pyhrf.jde.hrf.
HRFARSampler
(do_sampling=True, use_true_value=False, val_ini=None, duration=25.0, zero_constraint=True, normalise=1.0, deriv_order=2, covar_hack=False, prior_type='voxelwiseIID', do_voxelwise_outputs=False, compute_ah_online=False, output_ah=False)¶Bases: pyhrf.jde.hrf.HRFSampler
#THis class implements the sampling of the HRF when modelling a serially AR(1) noise process in the data. The structure of this noise is spatially varying in the sense that there is one AR parameter in combination with one noise variance per voxel.
computeStDS_StDY
(reps, noiseInvCov, nrls, varMBYPl)¶finalizeSampling
()¶linkToData
(dataInput)¶sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
pyhrf.jde.hrf.
HRFSampler
(do_sampling=True, use_true_value=False, val_ini=None, duration=25.0, zero_constraint=True, normalise=1.0, deriv_order=2, covar_hack=False, prior_type='voxelwiseIID', do_voxelwise_outputs=False, compute_ah_online=False, output_ah=False)¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
#TODO : HRF sampler for BiGaussian NLR mixture
calcXh
(hrf)¶checkAndSetInitValue
(variables)¶computeStDS_StDY
(rb, nrls, aa)¶detectSignError
()¶finalizeSampling
()¶getCurrentVar
()¶getFinalVar
()¶getOutputs
()¶getScaleFactor
()¶get_accuracy
(abs_error, rel_error, fv, tv, atol, rtol)¶Return the accuray of the estimate fv, compared to the true value tv
get_final_value
()¶Used to compare with simulated value
initObservables
()¶linkToData
(dataInput)¶parametersComments
= {'covar_hack': 'Divide the term coming from the likelihood by the nb of voxels\n when computing the posterior covariance. The aim is to balance\n the contribution coming from the prior with that coming from the likelihood.\n Note: this hack is only taken into account when "singleHRf" is used for "prior_type"', 'do_sampling': 'Flag for the HRF estimation (True or False).\nIf set to False then the HRF is fixed to a canonical form.', 'duration': 'HRF length in seconds', 'normalise': 'If 1. : Normalise samples of Hrf and NRLs when they are sampled.\nIf 0. : Normalise posterior means of Hrf and NRLs when they are sampled.\nelse : Do not normalise.', 'prior_type': 'Type of prior:\n - "singleHRF": one HRF modelled for the whole parcel ~N(0,v_h*R).\n - "voxelwiseIID": one HRF per voxel, all HRFs are iid ~N(0,v_h*R).', 'zero_constraint': 'If True: impose first and last value = 0.\nIf False: no constraint.'}¶parametersToShow
= ['do_sampling', 'duration', 'zero_constraint']¶reportCurrentVal
()¶sampleNextAlt
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated.
sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
samplingWarmUp
(variables)¶Called before the launch of the main sampling loop by the sampler engine. Should be overriden and perform precalculations.
setFinalValue
()¶updateNorm
()¶updateObsersables
()¶updateXh
()¶pyhrf.jde.hrf.
HRFSamplerWithRelVar
(do_sampling=True, use_true_value=False, val_ini=None, duration=25.0, zero_constraint=True, normalise=1.0, deriv_order=2, covar_hack=False, prior_type='voxelwiseIID', do_voxelwise_outputs=False, compute_ah_online=False, output_ah=False)¶Bases: pyhrf.jde.hrf.HRFSampler
This class introduce a new variable w (Relevant Variable) that takes its value in {0, 1} with :
computeStDS_StDY_WithRelVar
(rb, nrls, aa, w)¶finalizeSampling
()¶linkToData
(dataInput)¶sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
pyhrf.jde.hrf.
HRF_Drift_Sampler
(do_sampling=True, use_true_value=False, val_ini=None, duration=25.0, zero_constraint=True, normalise=1.0, deriv_order=2, covar_hack=False, prior_type='voxelwiseIID', do_voxelwise_outputs=False, compute_ah_online=False, output_ah=False)¶Bases: pyhrf.jde.hrf.HRFSampler
Class handling the Gibbs sampling of Neural Response Levels in the case of joint drift sampling.
computeStDS_StDY
(rb, nrls, aa)¶pyhrf.jde.hrf.
HRF_Drift_SamplerWithRelVar
(do_sampling=True, use_true_value=False, val_ini=None, duration=25.0, zero_constraint=True, normalise=1.0, deriv_order=2, covar_hack=False, prior_type='voxelwiseIID', do_voxelwise_outputs=False, compute_ah_online=False, output_ah=False)¶Bases: pyhrf.jde.hrf.HRFSamplerWithRelVar
Class handling the Gibbs sampling of Neural Response Levels in the case of joint drift sampling.
computeStDS_StDY_WithRelVar
(rb, nrls, aa, w)¶pyhrf.jde.hrf.
HRF_two_parts_Sampler
(do_sampling=True, use_true_value=False, val_ini=None, duration=25.0, zero_constraint=True, normalise=1.0, deriv_order=2, covar_hack=False, prior_type='voxelwiseIID', do_voxelwise_outputs=False, compute_ah_online=False, output_ah=False)¶Bases: pyhrf.jde.hrf.HRFSampler
calcXh
(hrf)¶checkAndSetInitValue
(variables)¶computeStDS_StDY
(rb, nrls, aa)¶detectSignError
()¶finalizeSampling
()¶getCurrentVar
()¶getFinalVar
()¶getOutputs
()¶getScaleFactor
()¶initObservables
()¶linkToData
(dataInput)¶reportCurrentVal
()¶sampleNextAlt
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated.
sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
samplingWarmUp
(variables)¶Called before the launch of the main sampling loop by the sampler engine. Should be overriden and perform precalculations.
setFinalValue
()¶updateNorm
()¶updateObsersables
()¶updateXh
()¶pyhrf.jde.hrf.
HRFwithHabSampler
(do_sampling=True, use_true_value=False, val_ini=None, duration=25.0, zero_constraint=True, normalise=1.0, deriv_order=2, covar_hack=False, prior_type='voxelwiseIID', do_voxelwise_outputs=False, compute_ah_online=False, output_ah=False)¶Bases: pyhrf.jde.hrf.HRFSampler
computeStDS_StDY
(rb, sumaX, Q)¶finalizeSampling
()¶getScaleFactor
()¶linkToData
(dataInput)¶sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
updateNorm
()¶pyhrf.jde.hrf.
RHSampler
(do_sampling=True, use_true_value=False, val_ini=array([ 0.1]), prior_mean=0.001, prior_var=10)¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
#TODO : comment
checkAndSetInitValue
(variables)¶getOutputs
()¶get_final_value
()¶linkToData
(dataInput)¶parametersToShow
= ['do_sampling', 'val_ini']¶sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
pyhrf.jde.hrf.
ScaleSampler
(do_sampling=False, use_true_value=False, val_ini=array([ 1.]))¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
getOutputs
()¶linkToData
(dataInput)¶sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
pyhrf.jde.hrf.
buildDiagGaussianMat
(size, width)¶pyhrf.jde.hrf.
msqrt
(cov)¶sig = msqrt(cov)
Return a matrix square root of a covariance matrix. Tries Cholesky factorization first, and factorizes by diagonalization if that fails.
pyhrf.jde.hrf.
sampleHRF_single_hrf
(stLambdaS, stLambdaY, varR, rh, nbColX, nbVox)¶pyhrf.jde.hrf.
sampleHRF_single_hrf_hack
(stLambdaS, stLambdaY, varR, rh, nbColX, nbVox)¶pyhrf.jde.hrf.
sampleHRF_voxelwise_iid
(stLambdaS, stLambdaY, varR, rh, nbColX, nbVox)¶