def dummy_jde(fmri_data, dt):
print 'run dummy_jde ...'
jde_mcmc_sampler = \
physio_build_jde_mcmc_sampler(3, 'basic_regularized',
do_sampling_prf=False,
do_sampling_brf=False,
do_sampling_prls=False,
do_sampling_labels=False,
do_sampling_prf_var=False,
do_sampling_brf_var=False,
brf_var_ini=np.array([0.1]),
prf_var_ini=np.array([0.1]))
analyser = JDEMCMCAnalyser(jde_mcmc_sampler, copy_sampler=False, dt=dt)
analyser.set_pass_errors(False)
tjde_mcmc = FMRITreatment(fmri_data, analyser, output_dir=None)
outputs, fns = tjde_mcmc.run()
print 'dummy_jde done!'
return tjde_mcmc.analyser.sampler
def jde_analyse_2steps_v1(output_dir, fmri_data, dt, nb_iterations, brf_var=None,
do_sampling_brf_var=False, prf_var=None, do_sampling_prf_var=False):
"""
#Return:
# dict of outputs
"""
nb_iterations_m1 = nb_iterations_m2 = nb_iterations
jde_output_dir = op.join(output_dir, 'bold_only')
if not op.exists(jde_output_dir):
os.makedirs(jde_output_dir)
dummy_sampler = dummy_jde(fmri_data, dt)
prf_ini_m1 = np.zeros_like(dummy_sampler.get_variable('prf').finalValue)
prls_ini_m1 = np.zeros_like(dummy_sampler.get_variable('prl').finalValue)
#perf_bl_ini_m1 = np.zeros_like(dummy_sampler.get_variable('perf_baseline').finalValue)
jde_mcmc_sampler_m1 = \
physio_build_jde_mcmc_sampler(nb_iterations_m1, 'basic_regularized',
prf_ini=prf_ini_m1,
do_sampling_prf=False,
prls_ini=prls_ini_m1,
do_sampling_prls=False,
brf_var_ini=brf_var,
do_sampling_brf_var=do_sampling_brf_var,
prf_var_ini=brf_var,
do_sampling_prf_var=False,
flag_zc = False)
pyhrf.verbose(2, 'JDE first pass -> BOLD fit')
analyser_m1 = JDEMCMCAnalyser(jde_mcmc_sampler_m1, copy_sampler=False,
dt=dt)
analyser_m1.set_pass_errors(False)
tjde_mcmc_m1 = FMRITreatment(fmri_data, analyser_m1,
output_dir=jde_output_dir)
outputs_m1, fns_m1 = tjde_mcmc_m1.run()
jde_output_dir = op.join(output_dir, 'perf_only_from_res')
if not op.exists(jde_output_dir):
os.makedirs(jde_output_dir)
brf_m1 = jde_mcmc_sampler_m1.get_variable('brf').finalValue
omega = jde_mcmc_sampler_m1.get_variable('prf').omega_operator
print omega.shape
print np.concatenate(([0],[0],[0],brf_m1,[0],[0],[0])).shape
#prf_m2 = np.dot(omega, brf_m1)
prf_m2 = np.dot(omega, np.concatenate(([0],[0],[0],brf_m1,[0],[0],[0])))[3:-3]
if 0:
import matplotlib.pyplot as plt
plt.plot(brf_m1)
plt.plot(prf_m2)
plt.show()
if 0:
import matplotlib.pyplot as plt
f, axarr = plt.subplots(2, 2)
axarr[0, 0].matshow(omega)
axarr[0, 1].plot(prf_m2)
import copy
omega2 = copy.deepcopy(omega)
omega2[-5:,:5] = 0.
omega2[:5,-5:] = 0.
prf_m2 = np.dot(omega2, brf_m1)
axarr[1, 0].matshow(omega2)
axarr[1, 1].plot(prf_m2)
plt.show()
#print 'PRF M2 shape = ', prf_m2.shape
#force begin & end to be zero
#XXX TODO: rather fix omega to avoid this kind of thing or use zero-contrainst again
#prf_m2[0] = 0.
#prf_m2[-5:] = 0.
brls_m1 = jde_mcmc_sampler_m1.get_variable('brl').finalValue
labels_m1 = jde_mcmc_sampler_m1.get_variable('label').finalValue >= 0.5
labels_m1 = labels_m1.astype(np.int32)
drift_m1 = jde_mcmc_sampler_m1.get_variable('drift_coeff').finalValue
drift_var_m1 = jde_mcmc_sampler_m1.get_variable('drift_var').finalValue
noise_var_m1 = jde_mcmc_sampler_m1.get_variable('noise_var').finalValue
pyhrf.verbose(2, 'ASL JDE second pass -> Perfusion fit')
pyhrf.verbose(1, 'Physiological prior stochastic regularized')
jde_mcmc_sampler_m2 = \
physio_build_jde_mcmc_sampler(nb_iterations_m2, 'physio_stochastic_not_regularized',
#prf_ini=prf_m2,
do_sampling_prf=True,
flag_zc=True,
prf_var_ini = prf_var,
do_sampling_prf_var = do_sampling_prf_var,
brls_ini=brls_m1, #if prls_ini_m1, it should be the same
do_sampling_brls=False,
brf_ini=brf_m1, #if prf_ini_m1, PRF sampler 'basic_regularized'
do_sampling_brf=False,
brf_var_ini=brf_var,
do_sampling_brf_var=False,
#perf_bl_ini=perf_bl_m1,
drift_ini=drift_m1,
do_sampling_drift = False,
drift_var_ini = drift_var_m1,
do_sampling_drift_var = False,
noise_var_ini=noise_var_m1,
labels_ini=labels_m1,
do_sampling_labels=True)
""" available_priors = ['physio_stochastic_regularized',
'physio_stochastic_not_regularized',
'physio_deterministic',
'physio_deterministic_hack',
'basic_regularized'] """
analyser_m2 = JDEMCMCAnalyser(jde_mcmc_sampler_m2, dt=dt)
analyser_m2.set_pass_errors(False)
tjde_mcmc_m2 = FMRITreatment(fmri_data, analyser_m2,
output_dir=jde_output_dir)
outputs_m2, fns_m2 = tjde_mcmc_m2.run()
# combine outputs
outputs = {}
for o in ['brf_pm', 'brl_pm']:
outputs[o] = outputs_m1[o]
outputs['noise_var_pm_m1'] = outputs_m1['noise_var_pm']
for o in ['prf_pm', 'prl_pm', 'label_pm']:
outputs[o] = outputs_m2[o]
outputs['noise_var_pm_m2'] = outputs_m2['noise_var_pm']
return outputs_m1
