pyhrf.ui.vb_jde_analyser.
JDEVEMAnalyser
(hrfDuration=25.0, sigmaH=0.1, fast=True, computeContrast=True, nbClasses=2, PLOT=False, nItMax=100, nItMin=1, scale=False, beta=1.0, estimateSigmaH=True, estimateHRF=True, TrueHrfFlag=False, HrfFilename='hrf.nii', estimateDrifts=True, hyper_prior_sigma_H=1000, dt=0.6, estimateBeta=True, contrasts=None, simulation=False, estimateLabels=True, LabelsFilename=None, MFapprox=False, estimateMixtParam=True, constrained=False, InitVar=0.5, InitMean=2.0, MiniVemFlag=False, NbItMiniVem=5, zero_constraint=True, output_drifts=False, drifts_type='poly')¶Bases: pyhrf.ui.jde.JDEAnalyser
Class that handles parcel-wise which is done according to the input data parcellation by default, and also takes care of merging parcel-specific outputs at the end of the JDE VEM analysis.
Parameters: |
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analyse_roi
(roiData)¶ROI analysis of the fMRI data.
Parameters: | roiData (FmriData ) – fMRI data to be analyzed. |
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Returns: | packed outputs. |
Return type: | dict of xndarray |
parametersComments
= {'HrfFilename': 'HRF filename', 'InitMean': 'initial value of active gaussian means', 'InitVar': 'initial value of active and inactive gaussian variances', 'LabelsFilename': 'labels filename', 'MFapprox': 'using of the Mean Field approximation in labels estimation', 'MiniVemFlag': 'if true, estimate the best initialisation of MixtParam and gamma_h', 'NbItMiniVem': 'number of iterations in Mini VEM algorithm', 'PLOT': 'plotting flag for convergence curves', 'TrueHrfFlag': 'if true, HRF will be fixed to the simulated value', 'beta': 'initial value of spatial Potts regularization parameter', 'constrained': 'adding constrains: positivity and norm = 1 ', 'contrasts': 'contrasts to be evaluated', 'drifts_type': 'type of the drift basis (default="polynomial")', 'dt': 'time resolution of the estimated HRF (in seconds)', 'estimateBeta': 'estimate or not the Potts spatial regularization parameter', 'estimateDrifts': 'explicit drift estimation (if false, drifts are marginalized', 'estimateHRF': 'estimate or not the HRF', 'estimateLabels': 'estimate or not the labels', 'estimateMixtParam': 'estimate or not the mixture parameters', 'estimateSigmaH': 'estimate or not the HRF variance', 'fast': 'running fast VEM with C extensions', 'hrfDuration': 'duration of the HRF (in seconds)', 'hyper_prior_sigma_H': 'parameter of the hyper-prior on sigma_H (if zero, no prior is applied)', 'nItMax': 'maximum number of iterations', 'nItMin': 'minimum number of iterations', 'nbClasses': 'number of classes for the response levels', 'scale': 'if true, the data fidelity term is divide by the number of voxels, otherwise it does nothing', 'sigmaH': 'initial HRF variance', 'simulation': 'indicates whether the run corresponds to a simulation example or not'}¶parametersToShow
= ['dt', 'hrfDuration', 'nItMax', 'nItMin', 'estimateSigmaH', 'estimateHRF', 'TrueHrfFlag', 'HrfFilename', 'estimateBeta', 'estimateLabels', 'LabelsFilename', 'MFapprox', 'estimateDrifts', 'estimateMixtParam', 'InitVar', 'InitMean', 'scale', 'nbClasses', 'fast', 'PLOT', 'sigmaH', 'contrasts', 'hyper_prior_sigma_H', 'constrained', 'simulation', 'MiniVemFlag', 'NbItMiniVem']¶pyhrf.ui.vb_jde_analyser.
change_dim
(labels)¶Change labels dimension from (ncond, nclass, nvox)
to (nclass, ncond, nvox)
.
pyhrf.ui.vb_jde_analyser.
run_analysis
(**params)¶Function to run the JDE VEM analyzer with parallel computation