def simulate_asl_physio_rfs(output_dir=None,
noise_scenario='high_snr',
spatial_size='tiny',
v_noise=None):
"""
Generate ASL data according to a LTI system, with PRF and BRF generated
from a physiological model.
Args:
- output_dir (str|None): path where to save outputs as nifti files.
If None: no output files
- noise_scenario ("high_snr"|"low_snr"): scenario defining the SNR
- spatial_size ("tiny"|"normal") : scenario for the size of the map
- "tiny" produces 2x2 maps
- "normal" produces 20x20 maps
Result:
dict (<item_label (str)> : <simulated_item (np.ndarray)>)
-> a dictionary mapping names of simulated items to their values
WARNING: in this dict the 'bold' item is in fact the ASL signal.
This name was used to be compatible with JDE which assumes
that the functional time series is named "bold".
TODO: rather use the more generic label 'fmri_signal'.
"""
drift_var = 10.
dt = .5
dsf = 2 #down sampling factor
#tr = dt * dsf
tr = 3.
if spatial_size == 'tiny':
lmap1, lmap2, lmap3 = 'tiny_1', 'tiny_2', 'tiny_3'
elif spatial_size == 'random_small':
lmap1, lmap2, lmap3 = 'random_small', 'random_small', 'random_small'
else:
lmap1, lmap2, lmap3 = 'ghost', 'icassp13', 'house_sun'
if noise_scenario == 'high_snr':
v_noise = v_noise or 0.05
conditions = [
Condition(name='audio',
perf_m_act=5.,
perf_v_act=.1,
perf_v_inact=.2,
bold_m_act=15.,
bold_v_act=.1,
bold_v_inact=.2,
label_map=lmap1),
Condition(name='video',
perf_m_act=5.,
perf_v_act=.11,
perf_v_inact=.21,
bold_m_act=14.,
bold_v_act=.11,
bold_v_inact=.21,
label_map=lmap2),
Condition(name='damier',
perf_m_act=12.,
perf_v_act=.12,
perf_v_inact=.22,
bold_m_act=20.,
bold_v_act=.12,
bold_v_inact=.22,
label_map=lmap3),
]
elif noise_scenario == 'low_snr_low_prl':
v_noise = v_noise or 7.
scale = .3
conditions = [
Condition(name='audio',
perf_m_act=1.6 * scale,
perf_v_act=.1,
perf_v_inact=.1,
bold_m_act=2.2,
bold_v_act=.3,
bold_v_inact=.3,
label_map=lmap1),
Condition(name='video',
perf_m_act=1.6 * scale,
perf_v_act=.1,
perf_v_inact=.1,
bold_m_act=2.2,
bold_v_act=.3,
bold_v_inact=.3,
label_map=lmap2),
]
else: #low_snr
v_noise = v_noise or 2.
conditions = [
Condition(name='audio',
perf_m_act=1.6,
perf_v_act=.3,
perf_v_inact=.3,
bold_m_act=2.2,
bold_v_act=.3,
bold_v_inact=.3,
label_map=lmap1),
Condition(name='video',
perf_m_act=1.6,
perf_v_act=.3,
perf_v_inact=.3,
bold_m_act=2.2,
bold_v_act=.3,
bold_v_inact=.3,
label_map=lmap2),
]
simulation_steps = {
'dt': dt,
'dsf': dsf,
'tr': tr,
'condition_defs': conditions,
# Paradigm
'paradigm': simbase.create_localizer_paradigm_avd,
'rastered_paradigm': simbase.rasterize_paradigm,
# Labels
'labels_vol': simbase.create_labels_vol,
'labels': simbase.flatten_labels_vol,
'nb_voxels': lambda labels: labels.shape[1],
# Physiological model (for generation of RFs)
'physiological_params': PHY_PARAMS_FRISTON00,
# Brls
'brls': simbase.create_time_invariant_gaussian_brls,
# Prls
'prls': simbase.create_time_invariant_gaussian_prls,
# BRF
'primary_brf': create_physio_brf,
'brf': simbase.duplicate_brf,
# PRF
'primary_prf': create_physio_prf,
'prf': simbase.duplicate_prf,
# Perf baseline
'perf_baseline': simbase.create_perf_baseline,
'perf_baseline_mean': 1.5,
'perf_baseline_var': .4,
# Stim induced
'bold_stim_induced': simbase.create_bold_stim_induced_signal,
'perf_stim_induced': simbase.create_perf_stim_induced_signal,
# Noise
'v_gnoise': v_noise,
'noise': simbase.create_gaussian_noise_asl,
# Drift
'drift_order': 4,
'drift_var': drift_var,
'drift_coeffs': simbase.create_drift_coeffs_asl,
'drift': simbase.create_polynomial_drift_from_coeffs_asl,
# Bold # maybe rename as ASL (should be handled afterwards ...
'ctrl_tag_mat': simbase.build_ctrl_tag_matrix,
'asl_shape': simbase.calc_asl_shape,
'bold': simbase.create_asl_from_stim_induced,
}
simu_graph = Pipeline(simulation_steps)
# Compute everything
simu_graph.resolve()
simulation = simu_graph.get_values()
if output_dir is not None:
#simu_graph.save_graph_plot(op.join(output_dir, 'simulation_graph.png'))
simbase.simulation_save_vol_outputs(simulation, output_dir)
# f = open(op.join(output_dir, 'simulation.pck'), 'w')
# cPickle.dump(simulation, f)
# f.close()
return simulation
def simulate_asl_physio_rfs(output_dir=None, noise_scenario='high_snr',
spatial_size='tiny'):
"""
Generate ASL data according to a LTI system, with PRF and BRF generated
from a physiological model.
Args:
- output_dir (str|None): path where to save outputs as nifti files.
If None: no output files
- noise_scenario ("high_snr"|"low_snr"): scenario defining the SNR
- spatial_size ("tiny"|"normal") : scenario for the size of the map
- "tiny" produces 2x2 maps
- "normal" produces 20x20 maps
Result:
dict (<item_label (str)> : <simulated_item (np.ndarray)>)
-> a dictionary mapping names of simulated items to their values
WARNING: in this dict the 'bold' item is in fact the ASL signal.
This name was used to be compatible with JDE which assumes
that the functional time series is named "bold".
TODO: rather use the more generic label 'fmri_signal'.
"""
drift_var = 10.
dt = .5
dsf = 2 #down sampling factor
if spatial_size == 'tiny':
lmap1, lmap2, lmap3 = 'tiny_1', 'tiny_2', 'tiny_3'
elif spatial_size == 'random_small':
lmap1, lmap2, lmap3 = 'random_small', 'random_small', 'random_small'
else:
lmap1, lmap2, lmap3 = 'icassp13', 'ghost', 'house_sun'
if noise_scenario == 'high_snr':
v_noise = 0.05
conditions = [
Condition(name='audio', perf_m_act=5., perf_v_act=.1, perf_v_inact=.2,
bold_m_act=15., bold_v_act=.1, bold_v_inact=.2,
label_map=lmap1),
Condition(name='video', perf_m_act=5., perf_v_act=.11, perf_v_inact=.21,
bold_m_act=14., bold_v_act=.11, bold_v_inact=.21,
label_map=lmap2),
Condition(name='damier', perf_m_act=12.,
perf_v_act=.12, perf_v_inact=.22,
bold_m_act=20., bold_v_act=.12, bold_v_inact=.22,
label_map=lmap3),
]
elif noise_scenario == 'low_snr_low_prl':
v_noise = 7.
scale = .3
print 'noise_scenario: low_snr_low_prl'
conditions = [
Condition(name='audio', perf_m_act=1.6*scale, perf_v_act=.1,
perf_v_inact=.1,
bold_m_act=2.2, bold_v_act=.3, bold_v_inact=.3,
label_map=lmap1),
Condition(name='video', perf_m_act=1.6*scale, perf_v_act=.1,
perf_v_inact=.1,
bold_m_act=2.2, bold_v_act=.3, bold_v_inact=.3,
label_map=lmap2),
]
else: #low_snr
v_noise = 2.
conditions = [
Condition(name='audio', perf_m_act=1.6, perf_v_act=.3,
perf_v_inact=.3,
bold_m_act=2.2, bold_v_act=.3, bold_v_inact=.3,
label_map=lmap1),
Condition(name='video', perf_m_act=1.6, perf_v_act=.3,
perf_v_inact=.3,
bold_m_act=2.2, bold_v_act=.3, bold_v_inact=.3,
label_map=lmap2),
]
simulation_steps = {
'dt' : dt,
'dsf' : dsf,
'tr' : dt * dsf,
'condition_defs' : conditions,
# Paradigm
'paradigm' : simbase.create_localizer_paradigm_avd,
'rastered_paradigm' : simbase.rasterize_paradigm,
# Labels
'labels_vol' : simbase.create_labels_vol,
'labels' : simbase.flatten_labels_vol,
'nb_voxels': lambda labels: labels.shape[1],
# Physiological model (for generation of RFs)
'physiological_params' : PHY_PARAMS_FRISTON00,
# Brls
'brls' : simbase.create_time_invariant_gaussian_brls,
# Prls
'prls' : simbase.create_time_invariant_gaussian_prls,
# BRF
'primary_brf' : create_physio_brf,
'brf' : simbase.duplicate_brf,
# PRF
'primary_prf' : create_physio_prf,
'prf' : simbase.duplicate_prf,
# Perf baseline
'perf_baseline' : simbase.create_perf_baseline,
'perf_baseline_mean' : 1.5,
'perf_baseline_var': .4,
# Stim induced
'bold_stim_induced' : simbase.create_bold_stim_induced_signal,
'perf_stim_induced' : simbase.create_perf_stim_induced_signal,
# Noise
'v_gnoise' : v_noise,
'noise' : simbase.create_gaussian_noise_asl,
# Drift
'drift_order' : 4,
'drift_var' : drift_var,
'drift_coeffs':simbase.create_drift_coeffs_asl,
'drift' : simbase.create_polynomial_drift_from_coeffs_asl,
# Bold # maybe rename as ASL (should be handled afterwards ...
'ctrl_tag_mat' : simbase.build_ctrl_tag_matrix,
'asl_shape' : simbase.calc_asl_shape,
'bold' : simbase.create_asl_from_stim_induced,
}
simu_graph = Pipeline(simulation_steps)
# Compute everything
simu_graph.resolve()
simulation = simu_graph.get_values()
if output_dir is not None:
#simu_graph.save_graph_plot(op.join(output_dir, 'simulation_graph.png'))
simbase.simulation_save_vol_outputs(simulation, output_dir)
# f = open(op.join(output_dir, 'simulation.pck'), 'w')
# cPickle.dump(simulation, f)
# f.close()
return simulation
def simulate_asl_full_physio(output_dir=None,
noise_scenario='high_snr',
spatial_size='tiny'):
"""
Generate ASL data by integrating a physiological dynamical system.
Ags:
- output_dir (str|None): path where to save outputs as nifti files.
If None: no output files
- noise_scenario ("high_snr"|"low_snr"): scenario defining the SNR
- spatial_size ("tiny"|"normal") : scenario for the size of the map
- "tiny" produces 2x2 maps
- "normal" produces 20x20 maps
Result:
dict (<item_label (str)> : <simulated_item (np.ndarray)>)
-> a dictionary mapping names of simulated items to their values
WARNING: in this dict the 'bold' item is in fact the ASL signal.
This name was used to be compatible with JDE which assumes
that the functional time series is named "bold".
TODO: rather use the more generic label 'fmri_signal'.
TODO: use magnetization model to properly simulate final ASL signal
"""
drift_var = 10.
dt = .5
dsf = 2 #down sampling factor
if spatial_size == 'tiny':
lmap1, lmap2, lmap3 = 'tiny_1', 'tiny_2', 'tiny_3'
elif spatial_size == 'random_small':
lmap1, lmap2, lmap3 = 'random_small', 'random_small', 'random_small'
else:
lmap1, lmap2, lmap3 = 'icassp13', 'ghost', 'house_sun'
if noise_scenario == 'high_snr':
v_noise = 0.05
conditions = [
Condition(name='audio',
m_act=10.,
v_act=.1,
v_inact=.2,
label_map=lmap1),
Condition(name='video',
m_act=11.,
v_act=.11,
v_inact=.21,
label_map=lmap2),
Condition(name='damier',
m_act=12.,
v_act=.12,
v_inact=.22,
label_map=lmap3),
]
else: #low_snr
v_noise = 2.
conditions = [
Condition(name='audio',
m_act=1.6,
v_act=.3,
v_inact=.3,
label_map=lmap1),
Condition(name='video',
m_act=1.6,
v_act=.3,
v_inact=.3,
label_map=lmap2),
]
simulation_steps = {
'dt': dt,
'dsf': dsf,
'tr': dt * dsf,
'condition_defs': conditions,
# Paradigm
'paradigm': simbase.create_localizer_paradigm_avd,
# Labels
'labels_vol': simbase.create_labels_vol,
'labels': simbase.flatten_labels_vol,
'nb_voxels': lambda labels: labels.shape[1],
# Neural efficacy
'neural_efficacies': create_tbg_neural_efficacies,
# BRF
'primary_brf': create_physio_brf,
'brf': simbase.duplicate_brf,
# PRF
'primary_prf': create_physio_prf,
'prf': simbase.duplicate_prf,
# Physiological model
'physiological_params': PHY_PARAMS_FRISTON00,
('flow_induction', 'perf_stim_induced', 'cbv', 'hbr'):
create_evoked_physio_signals,
'bold_stim_induced': create_bold_from_hbr_and_cbv,
# Noise
'v_gnoise': v_noise,
'noise': simbase.create_gaussian_noise_asl,
# Drift
'drift_order': 4,
'drift_var': drift_var,
'drift_coeffs': simbase.create_drift_coeffs_asl,
'drift': simbase.create_polynomial_drift_from_coeffs_asl,
# ASL
'ctrl_tag_mat': simbase.build_ctrl_tag_matrix,
'asl_shape': simbase.calc_asl_shape,
# Perf baseline #should be the inflow at rest ... #TODO
'perf_baseline': simbase.create_perf_baseline,
'perf_baseline_mean': 0.,
'perf_baseline_var': 0.,
# maybe rename to ASL (should be also modified in JDE)#TODO
'bold': simbase.create_asl_from_stim_induced,
}
simu_graph = Pipeline(simulation_steps)
# Compute everything
simu_graph.resolve()
simulation = simu_graph.get_values()
if output_dir is not None:
#simu_graph.save_graph_plot(op.join(output_dir, 'simulation_graph.png'))
simbase.simulation_save_vol_outputs(simulation, output_dir)
# f = open(op.join(output_dir, 'simulation.pck'), 'w')
# cPickle.dump(simulation, f)
# f.close()
return simulation
def simulate_asl_full_physio(output_dir=None, noise_scenario='high_snr',
spatial_size='tiny'):
"""
Generate ASL data by integrating a physiological dynamical system.
Ags:
- output_dir (str|None): path where to save outputs as nifti files.
If None: no output files
- noise_scenario ("high_snr"|"low_snr"): scenario defining the SNR
- spatial_size ("tiny"|"normal") : scenario for the size of the map
- "tiny" produces 2x2 maps
- "normal" produces 20x20 maps
Result:
dict (<item_label (str)> : <simulated_item (np.ndarray)>)
-> a dictionary mapping names of simulated items to their values
WARNING: in this dict the 'bold' item is in fact the ASL signal.
This name was used to be compatible with JDE which assumes
that the functional time series is named "bold".
TODO: rather use the more generic label 'fmri_signal'.
TODO: use magnetization model to properly simulate final ASL signal
"""
drift_var = 10.
dt = .5
dsf = 2 #down sampling factor
if spatial_size == 'tiny':
lmap1, lmap2, lmap3 = 'tiny_1', 'tiny_2', 'tiny_3'
elif spatial_size == 'random_small':
lmap1, lmap2, lmap3 = 'random_small', 'random_small', 'random_small'
else:
lmap1, lmap2, lmap3 = 'icassp13', 'ghost', 'house_sun'
if noise_scenario == 'high_snr':
v_noise = 0.05
conditions = [
Condition(name='audio', m_act=10., v_act=.1, v_inact=.2,
label_map=lmap1),
Condition(name='video', m_act=11., v_act=.11, v_inact=.21,
label_map=lmap2),
Condition(name='damier', m_act=12., v_act=.12, v_inact=.22,
label_map=lmap3),
]
else: #low_snr
v_noise = 2.
conditions = [
Condition(name='audio', m_act=1.6, v_act=.3, v_inact=.3,
label_map=lmap1),
Condition(name='video', m_act=1.6, v_act=.3, v_inact=.3,
label_map=lmap2),
]
simulation_steps = {
'dt' : dt,
'dsf' : dsf,
'tr' : dt * dsf,
'condition_defs' : conditions,
# Paradigm
'paradigm' : simbase.create_localizer_paradigm_avd,
# Labels
'labels_vol' : simbase.create_labels_vol,
'labels' : simbase.flatten_labels_vol,
'nb_voxels': lambda labels: labels.shape[1],
# Neural efficacy
'neural_efficacies' : create_tbg_neural_efficacies,
# BRF
'primary_brf' : create_physio_brf,
'brf' : simbase.duplicate_brf,
# PRF
'primary_prf' : create_physio_prf,
'prf' : simbase.duplicate_prf,
# Physiological model
'physiological_params' : PHY_PARAMS_FRISTON00,
('flow_induction','perf_stim_induced','cbv','hbr') :
create_evoked_physio_signals,
'bold_stim_induced' : create_bold_from_hbr_and_cbv,
# Noise
'v_gnoise' : v_noise,
'noise' : simbase.create_gaussian_noise_asl,
# Drift
'drift_order' : 4,
'drift_var' : drift_var,
'drift_coeffs': simbase.create_drift_coeffs_asl,
'drift' : simbase.create_polynomial_drift_from_coeffs_asl,
# ASL
'ctrl_tag_mat' : simbase.build_ctrl_tag_matrix,
'asl_shape' : simbase.calc_asl_shape,
# Perf baseline #should be the inflow at rest ... #TODO
'perf_baseline' : simbase.create_perf_baseline,
'perf_baseline_mean' : 0.,
'perf_baseline_var': 0.,
# maybe rename to ASL (should be also modified in JDE)#TODO
'bold' : simbase.create_asl_from_stim_induced,
}
simu_graph = Pipeline(simulation_steps)
# Compute everything
simu_graph.resolve()
simulation = simu_graph.get_values()
if output_dir is not None:
#simu_graph.save_graph_plot(op.join(output_dir, 'simulation_graph.png'))
simbase.simulation_save_vol_outputs(simulation, output_dir)
# f = open(op.join(output_dir, 'simulation.pck'), 'w')
# cPickle.dump(simulation, f)
# f.close()
return simulation
