([(n1, n2, [('spm_mat_file', 'spm_mat_file')]), (n2, n3, [('spm_mat_file', 'spm_mat_file'), ('beta_images', 'beta_images'), ('residual_image', 'residual_image')]), ]) a.config['execution']['stop_on_first_rerun'] = True a.config['execution']['remove_unnecessary_outputs'] = False return a
l1analysis = pe.Workflow(name='analysis') """Generate SPM-specific design information using :class:`nipype.interfaces.spm.SpecifyModel`. """ modelspec = pe.Node(interface=model.SpecifySPMModel(), name="modelspec") """Generate a first level SPM.mat file for analysis :class:`nipype.interfaces.spm.Level1Design`. """ level1design = pe.Node(interface=spm.Level1Design(), name="level1design") """Use :class:`nipype.interfaces.spm.EstimateModel` to determine the parameters of the model. """ level1estimate = pe.Node(interface=spm.EstimateModel(), name="level1estimate") level1estimate.inputs.estimation_method = {'Classical': 1} threshold = pe.Node(interface=spm.Threshold(), name="threshold") """Use :class:`nipype.interfaces.spm.EstimateContrast` to estimate the first level contrasts specified in a few steps above. """ contrastestimate = pe.Node(interface=spm.EstimateContrast(), name="contrastestimate") def pickfirst(l): return l[0]
:class:`nipype.interfaces.spm.SpecifyModel`. """ modelspec = pe.Node(model.SpecifySPMModel(), name="modelspec") modelspec.inputs.concatenate_runs = True """Generate a first level SPM.mat file for analysis :class:`nipype.interfaces.spm.Level1Design`. """ level1design = pe.Node(spm.Level1Design(), name="level1design") level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}} """Use :class:`nipype.interfaces.spm.EstimateModel` to determine the parameters of the model. """ level1estimate = pe.Node(spm.EstimateModel(), name="level1estimate") level1estimate.inputs.estimation_method = {'Classical': 1} """Use :class:`nipype.interfaces.spm.EstimateContrast` to estimate the first level contrasts specified in a few steps above. """ contrastestimate = pe.Node(spm.EstimateContrast(), name="contrastestimate") """Use :class: `nipype.interfaces.utility.Select` to select each contrast for reporting. """ selectcontrast = pe.Node(niu.Select(), name="selectcontrast") """Use :class:`nipype.interfaces.fsl.Overlay` to combine the statistical output of the contrast estimate and a background image into one volume. """
from nipype.interfaces import spm from nipype import Node, Workflow, MapNode import nipype.interfaces.utility as util # utility from nipype import SelectFiles import os from nipype.interfaces.matlab import MatlabCommand MatlabCommand.set_default_paths('/home/rj299/project/MATLAB/toolbox/spm12/' ) # set default SPM12 path in my computer. #%% Gourp analysis - based on SPM - should consider the fsl Randomize option (other script) # OneSampleTTestDesign - creates one sample T-Test Design onesamplettestdes = Node(spm.OneSampleTTestDesign(), name="onesampttestdes") # EstimateModel - estimates the model level2estimate = Node(spm.EstimateModel(estimation_method={'Classical': 1}), name="level2estimate") # EstimateContrast - estimates group contrast level2conestimate = Node(spm.EstimateContrast(group_contrast=True), name="level2conestimate") cont1 = ['Group', 'T', ['mean'], [1]] level2conestimate.inputs.contrasts = [cont1] # Which contrasts to use for the 2nd-level analysis contrast_list = [ 'con_0001', 'con_0002', 'con_0003', 'con_0004', 'con_0005', 'con_0006', 'con_0007', 'con_0008', 'con_0009', 'con_0010', 'con_0011', 'con_0012', 'con_0013', 'con_0014' ]
####################################################################################################################### # Initiation of a workflow wf = Workflow(name="l1run", base_dir="/media/Data/work") wf.connect([ (infosource, datasource, [('subject_id', 'subject_id')]), (datasource, gunzip, [('func', 'in_file')]), (gunzip, modelspec, [('out_file', 'functional_runs')]), (infosource, getsubjectinfo, [('subject_id', 'subject_id')]), (getsubjectinfo, modelspec, [('subject_info', 'subject_info')]), ]) wf.connect([(modelspec, level1design, [("session_info", "session_info")])]) ##########################################################################3 level1estimate = pe.Node(interface=spm.EstimateModel(), name="level1estimate") level1estimate.inputs.estimation_method = {'Classical': 1} contrastestimate = pe.Node(interface=spm.EstimateContrast(), name="contrastestimate") contrastestimate.inputs.contrasts = contrasts contrastestimate.overwrite = True contrastestimate.config = {'execution': {'remove_unnecessary_outputs': False}} ######################################################################## #%% Connecting level1 estimation and contrasts wf.connect([ (level1design, level1estimate, [('spm_mat_file', 'spm_mat_file')]), (level1estimate, contrastestimate, [('spm_mat_file', 'spm_mat_file'), ('beta_images', 'beta_images'), ('residual_image', 'residual_image')]),
def create_first_SPM(name='modelfit'): """First level task-fMRI modelling workflow Parameters ---------- name : name of workflow. Default = 'modelfit' Inputs ------ inputspec.session_info : inputspec.interscan_interval : inputspec.contrasts : inputspec.functional_data : inputspec.bases : inputspec.model_serial_correlations : Outputs ------- outputspec.copes : outputspec.varcopes : outputspec.dof_file : outputspec.pfiles : outputspec.parameter_estimates : outputspec.zstats : outputspec.tstats : outputspec.design_image : outputspec.design_file : outputspec.design_cov : Returns ------- workflow : first-level workflow """ import nipype.interfaces.spm as spm # fsl import nipype.interfaces.freesurfer as fs import nipype.pipeline.engine as pe import nipype.interfaces.utility as util modelfit = pe.Workflow(name=name) inputspec = pe.Node(util.IdentityInterface(fields=[ 'session_info', 'interscan_interval', 'contrasts', 'estimation_method', 'bases', 'mask', 'model_serial_correlations' ]), name='inputspec') level1design = pe.Node(interface=spm.Level1Design(timing_units='secs'), name="create_level1_design") modelestimate = pe.Node(interface=spm.EstimateModel(), name='estimate_model') conestimate = pe.Node(interface=spm.EstimateContrast(), name='estimate_contrast') convert = pe.MapNode(interface=fs.MRIConvert(out_type='nii'), name='convert', iterfield=['in_file']) outputspec = pe.Node(util.IdentityInterface(fields=[ 'RPVimage', 'beta_images', 'mask_image', 'residual_image', 'con_images', 'ess_images', 'spmF_images', 'spmT_images', 'spm_mat_file' ]), name='outputspec') # Utility function pop_lambda = lambda x: x[0] # Setup the connections modelfit.connect([ (inputspec, level1design, [('interscan_interval', 'interscan_interval'), ('session_info', 'session_info'), ('bases', 'bases'), ('mask', 'mask_image'), ('model_serial_correlations', 'model_serial_correlations')]), (inputspec, conestimate, [('contrasts', 'contrasts')]), (inputspec, modelestimate, [('estimation_method', 'estimation_method') ]), (level1design, modelestimate, [('spm_mat_file', 'spm_mat_file')]), (modelestimate, conestimate, [('beta_images', 'beta_images'), ('residual_image', 'residual_image'), ('spm_mat_file', 'spm_mat_file')]), (modelestimate, outputspec, [('RPVimage', 'RPVimage'), ('beta_images', 'beta_images'), ('mask_image', 'mask_image'), ('residual_image', 'residual_image')]), (conestimate, convert, [('con_images', 'in_file')]), (convert, outputspec, [('out_file', 'con_images')]), (conestimate, outputspec, [('ess_images', 'ess_images'), ('spmF_images', 'spmF_images'), ('spmT_images', 'spmT_images'), ('spm_mat_file', 'spm_mat_file')]) ]) return modelfit
level1design = spm.Level1Design() level1design.inputs.timing_units = 'secs' level1design.inputs.interscan_interval = TR level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}} level1design.inputs.model_serial_correlations = 'AR(1)' level1design.inputs.session_info = out.outputs.session_info out = level1design.run() #shutil.move(out.outputs.spm_mat_file,output_dir+'S'+str(subj)+'/by_category') print "- Estimate", sys.stdout.flush() level1estimate = spm.EstimateModel() level1estimate.inputs.estimation_method = {'Classical': 1} level1estimate.inputs.spm_mat_file = output_dir + 'S' + str( subj) + '/by_category/SPM.mat' out = level1estimate.run() print "- Contrast", level1contrast = spm.EstimateContrast() level1contrast.inputs.spm_mat_file = out.outputs.spm_mat_file level1contrast.inputs.beta_images = out.outputs.beta_images level1contrast.inputs.residual_image = out.outputs.residual_image cont1 = ('family>party', 'T', ['family', 'party'], [1, -1]) cont2 = ('family>sex', 'T', ['family', 'sex'], [1, -1]) cont3 = ('family>work', 'T', ['family', 'work'], [1, -1]) cont4 = ('party>sex', 'T', ['party', 'sex'], [1, -1])
def create_model_fit_pipeline(high_pass_filter_cutoff=128, nipy=False, ar1=True, name="model", save_residuals=False): inputnode = pe.Node(interface=util.IdentityInterface(fields=[ 'outlier_files', "realignment_parameters", "functional_runs", "mask", 'conditions', 'onsets', 'durations', 'TR', 'contrasts', 'units', 'sparse' ]), name="inputnode") modelspec = pe.Node(interface=model.SpecifySPMModel(), name="modelspec") if high_pass_filter_cutoff: modelspec.inputs.high_pass_filter_cutoff = high_pass_filter_cutoff create_subject_info = pe.Node(interface=util.Function( input_names=['conditions', 'onsets', 'durations'], output_names=['subject_info'], function=create_subject_inf), name="create_subject_info") modelspec.inputs.concatenate_runs = True #modelspec.inputs.input_units = units modelspec.inputs.output_units = "secs" #modelspec.inputs.time_repetition = tr #modelspec.inputs.subject_info = subjectinfo model_pipeline = pe.Workflow(name=name) model_pipeline.connect([ (inputnode, create_subject_info, [('conditions', 'conditions'), ('onsets', 'onsets'), ('durations', 'durations')]), (inputnode, modelspec, [('realignment_parameters', 'realignment_parameters'), ('functional_runs', 'functional_runs'), ('outlier_files', 'outlier_files'), ('units', 'input_units'), ('TR', 'time_repetition')]), (create_subject_info, modelspec, [('subject_info', 'subject_info')]), ]) if nipy: model_estimate = pe.Node(interface=FitGLM(), name="level1estimate") model_estimate.inputs.TR = tr model_estimate.inputs.normalize_design_matrix = True model_estimate.inputs.save_residuals = save_residuals if ar1: model_estimate.inputs.model = "ar1" model_estimate.inputs.method = "kalman" else: model_estimate.inputs.model = "spherical" model_estimate.inputs.method = "ols" model_pipeline.connect([ (modelspec, model_estimate, [('session_info', 'session_info')]), (inputnode, model_estimate, [('mask', 'mask')]) ]) if contrasts: contrast_estimate = pe.Node(interface=EstimateContrast(), name="contrastestimate") contrast_estimate.inputs.contrasts = contrasts model_pipeline.connect([ (model_estimate, contrast_estimate, [("beta", "beta"), ("nvbeta", "nvbeta"), ("s2", "s2"), ("dof", "dof"), ("axis", "axis"), ("constants", "constants"), ("reg_names", "reg_names")]), (inputnode, contrast_estimate, [('mask', 'mask')]), ]) else: level1design = pe.Node(interface=spm.Level1Design(), name="level1design") level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}} if ar1: level1design.inputs.model_serial_correlations = "AR(1)" else: level1design.inputs.model_serial_correlations = "none" level1design.inputs.timing_units = modelspec.inputs.output_units #level1design.inputs.interscan_interval = modelspec.inputs.time_repetition # if sparse: # level1design.inputs.microtime_resolution = n_slices*2 # else: # level1design.inputs.microtime_resolution = n_slices #level1design.inputs.microtime_onset = ref_slice microtime_resolution = pe.Node(interface=util.Function( input_names=['volume', 'sparse'], output_names=['microtime_resolution'], function=_get_microtime_resolution), name="microtime_resolution") level1estimate = pe.Node(interface=spm.EstimateModel(), name="level1estimate") level1estimate.inputs.estimation_method = {'Classical': 1} contrastestimate = pe.Node(interface=spm.EstimateContrast(), name="contrastestimate") #contrastestimate.inputs.contrasts = contrasts threshold = pe.MapNode(interface=spm.Threshold(), name="threshold", iterfield=['contrast_index', 'stat_image']) #threshold.inputs.contrast_index = range(1,len(contrasts)+1) threshold_topo_ggmm = neuroutils.CreateTopoFDRwithGGMM( "threshold_topo_ggmm") #threshold_topo_ggmm.inputs.inputnode.contrast_index = range(1,len(contrasts)+1) model_pipeline.connect([ (modelspec, level1design, [('session_info', 'session_info')]), (inputnode, level1design, [('mask', 'mask_image'), ('TR', 'interscan_interval'), (("functional_runs", get_ref_slice), "microtime_onset")]), (inputnode, microtime_resolution, [("functional_runs", "volume"), ("sparse", "sparse")]), (microtime_resolution, level1design, [("microtime_resolution", "microtime_resolution")]), (level1design, level1estimate, [('spm_mat_file', 'spm_mat_file')]), (inputnode, contrastestimate, [('contrasts', 'contrasts')]), (level1estimate, contrastestimate, [('spm_mat_file', 'spm_mat_file'), ('beta_images', 'beta_images'), ('residual_image', 'residual_image')]), (contrastestimate, threshold, [('spm_mat_file', 'spm_mat_file'), ('spmT_images', 'stat_image')]), (inputnode, threshold, [(('contrasts', _get_contrast_index), 'contrast_index')]), (level1estimate, threshold_topo_ggmm, [('mask_image', 'inputnode.mask_file')]), (contrastestimate, threshold_topo_ggmm, [('spm_mat_file', 'inputnode.spm_mat_file'), ('spmT_images', 'inputnode.stat_image')]), (inputnode, threshold_topo_ggmm, [(('contrasts', _get_contrast_index), 'inputnode.contrast_index') ]), ]) return model_pipeline
def create_2lvl(do_one_sample, name="group", mask=None): import nipype.interfaces.fsl as fsl import nipype.interfaces.spm as spm import nipype.pipeline.engine as pe import nipype.interfaces.utility as niu wk = pe.Workflow(name=name) inputspec = pe.Node(niu.IdentityInterface(fields=[ 'copes', 'estimation_method', 'template', "contrasts", "include_intercept", "regressors", "p_thresh", "height_thresh", 'min_cluster_size' ]), name='inputspec') if do_one_sample: model = pe.Node(spm.OneSampleTTestDesign(), name='onesample') else: model = pe.Node(spm.MultipleRegressionDesign(), name='l2model') wk.connect(inputspec, 'regressors', model, "user_covariates") wk.connect(inputspec, 'include_intercept', model, 'include_intercept') est_model = pe.Node(spm.EstimateModel(), name='estimate_model') wk.connect(inputspec, 'copes', model, 'in_files') wk.connect(inputspec, 'estimation_method', est_model, 'estimation_method') wk.connect(model, 'spm_mat_file', est_model, 'spm_mat_file') if mask == None: bet = pe.Node(fsl.BET(mask=True, frac=0.3, output_type='NIFTI'), name="template_brainmask") wk.connect(inputspec, 'template', bet, 'in_file') wk.connect(bet, 'mask_file', model, 'explicit_mask_file') else: wk.connect(inputspec, 'template', model, 'explicit_mask_file') est_cont = pe.Node(spm.EstimateContrast(group_contrast=True), name='estimate_contrast') wk.connect(inputspec, 'contrasts', est_cont, "contrasts") wk.connect(est_model, 'spm_mat_file', est_cont, "spm_mat_file") wk.connect(est_model, 'residual_image', est_cont, "residual_image") wk.connect(est_model, 'beta_images', est_cont, "beta_images") thresh = pe.MapNode(spm.Threshold(use_fwe_correction=False, use_topo_fdr=True, height_threshold_type='p-value'), name='fdr', iterfield=['stat_image', 'contrast_index']) wk.connect(est_cont, 'spm_mat_file', thresh, 'spm_mat_file') wk.connect(est_cont, 'spmT_images', thresh, 'stat_image') wk.connect(inputspec, 'min_cluster_size', thresh, 'extent_threshold') count = lambda x: range(1, len(x) + 1) wk.connect(inputspec, ('contrasts', count), thresh, 'contrast_index') wk.connect(inputspec, 'p_thresh', thresh, 'extent_fdr_p_threshold') wk.connect(inputspec, 'height_thresh', thresh, 'height_threshold') outputspec = pe.Node(niu.IdentityInterface(fields=[ 'RPVimage', 'beta_images', 'mask_image', 'residual_image', 'con_images', 'ess_images', 'spmF_images', 'spmT_images', 'spm_mat_file', 'pre_topo_fdr_map', 'thresholded_map' ]), name='outputspec') wk.connect(est_model, 'RPVimage', outputspec, 'RPVimage') wk.connect(est_model, 'beta_images', outputspec, 'beta_images') wk.connect(est_model, 'mask_image', outputspec, 'mask_image') wk.connect(est_model, 'residual_image', outputspec, 'residual_image') wk.connect(est_cont, 'con_images', outputspec, 'con_images') wk.connect(est_cont, 'ess_images', outputspec, 'ess_images') wk.connect(est_cont, 'spmF_images', outputspec, 'spmF_images') wk.connect(est_cont, 'spmT_images', outputspec, 'spmT_images') wk.connect(est_cont, 'spm_mat_file', outputspec, 'spm_mat_file') wk.connect(thresh, 'pre_topo_fdr_map', outputspec, 'pre_topo_fdr_map') wk.connect(thresh, 'thresholded_map', outputspec, 'thresholded_map') return wk
def build_pipeline(model_def): # create pointers to needed values from # the model dictionary # TODO - this could be refactored TR = model_def['TR'] subject_list = model_def['subject_list'] JSON_MODEL_FILE = model_def['model_path'] working_dir = model_def['working_dir'] output_dir = model_def['output_dir'] SUBJ_DIR = model_def['SUBJ_DIR'] PROJECT_DIR = model_def['PROJECT_DIR'] TASK_NAME = model_def['TaskName'] RUNS = model_def['Runs'] MODEL_NAME = model_def['ModelName'] PROJECT_NAME = model_def['ProjectID'] BASE_DIR = model_def['BaseDirectory'] SERIAL_CORRELATIONS = "AR(1)" if not model_def.get( 'SerialCorrelations') else model_def.get('SerialCorrelations') RESIDUALS = model_def.get('GenerateResiduals') # SpecifyModel - Generates SPM-specific Model modelspec = pe.Node(model.SpecifySPMModel(concatenate_runs=False, input_units='secs', output_units='secs', time_repetition=TR, high_pass_filter_cutoff=128), output_units='scans', name="modelspec") # #### Level 1 Design node # # ** TODO -- get the right matching template file for fmriprep ** # # * ??do we need a different mask than: # # `'/data00/tools/spm8/apriori/brainmask_th25.nii'` # Level1Design - Generates an SPM design matrix level1design = pe.Node( spm.Level1Design( bases={'hrf': { 'derivs': [0, 0] }}, timing_units='secs', interscan_interval=TR, # model_serial_correlations='AR(1)', # [none|AR(1)|FAST]', # 8/21/20 mbod - allow for value to be set in JSON model spec model_serial_correlations=SERIAL_CORRELATIONS, # TODO - allow for specified masks mask_image=BRAIN_MASK_PATH, global_intensity_normalization='none'), name="level1design") # #### Estimate Model node # EstimateModel - estimate the parameters of the model level1estimate = pe.Node( spm.EstimateModel( estimation_method={'Classical': 1}, # 8/21/20 mbod - allow for value to be set in JSON model spec write_residuals=RESIDUALS), name="level1estimate") # #### Estimate Contrasts node # EstimateContrast - estimates contrasts conestimate = pe.Node(spm.EstimateContrast(), name="conestimate") # ## Setup pipeline workflow for level 1 model # Initiation of the 1st-level analysis workflow l1analysis = pe.Workflow(name='l1analysis') # Connect up the 1st-level analysis components l1analysis.connect([ (modelspec, level1design, [('session_info', 'session_info')]), (level1design, level1estimate, [('spm_mat_file', 'spm_mat_file')]), (level1estimate, conestimate, [('spm_mat_file', 'spm_mat_file'), ('beta_images', 'beta_images'), ('residual_image', 'residual_image')]) ]) # ## Set up nodes for file handling and subject selection # ### `getsubjectinfo` node # # * Use `get_subject_info()` function to generate spec data structure for first level model design matrix # Get Subject Info - get subject specific condition information getsubjectinfo = pe.Node(util.Function( input_names=['subject_id', 'model_path'], output_names=['subject_info', 'realign_params', 'condition_names'], function=get_subject_info), name='getsubjectinfo') makecontrasts = pe.Node(util.Function( input_names=['subject_id', 'condition_names', 'model_path'], output_names=['contrasts'], function=make_contrast_list), name='makecontrasts') if model_def.get('ExcludeDummyScans'): ExcludeDummyScans = model_def['ExcludeDummyScans'] else: ExcludeDummyScans = 0 #if DEBUG: # print(f'Excluding {ExcludeDummyScans} dummy scans.') trimdummyscans = pe.MapNode(Trim(begin_index=ExcludeDummyScans), name='trimdummyscans', iterfield=['in_file']) # ### `infosource` node # # * iterate over list of subject ids and generate subject ids and produce list of contrasts for subsequent nodes # Infosource - a function free node to iterate over the list of subject names infosource = pe.Node(util.IdentityInterface( fields=['subject_id', 'model_path', 'resolution', 'smoothing']), name="infosource") try: fwhm_list = model_def['smoothing_list'] except: fwhm_list = [4, 6, 8] try: resolution_list = model_def['resolutions'] except: resolution_list = ['low', 'medium', 'high'] infosource.iterables = [ ('subject_id', subject_list), ('model_path', [JSON_MODEL_FILE] * len(subject_list)), ('resolution', resolution_list), ('smoothing', ['fwhm_{}'.format(s) for s in fwhm_list]) ] # SelectFiles - to grab the data (alternativ to DataGrabber) ## TODO: here need to figure out how to incorporate the run number and task name in call templates = { 'func': '{subject_id}/{resolution}/{smoothing}/sr{subject_id}_task-' + TASK_NAME + '_run-0*_*MNI*preproc*.nii' } selectfiles = pe.Node(nio.SelectFiles( templates, base_directory='{}/{}/derivatives/nipype/resampled_and_smoothed'. format(BASE_DIR, PROJECT_NAME)), working_dir=working_dir, name="selectfiles") # ### Specify datasink node # # * copy files to keep from various working folders to output folder for model for subject # Datasink - creates output folder for important outputs datasink = pe.Node( nio.DataSink( base_directory=SUBJ_DIR, parameterization=True, #container=output_dir ), name="datasink") datasink.inputs.base_directory = output_dir # Use the following DataSink output substitutions substitutions = [] subjFolders = [( '_model_path.*resolution_(low|medium|high)_smoothing_(fwhm_\\d{1,2})_subject_id_sub-.*/(.*)$', '\\1/\\2/\\3')] substitutions.extend(subjFolders) datasink.inputs.regexp_substitutions = substitutions # datasink connections datasink_in_outs = [('conestimate.spm_mat_file', '@spm'), ('level1estimate.beta_images', '@betas'), ('level1estimate.mask_image', '@mask'), ('conestimate.spmT_images', '@spmT'), ('conestimate.con_images', '@con'), ('conestimate.spmF_images', '@spmF')] if model_def.get('GenerateResiduals'): datasink_in_outs.append( ('level1estimate.residual_images', '@residuals')) # --------- # ## Set up workflow for whole process pipeline = pe.Workflow( name='first_level_model_{}_{}'.format(TASK_NAME.upper(), MODEL_NAME)) pipeline.base_dir = os.path.join(SUBJ_DIR, working_dir) pipeline.connect([ (infosource, selectfiles, [('subject_id', 'subject_id'), ('resolution', 'resolution'), ('smoothing', 'smoothing')]), (infosource, getsubjectinfo, [('subject_id', 'subject_id'), ('model_path', 'model_path')]), (infosource, makecontrasts, [('subject_id', 'subject_id'), ('model_path', 'model_path')]), (getsubjectinfo, makecontrasts, [('condition_names', 'condition_names') ]), (getsubjectinfo, l1analysis, [('subject_info', 'modelspec.subject_info'), ('realign_params', 'modelspec.realignment_parameters')]), (makecontrasts, l1analysis, [('contrasts', 'conestimate.contrasts')]), # (selectfiles, l1analysis, [('func', # 'modelspec.functional_runs')]), (selectfiles, trimdummyscans, [('func', 'in_file')]), (trimdummyscans, l1analysis, [('out_file', 'modelspec.functional_runs') ]), (infosource, datasink, [('subject_id', 'container')]), (l1analysis, datasink, datasink_in_outs) ]) return pipeline