pyhrf.ui.vb_jde_analyser module

class 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

analyse_roi(roiData)

parametersComments = {'estimateDrifts': 'Explicit drift estimation (if False then drifts are marginalized', 'hrfDuration': 'duration of the HRF in seconds', 'nbClasses': 'number of classes for the response levels', 'constrained': 'adding constrains: positivity and norm = 1 ', 'InitVar': 'Initiale value of active and inactive gaussian variances', 'scale': 'flag for the scaling factor applied to the data fidelity term during m_h step.\nIf scale=False then do nothing, else divide the data fidelity term by the number of voxels', 'hyper_prior_sigma_H': 'Parameter of the hyper-prior on sigma_H (if zero, no prior is applied)', 'fast': 'running fast VEM with C extensions', 'estimateBeta': 'estimate or not the Potts spatial regularization parameter', 'estimateLabels': 'estimate or not the Labels', 'TrueHrfFlag': 'If True, HRF will be fixed to the simulated value', 'InitMean': 'Initiale value of active gaussian means', 'beta': 'initial value of spatial Potts regularization parameter', 'nItMax': 'maximum iteration number', 'HrfFilename': 'True HRF Filename', 'contrasts': 'Contrasts to be evaluated', 'MFapprox': 'Using of the Mean Field approximation in labels estimation', 'dt': 'time resolution of the estimated HRF in seconds', 'PLOT': 'plotting flag for convergence curves', 'estimateSigmaH': 'estimate or not the HRF variance', 'sigmaH': 'Initial HRF variance', 'LabelsFilename': 'True Labels Filename', 'estimateMixtParam': 'estimate or not the mixture parameters', 'simulation': 'indicates whether the run corresponds to a simulation example or not', 'estimateHRF': 'estimate or not the HRF', 'MiniVemFlag': 'Choosing, if True, the best initialisation of MixtParam and gamma_h', 'nItMin': 'minimum iteration number', 'NbItMiniVem': 'The number of iterations in Mini VEM algorithme'}

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)