################################################## # Review from pyitab.preprocessing.pipelines import PreprocessingPipeline from pyitab.analysis.iterator import AnalysisIterator from pyitab.analysis.configurator import AnalysisConfigurator loader = DataLoader( configuration_file="/home/carlos/fmri/carlo_ofp/ofp_new.conf", task='OFP_NORES') prepro = PreprocessingPipeline(nodes=[ #Transformer(), Detrender(), Detrender(chunks_attr='file'), SampleZNormalizer(), FeatureZNormalizer(), ]) ds = loader.fetch(prepro=prepro) _default_options = { 'kwargs__roi': [['within_conjunction']], #'sample_slicer__subject': [[s] for s in], 'sample_slicer__evidence': [[1], [2], [3]], } _default_config = { 'prepro': ['target_transformer', 'sample_slicer', 'balancer'], 'target_transformer__attr': 'decision', 'sample_slicer__decision': ['L', 'F'], 'balancer__attr': 'subject',
bids_atlas="complete", bids_correction="corr", bids_derivatives='True', load_fx='hcp-blp') ds = loader.fetch() nodes = ds.fa.nodes_1 matrix = np.zeros_like(ds.samples[0]) nanmask = np.logical_not(np.isnan(ds.samples).sum(0)) ds = ds[:, nanmask] prepro = [ SampleSlicer(task=['rest', 'task1', 'task2', 'task4', 'task5']), FeatureZNormalizer(), SampleAttributeTransformer(attr='dexterity1', fx=('zscore', zscore)), SampleAttributeTransformer(attr='dexterity2', fx=('zscore', zscore)), ] ds = PreprocessingPipeline(nodes=prepro).transform(ds) bands = ['alpha', 'betahigh', 'betalow'] tasks = ['rest', 'task1', 'task2', 'task4', 'task5'] dataframe = dict() for b, t in product(bands, tasks): prepro = [SampleSlicer(task=[t], band=[b]), FeatureZNormalizer()] ds_ = PreprocessingPipeline(nodes=prepro).transform(ds)
bids_derivatives='True', load_fx='hcp-blp') ds = loader.fetch() nodes = ds.fa.nodes_1 matrix = np.zeros_like(ds.samples[0]) nanmask = np.logical_not(np.isnan(ds.samples).sum(0)) ds = ds[:, nanmask] # 1. Transform dataset to have mean 0 and std 1 prepro = [ FeatureZNormalizer(), SampleAttributeTransformer(attr='dexterity1', fx=('zscore', zscore)), SampleAttributeTransformer(attr='dexterity2', fx=('zscore', zscore)), ] ds = PreprocessingPipeline(nodes=prepro).transform(ds) formulas = [ 'task + dexterity1 - 1', 'task + dexterity2 - 1', 'task - 1', 'dexterity1 - 1', 'dexterity2 - 1', ]
roi_labels = { os.path.basename(fname).split('_')[0]: fname for fname in roi_labels_fname } loader = DataLoader(configuration_file=conf_file, event_file='residuals_attributes_full', roi_labels=roi_labels, task='RESIDUALS_MVPA') prepro = PreprocessingPipeline(nodes=[ #Transformer(), #Detrender(attr='file'), Detrender(attr='chunks'), SampleZNormalizer(), FeatureZNormalizer(), SampleSlicer(frame=[1, 2, 3, 4, 5, 6, 7]), #TargetTransformer(attr='decision'), MemoryReducer(dtype=np.float16), #Balancer(attr='frame'), ]) ds = loader.fetch(prepro=prepro, n_subjects=8) ds = MemoryReducer(dtype=np.float16).transform(ds) labels = list(roi_labels.keys())[:-1] import sentry_sdk sentry_sdk.init("https://[email protected]/1439199")