pyhrf.jde.jde_multi_sess.
BOLDGibbs_Multi_SessSampler
(nb_its=3000, obs_hist_pace=-1.0, glob_obs_hist_pace=-1, smpl_hist_pace=-1.0, burnin=0.3, callback=<pyhrf.jde.samplerbase.GSDefaultCallbackHandler object>, response_levels_sess=<pyhrf.jde.jde_multi_sess.NRL_Multi_Sess_Sampler object>, response_levels_mean=<pyhrf.jde.jde_multi_sess.NRLsBar_Drift_Multi_Sess_Sampler object>, beta=<pyhrf.jde.beta.BetaSampler object>, noise_var=<pyhrf.jde.jde_multi_sess.NoiseVariance_Drift_Multi_Sess_Sampler object>, hrf=<pyhrf.jde.jde_multi_sess.HRF_MultiSess_Sampler object>, hrf_var=<pyhrf.jde.hrf.RHSampler object>, mixt_weights=<pyhrf.jde.nrl.bigaussian.MixtureWeightsSampler object>, mixt_params=<pyhrf.jde.nrl.bigaussian.BiGaussMixtureParamsSampler object>, scale=<pyhrf.jde.hrf.ScaleSampler object>, drift=<pyhrf.jde.jde_multi_sess.Drift_MultiSess_Sampler object>, drift_var=<pyhrf.jde.jde_multi_sess.ETASampler_MultiSess object>, stop_crit_threshold=-1, stop_crit_from_start=False, check_final_value=None)¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSampler
cleanObservables
()¶computeFit
()¶computePMStimInducedSignal
()¶compute_crit_diff
(old_vals, means=None)¶default_nb_its
= 3000¶finalizeSampling
()¶getGlobalOutputs
()¶initGlobalObservables
()¶inputClass
¶alias of BOLDSampler_Multi_SessInput
parametersComments
= {'obs_hist_pace': 'See comment for samplesHistoryPaceSave.', 'smpl_hist_pace': 'To save the samples at each iteration\nIf x<0: no save\n If 0<x<1: define the fraction of iterations for which samples are saved\nIf x>=1: define the step in iterations number between saved samples.\nIf x=1: save samples at each iteration.'}¶parametersToShow
= ['nb_its', 'response_levels_sess', 'response_levels_mean', 'hrf', 'hrf_var']¶saveGlobalObservables
(it)¶stop_criterion
(it)¶updateGlobalObservables
()¶pyhrf.jde.jde_multi_sess.
BOLDSampler_Multi_SessInput
(data, dt, typeLFD, paramLFD, hrfZc, hrfDuration)¶Class holding data needed by the sampler : BOLD time courses for each voxel, onsets and voxel topology. It also perform some precalculation such as the convolution matrix based on the onsests (L{stackX}) —- Multi-sessions version
buildCosMat
(paramLFD, ny)¶buildOtherMatX
()¶buildParadigmConvolMatrix
(zc, estimDuration, availableDataIndex, parData)¶buildPolyMat
(paramLFD, n)¶calcDt
(dtMin)¶chewUpOnsets
(dt, hrfZc, hrfDuration)¶cleanMem
()¶cleanPrecalculations
()¶makePrecalculations
()¶setLFDMat
(paramLFD, typeLFD)¶Build the low frequency basis from polynomial basis functions.
pyhrf.jde.jde_multi_sess.
BiGaussMixtureParams_Multi_Sess_NRLsBar_Sampler
(do_sampling=True, use_true_value=False, val_ini=None, hyper_prior_type='Jeffreys', activ_thresh=4.0, var_ci_pr_alpha=2.04, var_ci_pr_beta=0.5, var_ca_pr_alpha=2.01, var_ca_pr_beta=0.5, mean_ca_pr_mean=5.0, mean_ca_pr_var=20.0)¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
I_MEAN_CA
= 0¶I_VAR_CA
= 1¶I_VAR_CI
= 2¶L_CA
= 1¶L_CI
= 0¶NB_PARAMS
= 3¶PARAMS_NAMES
= ['Mean_Activ', 'Var_Activ', 'Var_Inactiv']¶checkAndSetInitValue
(variables)¶computeWithJeffreyPriors
(j, cardCIj, cardCAj)¶computeWithProperPriors
(j, cardCIj, cardCAj)¶finalizeSampling
()¶getCurrentMeans
()¶getCurrentVars
()¶getOutputs
()¶get_string_value
(v)¶linkToData
(dataInput)¶parametersComments
= {'activ_thresh': 'Threshold for the max activ mean above which the region is considered activating', 'hyper_prior_type': "Either 'proper' or 'Jeffreys'"}¶parametersToShow
= []¶sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
updateObsersables
()¶pyhrf.jde.jde_multi_sess.
Drift_MultiSess_Sampler
(do_sampling=True, use_true_value=False, val_ini=None)¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
checkAndSetInitValue
(variables)¶getOutputs
()¶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
()¶get_true_value
()¶linkToData
(dataInput)¶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.
updateNorm
()¶pyhrf.jde.jde_multi_sess.
ETASampler_MultiSess
(do_sampling=True, use_true_value=False, val_ini=array([ 1.]))¶Bases: pyhrf.jde.drift.ETASampler
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.jde_multi_sess.
HRF_MultiSess_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)¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
HRF sampler for multisession model
calcXh
(hrf)¶checkAndSetInitValue
(variables)¶computeStDS_StDY
(rb_allSess, nrls_allSess, aa_allSess)¶computeStDS_StDY_from_HRFSampler
(rb, nrls, aa)¶just for comparison purpose. Should be removed in the end.
computeStDS_StDY_one_session
(rb, nrls, aa, sess)¶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
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.jde_multi_sess.
NRL_Multi_Sess_Sampler
(do_sampling=True, val_ini=None, use_true_value=False)¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
checkAndSetInitValue
(variables)¶cleanMemory
()¶computeAA
(nrls, destaa)¶computeComponentsApost
(s, m, varXh)¶computeVarYTildeSessionOpt
(varXh, s)¶finalizeSampling
()¶getOutputs
()¶get_accuracy
(abs_error, rel_error, fv, tv, atol, rtol)¶Return the accuray of the estimate fv, compared to the true value tv
is_accurate
()¶linkToData
(dataInput)¶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)¶#TODO : comment
saveCurrentValue
(it)¶pyhrf.jde.jde_multi_sess.
NRLsBar_Drift_Multi_Sess_Sampler
(do_sampling=True, val_ini=None, contrasts={}, do_label_sampling=True, use_true_nrls=False, use_true_labels=False, labels_ini=None, ppm_proba_threshold=0.05, ppm_value_threshold=0, ppm_value_multi_threshold=array([ 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2., 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. ]), mean_activation_threshold=4, rescale_results=False, wip_variance_computation=False)¶Bases: pyhrf.jde.nrl.bigaussian.NRLSampler
Class handling the Gibbs sampling of Neural Response Levels in the case of joint drift sampling.
checkAndSetInitValue
(variables)¶get_accuracy
(abs_error, rel_error, fv, tv, atol, rtol)¶Return the accuray of the estimate fv, compared to the true value tv
is_accurate
()¶linkToData
(dataInput)¶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.
sampleNrlsSerial
(varCI, varCA, meanCA, variables)¶samplingWarmUp
(variables)¶#TODO : comment
setFinalValue
()¶pyhrf.jde.jde_multi_sess.
NoiseVariance_Drift_Multi_Sess_Sampler
(do_sampling=True, use_true_value=False, val_ini=None)¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
checkAndSetInitValue
(variables)¶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.jde_multi_sess.
Variance_GaussianNRL_Multi_Sess
(do_sampling=True, use_true_value=False, val_ini=array([ 1.]))¶Bases: pyhrf.xmlio.Initable
, pyhrf.jde.samplerbase.GibbsSamplerVariable
checkAndSetInitValue
(variables)¶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.jde_multi_sess.
b
()¶pyhrf.jde.jde_multi_sess.
permutation
(x)¶Randomly permute a sequence, or return a permuted range.
If x is a multi-dimensional array, it is only shuffled along its first index.
Parameters: | x (int or array_like) – If x is an integer, randomly permute np.arange(x) .
If x is an array, make a copy and shuffle the elements
randomly. |
---|---|
Returns: | out – Permuted sequence or array range. |
Return type: | ndarray |
Examples
>>> np.random.permutation(10)
array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])
>>> np.random.permutation([1, 4, 9, 12, 15])
array([15, 1, 9, 4, 12])
>>> arr = np.arange(9).reshape((3, 3))
>>> np.random.permutation(arr)
array([[6, 7, 8],
[0, 1, 2],
[3, 4, 5]])
pyhrf.jde.jde_multi_sess.
rand
(d0, d1, ..., dn)¶Random values in a given shape.
Create an array of the given shape and populate it with
random samples from a uniform distribution
over [0, 1)
.
Parameters: | d1, .., dn (d0,) – The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned. |
---|---|
Returns: | out – Random values. |
Return type: | ndarray, shape (d0, d1, ..., dn) |
See also
random()
Notes
This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to np.random.random_sample .
Examples
>>> np.random.rand(3,2)
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #random
pyhrf.jde.jde_multi_sess.
randn
(d0, d1, ..., dn)¶Return a sample (or samples) from the “standard normal” distribution.
If positive, int_like or int-convertible arguments are provided,
randn generates an array of shape (d0, d1, ..., dn)
, filled
with random floats sampled from a univariate “normal” (Gaussian)
distribution of mean 0 and variance 1 (if any of the are
floats, they are first converted to integers by truncation). A single
float randomly sampled from the distribution is returned if no
argument is provided.
This is a convenience function. If you want an interface that takes a tuple as the first argument, use numpy.random.standard_normal instead.
Parameters: | d1, .., dn (d0,) – The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned. |
---|---|
Returns: | Z – A (d0, d1, ..., dn) -shaped array of floating-point samples from
the standard normal distribution, or a single such float if
no parameters were supplied. |
Return type: | ndarray or float |
See also
random.standard_normal()
Notes
For random samples from , use:
sigma * np.random.randn(...) + mu
Examples
>>> np.random.randn()
2.1923875335537315 #random
Two-by-four array of samples from N(3, 6.25):
>>> 2.5 * np.random.randn(2, 4) + 3
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random
pyhrf.jde.jde_multi_sess.
sampleHRF_single_hrf
(stLambdaS, stLambdaY, varR, rh, nbColX, nbVox)¶pyhrf.jde.jde_multi_sess.
sampleHRF_single_hrf_hack
(stLambdaS, stLambdaY, varR, rh, nbColX, nbVox)¶pyhrf.jde.jde_multi_sess.
sampleHRF_voxelwise_iid
(stLambdaS, stLambdaY, varR, rh, nbColX, nbVox, nbSess)¶pyhrf.jde.jde_multi_sess.
simulate_sessions
(output_dir, snr_scenario='high_snr', spatial_size='tiny')¶pyhrf.jde.jde_multi_sess.
simulate_single_session
(output_dir, var_sessions_nrls, cdefs, nrls_bar, labels, labels_vol, v_noise, drift_coeff_var, drift_amplitude)¶