pyhrf.jde.models.ARN_BiG_BOLDSamplerInput(data, dt, typeLFD, paramLFD, hrfZc, hrfDuration)¶Bases: pyhrf.jde.models.BOLDSamplerInput
cleanPrecalculations()¶makePrecalculations()¶pyhrf.jde.models.BOLDGibbsSampler(nb_iterations=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=<pyhrf.jde.nrl.bigaussian.NRLSampler object>, beta=<pyhrf.jde.beta.BetaSampler object>, noise_var=<pyhrf.jde.noise.NoiseVarianceSampler object>, hrf=<pyhrf.jde.hrf.HRFSampler 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>, 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¶getGlobalOutputs()¶initGlobalObservables()¶inputClass¶alias of WN_BiG_BOLDSamplerInput
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_iterations', 'response_levels', 'hrf', 'hrf_var']¶saveGlobalObservables(it)¶stop_criterion(it)¶updateGlobalObservables()¶pyhrf.jde.models.BOLDGibbsSampler_AR(nb_iterations=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=<pyhrf.jde.nrl.ar.NRLARSampler object>, beta=<pyhrf.jde.beta.BetaSampler object>, noise_var=<pyhrf.jde.noise.NoiseVarianceARSampler object>, noise_arp=<pyhrf.jde.noise.NoiseARParamsSampler object>, hrf=<pyhrf.jde.hrf.HRFARSampler 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.drift.DriftARSampler object>, drift_var=<pyhrf.jde.drift.ETASampler 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¶getGlobalOutputs()¶initGlobalObservables()¶inputClass¶alias of ARN_BiG_BOLDSamplerInput
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_iterations', 'response_levels', 'hrf', 'hrf_var']¶saveGlobalObservables(it)¶stop_criterion(it)¶updateGlobalObservables()¶pyhrf.jde.models.BOLDSamplerInput(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})
buildCosMat(paramLFD, ny)¶buildOtherMatX()¶buildParadigmConvolMatrix(zc, estimDuration, availableDataIndex, parData)¶buildParadigmSingleCondMatrix(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.models.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.models.CallbackCritDiff¶Bases: pyhrf.jde.samplerbase.GSDefaultCallbackHandler
callback(it, variables, samplerEngine)¶Execute action to be made after each Gibbs Sampling step (here : nothing). Should be overriden to define more specialized actions. @param it: the number of iterations elapsed in the current sampling process. @param samplerEngine: the parent gibbs sampler object @param vars: variables envolved in the sampling process (list of C{GibbsSamplerVariable} whose index is defined in L{samplerEngine})
pyhrf.jde.models.Drift_BOLDGibbsSampler(nb_iterations=3000, obs_hist_pace=-1, glob_obs_hist_pace=-1, smpl_hist_pace=-1, burnin=0.3, callback=<pyhrf.jde.samplerbase.GSDefaultCallbackHandler object>, response_levels=<pyhrf.jde.nrl.bigaussian_drift.NRL_Drift_Sampler object>, beta=<pyhrf.jde.beta.BetaSampler object>, noise_var=<pyhrf.jde.noise.NoiseVariance_Drift_Sampler object>, hrf=<pyhrf.jde.hrf.HRF_Drift_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.drift.DriftSampler object>, drift_var=<pyhrf.jde.drift.ETASampler object>, stop_crit_threshold=-1, stop_crit_from_start=False, check_final_value=None)¶Bases: pyhrf.xmlio.Initable, pyhrf.jde.samplerbase.GibbsSampler
computeFit()¶default_nb_its = 3000¶inputClass¶alias of WN_BiG_Drift_BOLDSamplerInput
parametersToShow = ['nb_iterations', 'response_levels', 'hrf', 'hrf_var']¶pyhrf.jde.models.Hab_WN_BiG_BOLDSamplerInput(data, dt, typeLFD, paramLFD, hrfZc, hrfDuration)¶Bases: pyhrf.jde.models.WN_BiG_BOLDSamplerInput
cleanPrecalculations()¶makePrecalculations()¶pyhrf.jde.models.WN_BiG_BOLDSamplerInput(data, dt, typeLFD, paramLFD, hrfZc, hrfDuration)¶Bases: pyhrf.jde.models.BOLDSamplerInput
cleanPrecalculations()¶makePrecalculations()¶pyhrf.jde.models.WN_BiG_Drift_BOLDSamplerInput(data, dt, typeLFD, paramLFD, hrfZc, hrfDuration)¶Bases: pyhrf.jde.models.BOLDSamplerInput
cleanPrecalculations()¶makePrecalculations()¶pyhrf.jde.models.W_BOLDGibbsSampler(nb_iterations=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=<pyhrf.jde.nrl.bigaussian.NRLSamplerWithRelVar object>, beta=<pyhrf.jde.beta.BetaSampler object>, noise_var=<pyhrf.jde.noise.NoiseVarianceSampler object>, hrf=<pyhrf.jde.hrf.HRFSamplerWithRelVar object>, hrf_var=<pyhrf.jde.hrf.RHSampler object>, mixt_weights=<pyhrf.jde.nrl.bigaussian.MixtureWeightsSampler object>, mixt_params=<pyhrf.jde.nrl.bigaussian.BiGaussMixtureParamsSamplerWithRelVar object>, scale=<pyhrf.jde.hrf.ScaleSampler object>, relevantVariable=<pyhrf.jde.wsampler.WSampler object>, stop_crit_threshold=-1, stop_crit_from_start=False, check_final_value=None)¶Bases: pyhrf.xmlio.Initable, pyhrf.jde.samplerbase.GibbsSampler
default_nb_its = 3000¶inputClass¶alias of WN_BiG_BOLDSamplerInput
parametersToShow = ['nb_iterations', 'response_levels', 'hrf', 'hrf_var']¶pyhrf.jde.models.W_Drift_BOLDGibbsSampler(nb_iterations=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=<pyhrf.jde.nrl.bigaussian_drift.NRL_Drift_SamplerWithRelVar object>, beta=<pyhrf.jde.beta.BetaSampler object>, noise_var=<pyhrf.jde.noise.NoiseVariance_Drift_Sampler object>, hrf=<pyhrf.jde.hrf.HRF_Drift_SamplerWithRelVar object>, hrf_var=<pyhrf.jde.hrf.RHSampler object>, mixt_weights=<pyhrf.jde.nrl.bigaussian.MixtureWeightsSampler object>, mixt_params=<pyhrf.jde.nrl.bigaussian.BiGaussMixtureParamsSamplerWithRelVar object>, scale=<pyhrf.jde.hrf.ScaleSampler object>, condion_relevance=<pyhrf.jde.wsampler.W_Drift_Sampler object>, drift=<pyhrf.jde.drift.DriftSamplerWithRelVar object>, drift_var=<pyhrf.jde.drift.ETASampler object>, stop_crit_threshold=-1, stop_crit_from_start=False, check_final_value=None)¶Bases: pyhrf.xmlio.Initable, pyhrf.jde.samplerbase.GibbsSampler
default_nb_its = 3000¶inputClass¶alias of WN_BiG_Drift_BOLDSamplerInput
parametersToShow = ['nb_iterations', 'response_levels', 'hrf', 'hrf_var']¶pyhrf.jde.models.computePl(drift, varP, dest=None)¶pyhrf.jde.models.computeSumjaXh(nrl, matXh, dest=None)¶pyhrf.jde.models.computeXh(hrf, varX, dest=None)¶pyhrf.jde.models.computeYBar(varMBY, varPl, dest=None)¶pyhrf.jde.models.computeYTilde(sumj_aXh, varMBY, dest=None)¶pyhrf.jde.models.computeYTilde_Pl(sumj_aXh, yBar, dest=None)¶pyhrf.jde.models.computehXQXh(hrf, matXQX, dest=None)¶pyhrf.jde.models.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.models.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.models.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.models.simulate_bold(output_dir=None, noise_scenario='high_snr', spatial_size='tiny', normalize_hrf=True)¶