pyhrf.jde.asl module¶

class pyhrf.jde.asl.ASLSampler(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.BOLDResponseLevelSampler object>, perf_response_levels=<pyhrf.jde.asl.PerfResponseLevelSampler object>, labels=<pyhrf.jde.asl.LabelSampler object>, noise_var=<pyhrf.jde.asl.NoiseVarianceSampler object>, brf=<pyhrf.jde.asl.BOLDResponseSampler object>, brf_var=<pyhrf.jde.asl.BOLDResponseVarianceSampler object>, prf=<pyhrf.jde.asl.PerfResponseSampler object>, prf_var=<pyhrf.jde.asl.PerfResponseVarianceSampler object>, bold_mixt_params=<pyhrf.jde.asl.BOLDMixtureSampler object>, perf_mixt_params=<pyhrf.jde.asl.PerfMixtureSampler object>, drift=<pyhrf.jde.asl.DriftCoeffSampler object>, drift_var=<pyhrf.jde.asl.DriftVarianceSampler object>, perf_baseline=<pyhrf.jde.asl.PerfBaselineSampler object>, perf_baseline_var=<pyhrf.jde.asl.PerfBaselineVarianceSampler object>, check_final_value=None, output_fit=False)¶

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

computeFit()¶

default_nb_its = 3000¶

finalizeSampling()¶

getGlobalOutputs()¶

inputClass¶: alias of WN_BiG_ASLSamplerInput

parametersToShow = ['nb_its', 'bold_response_levels', 'brf', 'brf_var', 'prf', 'prf_var']¶

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

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

get_true_values_from_simulation_cdefs(cdefs)¶

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

Bases: pyhrf.jde.asl.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.BOLDResponseSampler(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.asl.ResponseSampler, pyhrf.xmlio.Initable

computeYTilde()¶: y - sum cWXg - Pl - wa

get_mat_X()¶

get_mat_XtX()¶

get_stackX()¶

class pyhrf.jde.asl.BOLDResponseVarianceSampler(val_ini=array([ 0.001]), do_sampling=True, use_true_value=False)¶: Bases: pyhrf.jde.asl.ResponseVarianceSampler, pyhrf.xmlio.Initable

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

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

checkAndSetInitValue(variables)¶

compute_y_tilde()¶

get_accuracy(abs_error, rel_error, fv, tv, atol, rtol)¶

Return the accuray of the estimate fv, compared to the true value tv

Output:: axes_names (list of str), accuracy (numpy array of booleans)

get_final_value()¶

get_true_value()¶

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()¶

class pyhrf.jde.asl.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)¶: Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.

class pyhrf.jde.asl.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()¶

get_MAP_labels()¶

linkToData(dataInput)¶

sampleNextInternal(v)¶: Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.

samplingWarmUp(v)¶: Called before the launch of the main sampling loop by the sampler engine. Should be overriden and perform precalculations.

class pyhrf.jde.asl.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.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)¶: Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.

class pyhrf.jde.asl.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)¶: Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.

class pyhrf.jde.asl.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)¶: Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.

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

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

checkAndSetInitValue(variables)¶

get_true_values_from_simulation_cdefs(cdefs)¶

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

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

checkAndSetInitValue(variables)¶

computeVarYTildeOpt()¶

class pyhrf.jde.asl.PerfResponseSampler(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)¶

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

computeYTilde()¶: y - sum aXh - Pl - wa

get_mat_X()¶

get_mat_XtX()¶

get_stackX()¶

class pyhrf.jde.asl.PerfResponseVarianceSampler(val_ini=array([ 0.001]), do_sampling=True, use_true_value=False)¶: Bases: pyhrf.jde.asl.ResponseVarianceSampler, pyhrf.xmlio.Initable

class pyhrf.jde.asl.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()¶

getOutputs()¶

linkToData(dataInput)¶

sampleNextInternal(variables)¶

samplingWarmUp(variables)¶

updateObsersables()¶

class pyhrf.jde.asl.ResponseSampler(name, response_level_name, variance_name, 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()¶

get_mat_X()¶

get_mat_XtX()¶

get_rlrl()¶

get_stackX()¶

get_ybar()¶

linkToData(dataInput)¶

sampleNextInternal(variables)¶

setFinalValue()¶

updateNorm()¶

updateXResp()¶

class pyhrf.jde.asl.ResponseVarianceSampler(name, response_name, val_ini=array([ 0.001]), do_sampling=True, use_true_value=False)¶

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable

checkAndSetInitValue(v)¶

linkToData(dataInput)¶

sampleNextInternal(v)¶

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

Bases: pyhrf.jde.models.WN_BiG_Drift_BOLDSamplerInput

cleanPrecalculations()¶

makePrecalculations()¶

pyhrf.jde.asl.b()¶

pyhrf.jde.asl.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.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 $d_i$ 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