pyhrf.jde.asl_physio_hierarchical module

class pyhrf.jde.asl_physio_hierarchical.ASLPhysioSampler(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>, bold_response_levels=<pyhrf.jde.asl_physio_hierarchical.BOLDResponseLevelSampler object>, perf_response_levels=<pyhrf.jde.asl_physio_hierarchical.PerfResponseLevelSampler object>, labels=<pyhrf.jde.asl_physio_hierarchical.LabelSampler object>, noise_var=<pyhrf.jde.asl_physio_hierarchical.NoiseVarianceSampler object>, truebrf=<pyhrf.jde.asl_physio_hierarchical.PhysioTrueBOLDResponseSampler object>, truebrf_var=<pyhrf.jde.asl_physio_hierarchical.PhysioTrueBOLDResponseVarianceSampler object>, brf=<pyhrf.jde.asl_physio_hierarchical.PhysioBOLDResponseSampler object>, brf_var=<pyhrf.jde.asl_physio_hierarchical.PhysioBOLDResponseVarianceSampler object>, prf=<pyhrf.jde.asl_physio_hierarchical.PhysioPerfResponseSampler object>, prf_var=<pyhrf.jde.asl_physio_hierarchical.PhysioPerfResponseVarianceSampler object>, bold_mixt_params=<pyhrf.jde.asl_physio_hierarchical.BOLDMixtureSampler object>, perf_mixt_params=<pyhrf.jde.asl_physio_hierarchical.PerfMixtureSampler object>, drift=<pyhrf.jde.asl_physio_hierarchical.DriftCoeffSampler object>, drift_var=<pyhrf.jde.asl_physio_hierarchical.DriftVarianceSampler object>, perf_baseline=<pyhrf.jde.asl_physio_hierarchical.PerfBaselineSampler object>, perf_baseline_var=<pyhrf.jde.asl_physio_hierarchical.PerfBaselineVarianceSampler object>, check_final_value=None)

Bases: pyhrf.xmlio.Initable, pyhrf.jde.samplerbase.GibbsSampler

computeFit()

default_nb_its = 3000

finalizeSampling()

getGlobalOutputs()

inputClass

alias of WN_BiG_ASLSamplerInput

parametersToShow = ['nb_its', 'response_levels', 'hrf', 'hrf_var']

class pyhrf.jde.asl_physio_hierarchical.BOLDMixtureSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.asl_physio_hierarchical.MixtureParamsSampler, pyhrf.xmlio.Initable

get_true_values_from_simulation_cdefs(cdefs)

class pyhrf.jde.asl_physio_hierarchical.BOLDResponseLevelSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.asl_physio_hierarchical.ResponseLevelSampler, pyhrf.xmlio.Initable

computeVarYTildeOpt(update_perf=False)

if update_perf is True then also update sumcXg and prl.ytilde update_perf should only be used at init of variable values.

getOutputs()

samplingWarmUp(v)

class pyhrf.jde.asl_physio_hierarchical.DriftCoeffSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

checkAndSetInitValue(variables)

compute_y_tilde()

getOutputs()

linkToData(dataInput)

sampleNextInternal(variables)

samplingWarmUp(v)

updateNorm()

class pyhrf.jde.asl_physio_hierarchical.DriftVarianceSampler(val_ini=array([ 1.]), do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

checkAndSetInitValue(variables)

linkToData(dataInput)

sampleNextInternal(variables)

class pyhrf.jde.asl_physio_hierarchical.LabelSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

CLASSES = array([0, 1])

CLASS_NAMES = ['inactiv', 'activ']

L_CA = 1

L_CI = 0

checkAndSetInitValue(variables)

compute_ext_field()

countLabels()

linkToData(dataInput)

sampleNextInternal(v)

samplingWarmUp(v)

class pyhrf.jde.asl_physio_hierarchical.MixtureParamsSampler(name, response_level_name, val_ini=None, do_sampling=True, use_true_value=False)

Bases: 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)

get_current_means()

get_current_vars()

get_true_values_from_simulation_dict()

linkToData(dataInput)

sampleNextInternal(variables)

class pyhrf.jde.asl_physio_hierarchical.NoiseVarianceSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

checkAndSetInitValue(variables)

compute_y_tilde()

linkToData(dataInput)

sampleNextInternal(variables)

class pyhrf.jde.asl_physio_hierarchical.PerfBaselineSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

checkAndSetInitValue(variables)

compute_residuals()

compute_wa(a=None)

linkToData(dataInput)

sampleNextInternal(v)

class pyhrf.jde.asl_physio_hierarchical.PerfBaselineVarianceSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

checkAndSetInitValue(variables)

linkToData(dataInput)

sampleNextInternal(v)

class pyhrf.jde.asl_physio_hierarchical.PerfMixtureSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.asl_physio_hierarchical.MixtureParamsSampler, pyhrf.xmlio.Initable

checkAndSetInitValue(variables)

get_true_values_from_simulation_cdefs(cdefs)

class pyhrf.jde.asl_physio_hierarchical.PerfResponseLevelSampler(val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.asl_physio_hierarchical.ResponseLevelSampler, pyhrf.xmlio.Initable

checkAndSetInitValue(variables)

computeVarYTildeOpt()

class pyhrf.jde.asl_physio_hierarchical.PhysioBOLDResponseSampler(smooth_order=2, zero_constraint=True, duration=25.0, normalise=1.0, val_ini=None, do_sampling=True, use_true_value=False, prior_type='not_regularized')

Bases: pyhrf.jde.asl_physio_hierarchical.ResponseSampler, pyhrf.xmlio.Initable

computeYTilde()

y - sum cWXg - Pl - wa

get_mat_X()

get_mat_XtX()

get_stackX()

sampleNextInternal(variables)

Sample BRF

changes to mean: changes to var:

samplingWarmUp(v)

class pyhrf.jde.asl_physio_hierarchical.PhysioBOLDResponseVarianceSampler(val_ini=array([ 0.001]), do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

checkAndSetInitValue(v)

linkToData(dataInput)

sampleNextInternal(v)

Sample variance of BRF

class pyhrf.jde.asl_physio_hierarchical.PhysioPerfResponseSampler(smooth_order=2, zero_constraint=True, duration=25.0, normalise=1.0, val_ini=None, do_sampling=True, use_true_value=False, diff_res=True, prior_type='not_regularized')

Bases: pyhrf.jde.asl_physio_hierarchical.ResponseSampler, pyhrf.xmlio.Initable

computeYTilde()

y - sum aXh - Pl - wa

get_mat_X()

get_mat_XtX()

get_stackX()

sampleNextInternal(variables)

Sample PRF with physio prior

changes to mean: add a factor of Omega h Sigma_g^-1 v_g^-1

samplingWarmUp(variables)

class pyhrf.jde.asl_physio_hierarchical.PhysioPerfResponseVarianceSampler(val_ini=array([ 0.001]), do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

checkAndSetInitValue(v)

linkToData(dataInput)

sampleNextInternal(v)

Sample variance of PRF

class pyhrf.jde.asl_physio_hierarchical.PhysioTrueBOLDResponseSampler(smooth_order=2, zero_constraint=True, duration=25.0, normalise=1.0, val_ini=None, do_sampling=True, use_true_value=False, prior_type='regularized')

Bases: pyhrf.jde.asl_physio_hierarchical.ResponseSampler, pyhrf.xmlio.Initable

get_mat_X()

get_mat_XtX()

get_stackX()

sampleNextInternal(variables)

Sample TRUE BRF

class pyhrf.jde.asl_physio_hierarchical.PhysioTrueBOLDResponseVarianceSampler(val_ini=array([ 0.001]), do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable, pyhrf.xmlio.Initable

checkAndSetInitValue(v)

linkToData(dataInput)

sampleNextInternal(v)

Sample variance of BRF

class pyhrf.jde.asl_physio_hierarchical.ResponseLevelSampler(name, response_name, mixture_name, val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable

checkAndSetInitValue(variables)

computeRR()

computeVarYTildeOpt()

linkToData(dataInput)

sampleNextInternal(variables)

samplingWarmUp(variables)

class pyhrf.jde.asl_physio_hierarchical.ResponseSampler(name, response_level_name, variance_name, prior_type, smooth_order=2, zero_constraint=True, duration=25.0, normalise=1.0, val_ini=None, do_sampling=True, use_true_value=False)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable

Generic parent class to perfusion response & BOLD response samplers

calcXResp(resp, stackX=None)

checkAndSetInitValue(variables)

computeYTilde()

getOutputs()

get_mat_X()

get_mat_XtX()

get_rlrl()

get_stackX()

get_ybar()

linkToData(dataInput)

sampleNextInternal(variables)

setFinalValue()

updateNorm()

updateXResp()

class pyhrf.jde.asl_physio_hierarchical.WN_BiG_ASLSamplerInput(data, dt, typeLFD, paramLFD, hrfZc, hrfDuration)

Bases: pyhrf.jde.models.WN_BiG_Drift_BOLDSamplerInput

cleanPrecalculations()

makePrecalculations()

pyhrf.jde.asl_physio_hierarchical.b()

pyhrf.jde.asl_physio_hierarchical.compute_StS_StY(rls, v_b, mx, mxtx, ybar, rlrl, yaj, ajak_vb)

yaj and ajak_vb are only used to store intermediate quantities, they’re not inputs.

pyhrf.jde.asl_physio_hierarchical.compute_StS_StY_deterministic(brls, prls, v_b, mx, mxtx, mwx, mxtwx, mwxtwx, ybar, rlrl_bold, rlrl_perf, brlprl, omega, yj, ajak_vb)

yj, ajak_vb and cjck_vb are only used to store intermediate quantities, they’re not inputs.

pyhrf.jde.asl_physio_hierarchical.compute_bRpR(brl, prl, nbConditions, nbVoxels)