def __init__(self):
NRLSampler.__init__(self)
# Comment setting of beta parameter since it is properly handled in the base class
# self.beta = self.parameters[self.P_BETA]
# habituation speed sampling parameters
self.sampleHabitFlag = self.parameters[self.P_SAMPLE_HABITS]
# Habituation initialization
self.habits = self.parameters[self.P_HABITS_INI]
if self.parameters.has_key(self.P_TRUE_HABITS):
# load true habits if exist
self.trueHabits = params[self.P_TRUE_HABITS]
else:
self.trueHabits = None
# parametre pour la Laplacienne tronquee
self.Lexp = self.parameters[self.P_HAB_ALGO_PARAM]
# pour la sauvegarde des donnees
self.habitsHistory = None
self.outputRatio = self.parameters[
self.P_OUTPUT_RATIO] # affiche ou pas les ratio
if self.outputRatio:
self.ratioHistory = None # pour voir les ratio
self.ratiocourbeHistory = None # pour voir la courbe des ratio
self.labelsColors = self.parameters[self.P_LABELS_COLORS]
def saveCurrentValue(self):
NRLSampler.saveCurrentValue(self)
if self.keepSamples:
if (self.iteration % self.sampleHistoryPace) == 0:
# if self.habitsHistory != None:
#self.habitsHistory = concatenate((self.habitsHistory,[self.habits]))
# else:
#self.habitsHistory = [self.habits]
if self.habitsHistory != None:
self.habitsHistory = concatenate(
(self.habitsHistory, [self.habits]))
else:
self.habitsHistory = array([self.habits])
if self.nrlsHistory != None:
self.nrlsHistory = concatenate(
(self.nrlsHistory, [self.currentValue]))
else:
self.nrlsHistory = array([self.currentValue])
if self.outputRatio:
if self.ratioHistory != None:
self.ratioHistory = concatenate(
(self.ratioHistory, [self.ratio]))
else:
self.ratioHistory = array([self.ratio])
if self.ratiocourbeHistory != None:
self.ratiocourbeHistory = concatenate(
(self.ratiocourbeHistory, [self.ratiocourbe]))
else:
self.ratiocourbeHistory = array([self.ratiocourbe])
def saveCurrentValue(self):
NRLSampler.saveCurrentValue(self)
if self.keepSamples:
if (self.iteration % self.sampleHistoryPace) == 0:
# if self.habitsHistory != None:
#self.habitsHistory = concatenate((self.habitsHistory,[self.habits]))
# else:
#self.habitsHistory = [self.habits]
if self.habitsHistory != None:
self.habitsHistory = concatenate(
(self.habitsHistory, [self.habits]))
else:
self.habitsHistory = array([self.habits])
if self.nrlsHistory != None:
self.nrlsHistory = concatenate(
(self.nrlsHistory, [self.currentValue]))
else:
self.nrlsHistory = array([self.currentValue])
if self.outputRatio:
if self.ratioHistory != None:
self.ratioHistory = concatenate(
(self.ratioHistory, [self.ratio]))
else:
self.ratioHistory = array([self.ratio])
if self.ratiocourbeHistory != None:
self.ratiocourbeHistory = concatenate(
(self.ratiocourbeHistory, [self.ratiocourbe]))
else:
self.ratiocourbeHistory = array([self.ratiocourbe])
def checkAndSetInitValue(self, variables):
NRLSampler.checkAndSetInitLabels(self, variables)
NRLSampler.checkAndSetInitNRL(self, variables)
mixt_par = self.get_variable('mixt_params')
mixt_par.checkAndSetInitValue(variables)
weights_par = variables[self.samplerEngine.I_WEIGHTING_PROBA_NRLS_BAR]
weights_par.checkAndSetInitValue(variables)
def checkAndSetInitValue(self, variables):
NRLSampler.checkAndSetInitLabels(self, variables)
NRLSampler.checkAndSetInitNRL(self, variables)
mixt_par = self.get_variable('mixt_params')
mixt_par.checkAndSetInitValue(variables)
weights_par = variables[self.samplerEngine.I_WEIGHTING_PROBA_NRLS_BAR]
weights_par.checkAndSetInitValue(variables)
def __init__(self):
NRLSampler.__init__(self)
# Comment setting of beta parameter since it is properly handled in the base class
# self.beta = self.parameters[self.P_BETA]
# habituation speed sampling parameters
self.sampleHabitFlag = self.parameters[self.P_SAMPLE_HABITS]
# Habituation initialization
self.habits = self.parameters[self.P_HABITS_INI]
if self.parameters.has_key(self.P_TRUE_HABITS):
# load true habits if exist
self.trueHabits = params[self.P_TRUE_HABITS]
else:
self.trueHabits = None
# parametre pour la Laplacienne tronquee
self.Lexp = self.parameters[self.P_HAB_ALGO_PARAM]
# pour la sauvegarde des donnees
self.habitsHistory = None
self.outputRatio = self.parameters[
self.P_OUTPUT_RATIO] # affiche ou pas les ratio
if self.outputRatio:
self.ratioHistory = None # pour voir les ratio
self.ratiocourbeHistory = None # pour voir la courbe des ratio
self.labelsColors = self.parameters[self.P_LABELS_COLORS]
def updateObsersables(self):
NRLSampler.updateObsersables(self)
self.obs += 1
self.cumulHabits += self.habits
#self.meanHabits = self.cumulHabits / self.nbItObservables
self.meanHabits = self.cumulHabits / self.obs
temp = zeros((self.nbConditions, self.nbVox), dtype=float)
for c in xrange(self.nbClasses):
putmask(temp, self.labels == c, self.habits)
self.cumulHabitsCond[c, :, :] += temp
self.voxelActivity[c, :, :] += self.labels == c
self.meanHabitsCond = self.cumulHabitsCond / self.voxelActivity
def updateObsersables(self):
NRLSampler.updateObsersables(self)
self.obs += 1
self.cumulHabits += self.habits
#self.meanHabits = self.cumulHabits / self.nbItObservables
self.meanHabits = self.cumulHabits / self.obs
temp = zeros((self.nbConditions, self.nbVox), dtype=float)
for c in xrange(self.nbClasses):
putmask(temp, self.labels == c, self.habits)
self.cumulHabitsCond[c, :, :] += temp
self.voxelActivity[c, :, :] += self.labels == c
self.meanHabitsCond = self.cumulHabitsCond / self.voxelActivity
def linkToData(self, dataInput):
NRLSampler.linkToData(self, dataInput)
# recuperation de X (matrice (nbCond * ny * nh) )
self.varSingleCondXtrials = self.dataInput.varSingleCondXtrials
# pour les onsets
#self.nbTrials=zeros((self.nbSessions,self.nbConditions), dtype=int)
# for isess in xrange(self.nbSessions):
self.onsets = self.dataInput.onsets
self.lastonset = 0.
self.deltaOns = {}
self.nbTrials = zeros(self.nbConditions, dtype=int)
for nc in xrange(self.nbConditions):
self.deltaOns[nc] = numpy.diff(self.onsets[nc])
self.nbTrials[nc] = len(self.onsets[nc])
self.lastonset = max(self.lastonset,
self.onsets[nc][self.nbTrials[nc] - 1])
# astuce pour les donnees reelles -> pour remettre 'a zero' tout les 4
# occurences
for trial in xrange(self.nbTrials[nc] - 1):
if (((trial + 1) % 4) == 0):
self.deltaOns[nc][trial] = 100.
else:
# on divise les deltaOns par 4. pour prendre plus en compte
# les valeurs anterieures d'habituation
self.deltaOns[nc][trial] = self.deltaOns[nc][trial] / 4.
# calcul du dernier onset -> pour afficher les timesNRLs
self.lastonset = int(self.lastonset + 1)
# print self.lastonset
# determination du nbGammas utile dans spExtract -> mais il y a une
# erreur pour Xmask (dans sparsedot
self.nbGammas = range(self.nbConditions)
self.nnulls = range(self.nbConditions)
self.Xmask = range(self.nbConditions)
for nc in xrange(self.nbConditions):
self.Xmask[nc] = range(self.nbTrials[nc])
self.nbGammas[nc] = zeros(self.nbTrials[nc], dtype=int)
self.nnulls[nc] = zeros(
(self.varSingleCondXtrials[nc, :, :] == 0).sum())
for i in xrange(self.nbTrials[nc]):
#self.nbGammas[j][i] = (self.varSingleCondXtrials[j,:,:] == (i + 1)).sum()
self.Xmask[nc][i] = transpose(
where(self.varSingleCondXtrials[nc, :, :] == (i + 1)))
self.nbGammas[nc][i] = shape(self.Xmask[nc][i])[0]
logger.info('deltaOns :')
logger.info(self.deltaOns)
def linkToData(self, dataInput):
NRLSampler.linkToData(self, dataInput)
# recuperation de X (matrice (nbCond * ny * nh) )
self.varSingleCondXtrials = self.dataInput.varSingleCondXtrials
# pour les onsets
#self.nbTrials=zeros((self.nbSessions,self.nbConditions), dtype=int)
# for isess in xrange(self.nbSessions):
self.onsets = self.dataInput.onsets
self.lastonset = 0.
self.deltaOns = {}
self.nbTrials = zeros(self.nbConditions, dtype=int)
for nc in xrange(self.nbConditions):
self.deltaOns[nc] = numpy.diff(self.onsets[nc])
self.nbTrials[nc] = len(self.onsets[nc])
self.lastonset = max(
self.lastonset, self.onsets[nc][self.nbTrials[nc] - 1])
# astuce pour les donnees reelles -> pour remettre 'a zero' tout les 4
# occurences
for trial in xrange(self.nbTrials[nc] - 1):
if (((trial + 1) % 4) == 0):
self.deltaOns[nc][trial] = 100.
else:
# on divise les deltaOns par 4. pour prendre plus en compte
# les valeurs anterieures d'habituation
self.deltaOns[nc][trial] = self.deltaOns[nc][trial] / 4.
# calcul du dernier onset -> pour afficher les timesNRLs
self.lastonset = int(self.lastonset + 1)
# print self.lastonset
# determination du nbGammas utile dans spExtract -> mais il y a une
# erreur pour Xmask (dans sparsedot
self.nbGammas = range(self.nbConditions)
self.nnulls = range(self.nbConditions)
self.Xmask = range(self.nbConditions)
for nc in xrange(self.nbConditions):
self.Xmask[nc] = range(self.nbTrials[nc])
self.nbGammas[nc] = zeros(self.nbTrials[nc], dtype=int)
self.nnulls[nc] = zeros(
(self.varSingleCondXtrials[nc, :, :] == 0).sum())
for i in xrange(self.nbTrials[nc]):
#self.nbGammas[j][i] = (self.varSingleCondXtrials[j,:,:] == (i + 1)).sum()
self.Xmask[nc][i] = transpose(
where(self.varSingleCondXtrials[nc, :, :] == (i + 1)))
self.nbGammas[nc][i] = shape(self.Xmask[nc][i])[0]
logger.info('deltaOns :')
logger.info(self.deltaOns)
def initObservables(self):
NRLSampler.initObservables(self)
# sauvegarde des habits et nrls
self.nrlsHistory = None
self.meanHabits = None
# Toutes les habituations par voxels
self.cumulHabits = zeros((self.nbConditions, self.nbVox), dtype=float)
self.meanHabitsCond = zeros(
(self.nbClasses, self.nbConditions, self.nbVox), dtype=float)
self.cumulHabitsCond = zeros(
(self.nbClasses, self.nbConditions, self.nbVox), dtype=float)
self.voxelActivity = zeros(
(self.nbClasses, self.nbConditions, self.nbVox), dtype=int)
self.obs = 0
def initObservables(self):
NRLSampler.initObservables(self)
# sauvegarde des habits et nrls
self.nrlsHistory = None
self.meanHabits = None
# Toutes les habituations par voxels
self.cumulHabits = zeros((self.nbConditions, self.nbVox), dtype=float)
self.meanHabitsCond = zeros(
(self.nbClasses, self.nbConditions, self.nbVox), dtype=float)
self.cumulHabitsCond = zeros(
(self.nbClasses, self.nbConditions, self.nbVox), dtype=float)
self.voxelActivity = zeros(
(self.nbClasses, self.nbConditions, self.nbVox), dtype=int)
self.obs = 0
def jde_analyse(fdata, contrasts, output_dir):
from pyhrf.jde.models import BOLDGibbsSampler as BG
from pyhrf.jde.hrf import RHSampler
from pyhrf.jde.nrl.bigaussian import NRLSampler
sampler = BG(nb_iterations=250,
hrf_var=RHSampler(do_sampling=False, val_ini=np.array([0.05])),
response_levels=NRLSampler(contrasts=contrasts))
analyser = JDEMCMCAnalyser(sampler=sampler)
analyser.set_gzip_outputs(True)
tt = FMRITreatment(fdata, analyser, output_dir=output_dir)
tt.run(parallel='local')
def jde_analyse(data=None, nbIterations=3,
hrfModel='estimated', hrfNorm=1., hrfTrick=False,
sampleHrfVar=True,
hrfVar=1e-5, keepSamples=False, samplesHistPace=1):
"""
"""
if data is None:
data = pyhrf.data.get_default_data()
data.keep_only_rois([2])
if hrfModel == 'estimated':
sampleHRF = True
elif hrfModel == 'canonical':
sampleHRF = False
else:
raise Exception('Unknown hrf model %s' % hrfModel)
hrfSampler = HRFSampler({
HRFSampler.P_SAMPLE_FLAG: sampleHRF,
HRFSampler.P_NORMALISE: hrfNorm,
HRFSampler.P_TRICK: hrfTrick,
})
hrfVarSampler = RHSampler({
RHSampler.P_SAMPLE_FLAG: sampleHrfVar,
})
nrlSampler = NRLSampler({
NRLSampler.P_KEEP_SAMPLES: keepSamples,
NRLSampler.P_SAMPLE_HIST_PACE: samplesHistPace,
})
sampler = BOLDGibbsSampler({
BOLDGibbsSampler.P_NB_ITERATIONS: nbIterations,
BOLDGibbsSampler.P_RH: hrfVarSampler,
BOLDGibbsSampler.P_HRF: hrfSampler,
BOLDGibbsSampler.P_NRLS: nrlSampler,
})
analyser = JDEAnalyser({
JDEAnalyser.P_SAMPLER: sampler,
JDEAnalyser.P_OUTPUT_FILE: None, })
result = analyser.analyse(data)
output = analyser.outputResults(result)
return output
def getOutputs(self):
outputs = NRLSampler.getOutputs(self)
axes_names = ['condition', 'voxel']
axes_domains = {'condition': self.dataInput.cNames}
outputs['pm_Habits'] = xndarray(self.meanHabits,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="Habituation")
axes_names = ['condition', 'numeroVox', 'time']
axes_domains = {
'condition': self.dataInput.cNames,
'numeroVox': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'time': range(self.lastonset)
}
outputs['pm_timeNrls'] = xndarray(self.outputTimeNRLs,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="TimeNRLs")
dt = self.samplerEngine.get_variable('hrf').dt
rpar = self.dataInput.paradigm.get_joined_and_rastered(dt)
parmask = [where(rpar[c] == 1) for c in self.dataInput.cNames]
tnrl = zeros(
(self.nbVox, self.nbConditions, len(rpar[rpar.keys()[0]])),
dtype=float)
for i in xrange(self.nbVox):
for j in xrange(self.nbConditions):
tnrl[i, j, parmask[j]] = self.timeNrls[j][i, :]
# TODO: dirac outputs to use less space
axes_domains = {
'condition': self.dataInput.cNames,
'time': arange(0, self.dataInput.paradigm.get_t_max(), dt)
}
outputs['pm_tNrls'] = xndarray(
tnrl,
axes_names=['voxel', 'condition', 'time'],
axes_domains=axes_domains,
value_label="nrl")
if self.keepSamples:
axes_names = ['iteration', 'condition', 'voxel']
axes_domains = {
'classe': ['inactif', 'actif'],
'condition': self.dataInput.cNames,
'iteration': self.sampleHistoryIterations
}
outputs['pm_Habits_hist'] = xndarray(self.habitsHistory,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="Habituation")
if self.outputRatio:
axes_names = ['iteration', 'condition', 'voxel', 'ratio']
axes_domains = {
'condition': self.dataInput.cNames,
'iteration': self.sampleHistoryIterations,
'ratio': [1, 2]
}
outputs['pm_ratio_hist'] = xndarray(self.ratioHistory,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="Ratio")
axes_names = [
'iteration', 'condition', 'voxel', 'courbe', 'ratio'
]
axes_domains = {
'condition': self.dataInput.cNames,
'iteration': self.sampleHistoryIterations,
'ratio': [1, 2, 3, 4, 5],
'courbe': arange(0., 1., 0.01)
}
outputs['pm_ratiocourbe_hist'] = xndarray(
self.ratiocourbeHistory,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="Courbe Ratio")
axes_names = ['condition', 'voxel']
axes_domains = {'condition': self.dataInput.cNames}
outputs['pm_compteur'] = xndarray(self.compteur,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="compteur")
return outputs
def computeVarYTildeOpt(self, varXh):
NRLSampler.computeVarYTildeOpt(self, varXh)
matPl = self.samplerEngine.get_variable('drift').matPl
self.varYbar = self.varYtilde - matPl
def checkAndSetInitValue(self, variables):
# init NRL and Labels
NRLSampler.checkAndSetInitValue(self, variables)
self.checkAndSetInitHabit(variables)
def cleanObservables(self):
NRLSampler.cleanObservables(self)
del self.cumulHabits
def cleanObservables(self):
NRLSampler.cleanObservables(self)
del self.cumulHabits
def computeVarYTildeOpt(self, varXh):
NRLSampler.computeVarYTildeOpt(self, varXh)
matPl = self.samplerEngine.get_variable('drift').matPl
self.varYbar = self.varYtilde - matPl
def checkAndSetInitValue(self, variables):
# init NRL and Labels
NRLSampler.checkAndSetInitValue(self, variables)
self.checkAndSetInitHabit(variables)
def getOutputs(self):
outputs = NRLSampler.getOutputs(self)
axes_names = ['condition', 'voxel']
axes_domains = {'condition': self.dataInput.cNames}
outputs['pm_Habits'] = xndarray(self.meanHabits,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="Habituation")
axes_names = ['condition', 'numeroVox', 'time']
axes_domains = {'condition': self.dataInput.cNames,
'numeroVox': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'time': range(self.lastonset)}
outputs['pm_timeNrls'] = xndarray(self.outputTimeNRLs,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="TimeNRLs")
dt = self.samplerEngine.get_variable('hrf').dt
rpar = self.dataInput.paradigm.get_joined_and_rastered(dt)
parmask = [where(rpar[c] == 1) for c in self.dataInput.cNames]
tnrl = zeros((self.nbVox, self.nbConditions, len(rpar[rpar.keys()[0]])),
dtype=float)
for i in xrange(self.nbVox):
for j in xrange(self.nbConditions):
tnrl[i, j, parmask[j]] = self.timeNrls[j][i, :]
# TODO: dirac outputs to use less space
axes_domains = {'condition': self.dataInput.cNames,
'time': arange(0, self.dataInput.paradigm.get_t_max(), dt)}
outputs['pm_tNrls'] = xndarray(tnrl,
axes_names=[
'voxel', 'condition', 'time'],
axes_domains=axes_domains,
value_label="nrl")
if self.keepSamples:
axes_names = ['iteration', 'condition', 'voxel']
axes_domains = {'classe': ['inactif', 'actif'],
'condition': self.dataInput.cNames,
'iteration': self.sampleHistoryIterations}
outputs['pm_Habits_hist'] = xndarray(self.habitsHistory,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="Habituation")
if self.outputRatio:
axes_names = ['iteration', 'condition', 'voxel', 'ratio']
axes_domains = {'condition': self.dataInput.cNames,
'iteration': self.sampleHistoryIterations,
'ratio': [1, 2]}
outputs['pm_ratio_hist'] = xndarray(self.ratioHistory,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="Ratio")
axes_names = [
'iteration', 'condition', 'voxel', 'courbe', 'ratio']
axes_domains = {'condition': self.dataInput.cNames,
'iteration': self.sampleHistoryIterations,
'ratio': [1, 2, 3, 4, 5],
'courbe': arange(0., 1., 0.01)
}
outputs['pm_ratiocourbe_hist'] = xndarray(self.ratiocourbeHistory,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="Courbe Ratio")
axes_names = ['condition', 'voxel']
axes_domains = {'condition': self.dataInput.cNames}
outputs['pm_compteur'] = xndarray(self.compteur,
axes_names=axes_names,
axes_domains=axes_domains,
value_label="compteur")
return outputs
