def from_head_to_hypotheses(ep_name, config, plot_head=False):
# -------------------------------Reading model_data-----------------------------------
reader = TVBReader() if config.input.IS_TVB_MODE else H5Reader()
logger.info("Reading from: " + config.input.HEAD)
head = reader.read_head(config.input.HEAD)
if plot_head:
plotter = Plotter(config)
plotter.plot_head(head)
# --------------------------Hypothesis definition-----------------------------------
# # Manual definition of hypothesis...:
# x0_indices = [20]
# x0_values = [0.9]
# e_indices = [70]
# e_values = [0.9]
# disease_values = x0_values + e_values
# disease_indices = x0_indices + e_indices
# ...or reading a custom file:
# FOLDER_RES = os.path.join(data_folder, ep_name)
hypo_builder = HypothesisBuilder(head.connectivity.number_of_regions,
config=config).set_normalize(0.95)
# This is an example of Excitability Hypothesis:
hyp_x0 = hypo_builder.build_hypothesis_from_file(ep_name)
# This is an example of Epileptogenicity Hypothesis:
hyp_E = hypo_builder.build_hypothesis_from_file(
ep_name, e_indices=hyp_x0.x0_indices)
# This is an example of Mixed Hypothesis:
x0_indices = [hyp_x0.x0_indices[-1]]
x0_values = [hyp_x0.x0_values[-1]]
e_indices = hyp_x0.x0_indices[0:-1].tolist()
e_values = hyp_x0.x0_values[0:-1].tolist()
hyp_x0_E = hypo_builder.set_x0_hypothesis(x0_indices, x0_values). \
set_e_hypothesis(e_indices, e_values).build_hypothesis()
hypos = (hyp_x0, hyp_E, hyp_x0_E)
return head, hypos
def main_vep(config=Config(),
ep_name=EP_NAME,
K_unscaled=K_DEF,
ep_indices=[],
hyp_norm=0.99,
manual_hypos=[],
sim_type="paper",
pse_flag=PSE_FLAG,
sa_pse_flag=SA_PSE_FLAG,
sim_flag=SIM_FLAG,
n_samples=1000,
test_write_read=False):
logger = initialize_logger(__name__, config.out.FOLDER_LOGS)
# -------------------------------Reading data-----------------------------------
reader = TVBReader() if config.input.IS_TVB_MODE else H5Reader()
writer = H5Writer()
logger.info("Reading from: " + config.input.HEAD)
head = reader.read_head(config.input.HEAD)
plotter = Plotter(config)
plotter.plot_head(head)
if test_write_read:
writer.write_head(head, os.path.join(config.out.FOLDER_RES, "Head"))
# --------------------------Hypothesis definition-----------------------------------
hypotheses = []
# Reading a h5 file:
if len(ep_name) > 0:
# For an Excitability Hypothesis you leave e_indices empty
# For a Mixed Hypothesis: you give as e_indices some indices for values > 0
# For an Epileptogenicity Hypothesis: you give as e_indices all indices for values > 0
hyp_file = HypothesisBuilder(head.connectivity.number_of_regions, config=config).set_normalize(hyp_norm). \
build_hypothesis_from_file(ep_name, e_indices=ep_indices)
hyp_file.name += ep_name
# print(hyp_file.string_regions_disease(head.connectivity.region_labels))
hypotheses.append(hyp_file)
hypotheses += manual_hypos
# --------------------------Hypothesis and LSA-----------------------------------
for hyp in hypotheses:
logger.info("\n\nRunning hypothesis: " + hyp.name)
all_regions_indices = np.array(range(head.number_of_regions))
healthy_indices = np.delete(all_regions_indices,
hyp.regions_disease_indices).tolist()
logger.info("\n\nCreating model configuration...")
model_config_builder = ModelConfigurationBuilder(hyp.number_of_regions,
K=K_unscaled,
tau1=TAU1_DEF,
tau0=TAU0_DEF)
mcs_file = os.path.join(config.out.FOLDER_RES,
hyp.name + "_model_config_builder.h5")
writer.write_model_configuration_builder(model_config_builder,
mcs_file)
if test_write_read:
logger.info(
"Written and read model configuration services are identical?: "
+ str(
assert_equal_objects(
model_config_builder,
reader.read_model_configuration_builder(mcs_file),
logger=logger)))
# Fix healthy regions to default equilibria:
# model_configuration = \
# model_config_builder.build_model_from_E_hypothesis(hyp, head.connectivity.normalized_weights)
# Fix healthy regions to default x0s:
model_configuration = \
model_config_builder.build_model_from_hypothesis(hyp, head.connectivity.normalized_weights)
mc_path = os.path.join(config.out.FOLDER_RES,
hyp.name + "_ModelConfig.h5")
writer.write_model_configuration(model_configuration, mc_path)
if test_write_read:
logger.info(
"Written and read model configuration are identical?: " + str(
assert_equal_objects(model_configuration,
reader.read_model_configuration(
mc_path),
logger=logger)))
# Plot nullclines and equilibria of model configuration
plotter.plot_state_space(model_configuration,
"6d",
head.connectivity.region_labels,
special_idx=hyp.regions_disease_indices,
zmode="lin",
figure_name=hyp.name + "_StateSpace")
logger.info("\n\nRunning LSA...")
lsa_service = LSAService(eigen_vectors_number=1)
lsa_hypothesis = lsa_service.run_lsa(hyp, model_configuration)
lsa_path = os.path.join(config.out.FOLDER_RES,
lsa_hypothesis.name + "_LSA.h5")
lsa_config_path = os.path.join(config.out.FOLDER_RES,
lsa_hypothesis.name + "_LSAConfig.h5")
writer.write_hypothesis(lsa_hypothesis, lsa_path)
writer.write_lsa_service(lsa_service, lsa_config_path)
if test_write_read:
logger.info("Written and read LSA services are identical?: " + str(
assert_equal_objects(lsa_service,
reader.read_lsa_service(lsa_config_path),
logger=logger)))
logger.info(
"Written and read LSA hypotheses are identical (no input check)?: "
+ str(
assert_equal_objects(lsa_hypothesis,
reader.read_hypothesis(lsa_path),
logger=logger)))
plotter.plot_lsa(lsa_hypothesis,
model_configuration,
lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number,
head.connectivity.region_labels,
None,
lsa_service=lsa_service)
if pse_flag:
# --------------Parameter Search Exploration (PSE)-------------------------------
logger.info("\n\nRunning PSE LSA...")
pse_results = pse_from_lsa_hypothesis(
n_samples,
lsa_hypothesis,
head.connectivity.normalized_weights,
model_config_builder,
lsa_service,
head.connectivity.region_labels,
param_range=0.1,
global_coupling=[{
"indices": all_regions_indices
}],
healthy_regions_parameters=[{
"name": "x0_values",
"indices": healthy_indices
}],
logger=logger,
save_flag=True)[0]
plotter.plot_lsa(lsa_hypothesis, model_configuration,
lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number,
head.connectivity.region_labels, pse_results)
pse_lsa_path = os.path.join(
config.out.FOLDER_RES,
lsa_hypothesis.name + "_PSE_LSA_results.h5")
writer.write_dictionary(pse_results, pse_lsa_path)
if test_write_read:
logger.info(
"Written and read sensitivity analysis parameter search results are identical?: "
+ str(
assert_equal_objects(pse_results,
reader.read_dictionary(
pse_lsa_path),
logger=logger)))
if sa_pse_flag:
# --------------Sensitivity Analysis Parameter Search Exploration (PSE)-------------------------------
logger.info("\n\nrunning sensitivity analysis PSE LSA...")
sa_results, pse_sa_results = \
sensitivity_analysis_pse_from_lsa_hypothesis(n_samples, lsa_hypothesis,
head.connectivity.normalized_weights,
model_config_builder, lsa_service,
head.connectivity.region_labels,
method="sobol", param_range=0.1,
global_coupling=[{"indices": all_regions_indices,
"bounds": [0.0, 2 *
model_config_builder.K_unscaled[
0]]}],
healthy_regions_parameters=[
{"name": "x0_values", "indices": healthy_indices}],
config=config)
plotter.plot_lsa(lsa_hypothesis,
model_configuration,
lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number,
head.connectivity.region_labels,
pse_sa_results,
title="SA PSE Hypothesis Overview")
sa_pse_path = os.path.join(
config.out.FOLDER_RES,
lsa_hypothesis.name + "_SA_PSE_LSA_results.h5")
sa_lsa_path = os.path.join(
config.out.FOLDER_RES,
lsa_hypothesis.name + "_SA_LSA_results.h5")
writer.write_dictionary(pse_sa_results, sa_pse_path)
writer.write_dictionary(sa_results, sa_lsa_path)
if test_write_read:
logger.info(
"Written and read sensitivity analysis results are identical?: "
+ str(
assert_equal_objects(sa_results,
reader.read_dictionary(
sa_lsa_path),
logger=logger)))
logger.info(
"Written and read sensitivity analysis parameter search results are identical?: "
+ str(
assert_equal_objects(pse_sa_results,
reader.read_dictionary(
sa_pse_path),
logger=logger)))
if sim_flag:
# --------------------------Simulation preparations-----------------------------------
# If you choose model...
# Available models beyond the TVB Epileptor (they all encompass optional variations from the different papers):
# EpileptorDP: similar to the TVB Epileptor + optional variations,
# EpileptorDP2D: reduced 2D model, following Proix et all 2014 +optional variations,
# EpleptorDPrealistic: starting from the TVB Epileptor + optional variations, but:
# -x0, Iext1, Iext2, slope and K become noisy state variables,
# -Iext2 and slope are coupled to z, g, or z*g in order for spikes to appear before seizure,
# -correlated noise is also used
# We don't want any time delays for the moment
head.connectivity.tract_lengths *= config.simulator.USE_TIME_DELAYS_FLAG
sim_types = ensure_list(sim_type)
integrator = "HeunStochastic"
for sim_type in sim_types:
# ------------------------------Simulation--------------------------------------
logger.info(
"\n\nConfiguring simulation from model_configuration...")
sim_builder = SimulatorBuilder(config.simulator.MODE)
if isequal_string(sim_type, "realistic"):
model.tau0 = 60000.0
model.tau1 = 0.2
model.slope = 0.25
model.Iext2 = 0.45
model.pmode = np.array(
"z") # np.array("None") to opt out for feedback
sim_settings = \
sim_builder.set_fs(2048.0).set_fs_monitor(1024.0).set_simulated_period(60000).build_sim_settings()
sim_settings.noise_type = COLORED_NOISE
sim_settings.noise_ntau = 20
integrator = "Dop853Stochastic"
elif isequal_string(sim_type, "fitting"):
sim_settings = sim_builder.set_model_name("EpileptorDP2D").set_fs(2048.0).set_fs_monitor(2048.0).\
set_simulated_period(2000).build_sim_settings()
sim_settings.noise_intensity = 1e-5
model = sim_builder.generate_model_tvb(model_configuration)
model.tau0 = 300.0
model.tau1 = 0.5
elif isequal_string(sim_type, "reduced"):
sim_settings = sim_builder.set_model_name("EpileptorDP2D").set_fs(4096.0). \
set_simulated_period(1000).build_sim_settings()
model = sim_builder.generate_model_tvb(model_configuration)
elif isequal_string(sim_type, "paper"):
sim_builder.set_model_name("Epileptor")
sim_settings = sim_builder.build_sim_settings()
model = sim_builder.generate_model_tvb(model_configuration)
else:
sim_settings = sim_builder.build_sim_settings()
model = sim_builder.generate_model_tvb(model_configuration)
sim, sim_settings, model = \
sim_builder.build_simulator_TVB_from_model_sim_settings(model_configuration,head.connectivity,
model, sim_settings, integrator=integrator)
# Integrator and initial conditions initialization.
# By default initial condition is set right on the equilibrium point.
writer.write_simulator_model(
sim.model, sim.connectivity.number_of_regions,
os.path.join(config.out.FOLDER_RES,
lsa_hypothesis.name + "_sim_model.h5"))
logger.info("\n\nSimulating...")
sim_output, status = sim.launch_simulation(
report_every_n_monitor_steps=100)
sim_path = os.path.join(
config.out.FOLDER_RES,
lsa_hypothesis.name + "_sim_settings.h5")
writer.write_simulation_settings(sim.simulation_settings,
sim_path)
if test_write_read:
# TODO: find out why it cannot set monitor expressions
logger.info(
"Written and read simulation settings are identical?: "
+ str(
assert_equal_objects(
sim.simulation_settings,
reader.read_simulation_settings(sim_path),
logger=logger)))
if not status:
logger.warning("\nSimulation failed!")
else:
time = np.array(sim_output.time_line).astype("f")
logger.info("\n\nSimulated signal return shape: %s",
sim_output.shape)
logger.info("Time: %s - %s", time[0], time[-1])
logger.info("Values: %s - %s", sim_output.data.min(),
sim_output.data.max())
if not status:
logger.warning("\nSimulation failed!")
else:
sim_output, seeg = compute_seeg_and_write_ts_to_h5(
sim_output,
sim.model,
head.sensorsSEEG,
os.path.join(config.out.FOLDER_RES,
model._ui_name + "_ts.h5"),
seeg_gain_mode="lin",
hpf_flag=True,
hpf_low=10.0,
hpf_high=512.0)
# Plot results
plotter.plot_simulated_timeseries(
sim_output,
sim.model,
lsa_hypothesis.lsa_propagation_indices,
seeg_list=seeg,
spectral_raster_plot=False,
title_prefix=hyp.name,
spectral_options={"log_scale": True})
def main_vep(config=Config(), sim_type="default", test_write_read=False,
pse_flag=PSE_FLAG, sa_pse_flag=SA_PSE_FLAG, sim_flag=SIM_FLAG):
logger = initialize_logger(__name__, config.out.FOLDER_LOGS)
# -------------------------------Reading data-----------------------------------
reader = TVBReader() if config.input.IS_TVB_MODE else H5Reader()
writer = H5Writer()
logger.info("Reading from: " + config.input.HEAD)
head = reader.read_head(config.input.HEAD)
plotter = Plotter(config)
plotter.plot_head(head)
if test_write_read:
writer.write_head(head, os.path.join(config.out.FOLDER_RES, "Head"))
# --------------------------Hypothesis definition-----------------------------------
n_samples = 100
# # Manual definition of hypothesis...:
# x0_indices = [20]
# x0_values = [0.9]
# e_indices = [70]
# e_values = [0.9]
# disease_values = x0_values + e_values
# disease_indices = x0_indices + e_indices
# ...or reading a custom file:
hypo_builder = HypothesisBuilder(head.connectivity.number_of_regions, config=config).set_normalize(0.95)
# This is an example of Epileptogenicity Hypothesis: you give as ep all indices for values > 0
hyp_E = hypo_builder.build_hypothesis_from_file(EP_NAME, e_indices=[1, 3, 16, 25])
# print(hyp_E.string_regions_disease(head.connectivity.region_labels))
# This is an example of Excitability Hypothesis:
hyp_x0 = hypo_builder.build_hypothesis_from_file(EP_NAME)
# # This is an example of Mixed Hypothesis set manually by the user:
# x0_indices = [hyp_x0.x0_indices[-1]]
# x0_values = [hyp_x0.x0_values[-1]]
# e_indices = hyp_x0.x0_indices[0:-1].tolist()
# e_values = hyp_x0.x0_values[0:-1].tolist()
# hyp_x0_E = hypo_builder.set_x0_hypothesis(x0_indices, x0_values). \
# set_e_hypothesis(e_indices, e_values).build_hypothesis()
# This is an example of x0_values mixed Excitability and Epileptogenicity Hypothesis set from file:
all_regions_indices = np.array(range(head.number_of_regions))
healthy_indices = np.delete(all_regions_indices, hyp_E.x0_indices + hyp_E.e_indices).tolist()
hyp_x0_E = hypo_builder.build_hypothesis_from_file(EP_NAME, e_indices=[16, 25])
hypotheses = (hyp_x0_E, hyp_x0, hyp_E)
# --------------------------Simulation preparations-----------------------------------
# If you choose model...
# Available models beyond the TVB Epileptor (they all encompass optional variations from the different papers):
# EpileptorDP: similar to the TVB Epileptor + optional variations,
# EpileptorDP2D: reduced 2D model, following Proix et all 2014 +optional variations,
# EpleptorDPrealistic: starting from the TVB Epileptor + optional variations, but:
# -x0, Iext1, Iext2, slope and K become noisy state variables,
# -Iext2 and slope are coupled to z, g, or z*g in order for spikes to appear before seizure,
# -multiplicative correlated noise is also used
# We don't want any time delays for the moment
head.connectivity.tract_lengths *= config.simulator.USE_TIME_DELAYS_FLAG
sim_builder = SimulatorBuilder(config.simulator.MODE)
if isequal_string(sim_type, "realistic"):
sim_settings = sim_builder.set_model_name("EpileptorDPrealistic").set_simulated_period(50000).build_sim_settings()
sim_settings.noise_type = COLORED_NOISE
sim_settings.noise_ntau = 10
elif isequal_string(sim_type, "fitting"):
sim_settings = sim_builder.set_model_name("EpileptorDP2D").build_sim_settings()
sim_settings.noise_intensity = 1e-3
elif isequal_string(sim_type, "paper"):
sim_builder.set_model_name("Epileptor")
sim_settings = sim_builder.build_sim_settings()
else:
sim_settings = sim_builder.build_sim_settings()
# --------------------------Hypothesis and LSA-----------------------------------
for hyp in hypotheses:
logger.info("\n\nRunning hypothesis: " + hyp.name)
logger.info("\n\nCreating model configuration...")
builder = ModelConfigurationBuilder(hyp.number_of_regions)
mcs_file = os.path.join(config.out.FOLDER_RES, hyp.name + "_model_config_service.h5")
writer.write_model_configuration_builder(builder, mcs_file)
if test_write_read:
logger.info("Written and read model configuration services are identical?: " +
str(assert_equal_objects(builder, reader.read_model_configuration_builder(mcs_file),
logger=logger)))
if hyp.type == "Epileptogenicity":
model_configuration = builder.build_model_from_E_hypothesis(hyp, head.connectivity.normalized_weights)
else:
model_configuration = builder.build_model_from_hypothesis(hyp, head.connectivity.normalized_weights)
mc_path = os.path.join(config.out.FOLDER_RES, hyp.name + "_ModelConfig.h5")
writer.write_model_configuration(model_configuration, mc_path)
if test_write_read:
logger.info("Written and read model configuration are identical?: " +
str(assert_equal_objects(model_configuration, reader.read_model_configuration(mc_path),
logger=logger)))
# Plot nullclines and equilibria of model configuration
plotter.plot_state_space(model_configuration, "6d", head.connectivity.region_labels,
special_idx=hyp_x0.x0_indices + hyp_E.e_indices, zmode="lin",
figure_name=hyp.name + "_StateSpace")
logger.info("\n\nRunning LSA...")
lsa_service = LSAService(eigen_vectors_number=None, weighted_eigenvector_sum=True)
lsa_hypothesis = lsa_service.run_lsa(hyp, model_configuration)
lsa_path = os.path.join(config.out.FOLDER_RES, lsa_hypothesis.name + "_LSA.h5")
lsa_config_path = os.path.join(config.out.FOLDER_RES, lsa_hypothesis.name + "_LSAConfig.h5")
writer.write_hypothesis(lsa_hypothesis, lsa_path)
writer.write_lsa_service(lsa_service, lsa_config_path)
if test_write_read:
logger.info("Written and read LSA services are identical?: " +
str(assert_equal_objects(lsa_service, reader.read_lsa_service(lsa_config_path), logger=logger)))
logger.info("Written and read LSA hypotheses are identical (no input check)?: " +
str(assert_equal_objects(lsa_hypothesis, reader.read_hypothesis(lsa_path), logger=logger)))
plotter.plot_lsa(lsa_hypothesis, model_configuration, lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number, head.connectivity.region_labels, None)
if pse_flag:
# --------------Parameter Search Exploration (PSE)-------------------------------
logger.info("\n\nRunning PSE LSA...")
pse_results = pse_from_lsa_hypothesis(lsa_hypothesis,
head.connectivity.normalized_weights,
head.connectivity.region_labels,
n_samples, param_range=0.1,
global_coupling=[{"indices": all_regions_indices}],
healthy_regions_parameters=[
{"name": "x0_values", "indices": healthy_indices}],
model_configuration_builder=builder,
lsa_service=lsa_service, logger=logger, save_flag=True)[0]
plotter.plot_lsa(lsa_hypothesis, model_configuration, lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number, head.connectivity.region_labels, pse_results)
pse_lsa_path = os.path.join(config.out.FOLDER_RES, lsa_hypothesis.name + "_PSE_LSA_results.h5")
writer.write_dictionary(pse_results, pse_lsa_path)
if test_write_read:
logger.info("Written and read sensitivity analysis parameter search results are identical?: " +
str(assert_equal_objects(pse_results, reader.read_dictionary(pse_lsa_path), logger=logger)))
if sa_pse_flag:
# --------------Sensitivity Analysis Parameter Search Exploration (PSE)-------------------------------
logger.info("\n\nrunning sensitivity analysis PSE LSA...")
sa_results, pse_sa_results = \
sensitivity_analysis_pse_from_lsa_hypothesis(lsa_hypothesis,
head.connectivity.normalized_weights,
head.connectivity.region_labels,
n_samples, method="sobol", param_range=0.1,
global_coupling=[{"indices": all_regions_indices,
"bounds": [0.0, 2 *
builder.K_unscaled[
0]]}],
healthy_regions_parameters=[
{"name": "x0_values", "indices": healthy_indices}],
model_configuration_builder=builder,
lsa_service=lsa_service, config=config)
plotter.plot_lsa(lsa_hypothesis, model_configuration, lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number, head.connectivity.region_labels, pse_sa_results,
title="SA PSE Hypothesis Overview")
sa_pse_path = os.path.join(config.out.FOLDER_RES, lsa_hypothesis.name + "_SA_PSE_LSA_results.h5")
sa_lsa_path = os.path.join(config.out.FOLDER_RES, lsa_hypothesis.name + "_SA_LSA_results.h5")
writer.write_dictionary(pse_sa_results, sa_pse_path)
writer.write_dictionary(sa_results, sa_lsa_path)
if test_write_read:
logger.info("Written and read sensitivity analysis results are identical?: " +
str(assert_equal_objects(sa_results, reader.read_dictionary(sa_lsa_path), logger=logger)))
logger.info("Written and read sensitivity analysis parameter search results are identical?: " +
str(assert_equal_objects(pse_sa_results, reader.read_dictionary(sa_pse_path),
logger=logger)))
if sim_flag:
# ------------------------------Simulation--------------------------------------
logger.info("\n\nConfiguring simulation from model_configuration...")
model = sim_builder.generate_model(model_configuration)
if isequal_string(sim_type, "realistic"):
model.tau0 = 30000.0
model.tau1 = 0.2
model.slope = 0.25
elif isequal_string(sim_type, "fitting"):
model.tau0 = 10.0
model.tau1 = 0.5
sim, sim_settings, model = sim_builder.build_simulator_TVB_from_model_sim_settings(model_configuration,
head.connectivity, model, sim_settings)
# Integrator and initial conditions initialization.
# By default initial condition is set right on the equilibrium point.
writer.write_generic(sim.model, config.out.FOLDER_RES, lsa_hypothesis.name + "_sim_model.h5")
logger.info("\n\nSimulating...")
ttavg, tavg_data, status = sim.launch_simulation(report_every_n_monitor_steps=100)
sim_path = os.path.join(config.out.FOLDER_RES, lsa_hypothesis.name + "_sim_settings.h5")
writer.write_simulation_settings(sim.simulation_settings, sim_path)
if test_write_read:
# TODO: find out why it cannot set monitor expressions
logger.info("Written and read simulation settings are identical?: " +
str(assert_equal_objects(sim.simulation_settings,
reader.read_simulation_settings(sim_path), logger=logger)))
if not status:
logger.warning("\nSimulation failed!")
else:
time = np.array(ttavg, dtype='float32')
output_sampling_time = np.mean(np.diff(time))
tavg_data = tavg_data[:, :, :, 0]
logger.info("\n\nSimulated signal return shape: %s", tavg_data.shape)
logger.info("Time: %s - %s", time[0], time[-1])
logger.info("Values: %s - %s", tavg_data.min(), tavg_data.max())
# Variables of interest in a dictionary:
res_ts = prepare_vois_ts_dict(sim_settings.monitor_expressions, tavg_data)
res_ts['time'] = time
res_ts['time_units'] = 'msec'
res_ts = compute_seeg_and_write_ts_h5_file(config.out.FOLDER_RES, lsa_hypothesis.name + "_ts.h5",
sim.model, res_ts, output_sampling_time,
sim_settings.simulated_period,
hpf_flag=True, hpf_low=10.0, hpf_high=512.0,
sensors_list=head.sensorsSEEG)
# Plot results
if model._ui_name is "EpileptorDP2D":
spectral_raster_plot = False
trajectories_plot = True
else:
spectral_raster_plot = "lfp"
trajectories_plot = False
#TODO: plotting fails when spectral_raster_plot="lfp". Denis will fix this
plotter.plot_sim_results(sim.model, lsa_hypothesis.lsa_propagation_indices, res_ts,
head.sensorsSEEG, hpf_flag=True, trajectories_plot=trajectories_plot,
spectral_raster_plot=False, log_scale=True)
def main_cc_vep(config,
head_folder,
ep_name="clinical_hypothesis",
x0_indices=[],
pse_flag=False,
sim_flag=True):
if not (os.path.isdir(config.out.FOLDER_RES)):
os.mkdir(config.out.FOLDER_RES)
logger = initialize_logger(__name__, config.out.FOLDER_LOGS)
# -------------------------------Reading data-----------------------------------
reader = TVBReader() if config.input.IS_TVB_MODE else H5Reader()
writer = H5Writer()
logger.info("Reading from: %s", head_folder)
head = reader.read_head(head_folder)
plotter = Plotter(config)
plotter.plot_head(head)
# --------------------------Hypothesis definition-----------------------------------
hypo_builder = HypothesisBuilder(head.connectivity.number_of_regions)
all_regions_indices = np.array(range(head.number_of_regions))
# This is an example of Epileptogenicity Hypothesis:
hyp_E = hypo_builder.build_hypothesis_from_file(ep_name, x0_indices)
# This is an example of Excitability Hypothesis:
hyp_x0 = hypo_builder.build_hypothesis_from_file(ep_name)
disease_indices = hyp_E.e_indices + hyp_x0.x0_indices
healthy_indices = np.delete(all_regions_indices, disease_indices).tolist()
if len(x0_indices) > 0:
# This is an example of x0_values mixed Excitability and Epileptogenicity Hypothesis:
disease_values = reader.read_epileptogenicity(head_folder,
name=ep_name)
disease_values = disease_values.tolist()
x0_values = []
for ix0 in x0_indices:
ind = disease_indices.index(ix0)
del disease_indices[ind]
x0_values.append(disease_values.pop(ind))
e_indices = disease_indices
e_values = np.array(disease_values)
x0_values = np.array(x0_values)
hyp_x0_E = hypo_builder.set_x0_hypothesis(
x0_indices,
x0_values).set_e_hypothesis(e_indices,
e_values).build_hypothesis()
hypotheses = (hyp_E, hyp_x0, hyp_x0_E)
else:
hypotheses = (
hyp_E,
hyp_x0,
)
# --------------------------Hypothesis and LSA-----------------------------------
for hyp in hypotheses:
logger.info("Running hypothesis: %s", hyp.name)
logger.info("Creating model configuration...")
builder = ModelConfigurationBuilder(hyp.number_of_regions)
writer.write_model_configuration_builder(
builder,
os.path.join(config.out.FOLDER_RES, "model_config_service.h5"))
if hyp.type == "Epileptogenicity":
model_configuration = builder.build_model_from_E_hypothesis(
hyp, head.connectivity.normalized_weights)
else:
model_configuration = builder.build_model_from_hypothesis(
hyp, head.connectivity.normalized_weights)
writer.write_model_configuration(
model_configuration,
os.path.join(config.out.FOLDER_RES, "ModelConfiguration.h5"))
# Plot nullclines and equilibria of model configuration
plotter.plot_state_space(model_configuration,
region_labels=head.connectivity.region_labels,
special_idx=disease_indices,
model="2d",
zmode="lin",
figure_name=hyp.name + "_StateSpace")
logger.info("Running LSA...")
lsa_service = LSAService(eigen_vectors_number=None,
weighted_eigenvector_sum=True)
lsa_hypothesis = lsa_service.run_lsa(hyp, model_configuration)
writer.write_hypothesis(
lsa_hypothesis,
os.path.join(config.out.FOLDER_RES, lsa_hypothesis.name + ".h5"))
writer.write_lsa_service(
lsa_service,
os.path.join(config.out.FOLDER_RES, "lsa_config_service.h5"))
plotter.plot_lsa(lsa_hypothesis, model_configuration,
lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number,
head.connectivity.region_labels, None)
if pse_flag:
n_samples = 100
# --------------Parameter Search Exploration (PSE)-------------------------------
logger.info("Running PSE LSA...")
pse_results = pse_from_lsa_hypothesis(
lsa_hypothesis,
head.connectivity.normalized_weights,
head.connectivity.region_labels,
n_samples,
param_range=0.1,
global_coupling=[{
"indices": all_regions_indices
}],
healthy_regions_parameters=[{
"name": "x0_values",
"indices": healthy_indices
}],
model_configuration_builder=builder,
lsa_service=lsa_service,
save_flag=True,
folder_res=config.out.FOLDER_RES,
filename="PSE_LSA",
logger=logger)[0]
plotter.plot_lsa(lsa_hypothesis,
model_configuration,
lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number,
head.connectivity.region_labels,
pse_results,
title="Hypothesis PSE LSA Overview")
if sim_flag:
config.out.subfolder = "simulations"
for folder in (config.out.FOLDER_RES, config.out.FOLDER_FIGURES):
if not (os.path.isdir(folder)):
os.mkdir(folder)
dynamical_models = ["EpileptorDP2D", "EpileptorDPrealistic"]
for dynamical_model, sim_type in zip(dynamical_models,
["fitting", "realistic"]):
ts_file = None # os.path.join(sim_folder_res, dynamical_model + "_ts.h5")
vois_ts_dict = \
from_model_configuration_to_simulation(model_configuration, head, lsa_hypothesis,
sim_type=sim_type, dynamical_model=dynamical_model,
ts_file=ts_file, plot_flag=True, config=config)
def main_fit_sim_hyplsa(
stan_model_name="vep_sde_ins.stan",
empirical_file="",
observation_model=OBSERVATION_MODELS.SEEG_LOGPOWER.value,
sensors_lbls=[],
sensor_id=0,
times_on_off=[],
fitmethod="optimizing",
pse_flag=True,
fit_flag=True,
config=Config(),
**kwargs):
def path(name):
if len(name) > 0:
return base_path + "_" + name + ".h5"
else:
return base_path + ".h5"
# Prepare necessary services:
logger = initialize_logger(__name__, config.out.FOLDER_LOGS)
reader = H5Reader()
writer = H5Writer()
plotter = Plotter(config)
# Read head
logger.info("Reading from: " + config.input.HEAD)
head = reader.read_head(config.input.HEAD)
sensors = head.get_sensors_id(sensor_ids=sensor_id)
plotter.plot_head(head)
# Set hypotheses:
hypotheses = set_hypotheses(head, config)
# ------------------------------Stan model and service--------------------------------------
model_code_path = os.path.join(config.generic.PROBLSTC_MODELS_PATH,
stan_model_name + ".stan")
stan_service = CmdStanService(model_name=stan_model_name,
model_code_path=model_code_path,
fitmethod=fitmethod,
config=config)
stan_service.set_or_compile_model()
for hyp in hypotheses[:1]:
base_path = os.path.join(config.out.FOLDER_RES, hyp.name)
# Set model configuration and compute LSA
model_configuration, lsa_hypothesis, pse_results = \
set_model_config_LSA(head, hyp, reader, config, K_unscaled=3*K_DEF, tau1=TAU1_DEF, tau0=TAU0_DEF,
pse_flag=pse_flag, plotter=plotter, writer=writer)
# -------------------------- Get model_data and observation signals: -------------------------------------------
# Create model inversion service (stateless)
problstc_model_file = path("ProblstcModel")
model_data_file = path("ModelData")
target_data_file = path("TargetData")
if os.path.isfile(problstc_model_file) and os.path.isfile(
model_data_file) and os.path.isfile(target_data_file):
# Read existing probabilistic model and model data...
probabilistic_model = reader.read_probabilistic_model(
problstc_model_file)
model_data = stan_service.load_model_data_from_file(
model_data_path=model_data_file)
target_data = reader.read_timeseries(target_data_file)
else:
model_inversion = SDEModelInversionService()
# ...or generate a new probabilistic model and model data
probabilistic_model = \
SDEProbabilisticModelBuilder(model_name="vep_sde_ins.stan", model_config=model_configuration,
parameters=[XModes.X0MODE.value, "sigma_"+XModes.X0MODE.value,
"x1_init", "z_init", "tau1", # "tau0", "K",
"sigma", "dZt", "epsilon", "scale", "offset"], # "dX1t",
xmode=XModes.X0MODE.value, priors_mode=PriorsModes.NONINFORMATIVE.value,
sde_mode=SDE_MODES.NONCENTERED.value, observation_model=observation_model).\
generate_model()
# Update active model's active region nodes
e_values = pse_results.get("e_values_mean",
model_configuration.e_values)
lsa_propagation_strength = pse_results.get(
"lsa_propagation_strengths_mean",
lsa_hypothesis.lsa_propagation_strengths)
model_inversion.active_e_th = 0.2
probabilistic_model = \
model_inversion.update_active_regions(probabilistic_model, e_values=e_values,
lsa_propagation_strengths=lsa_propagation_strength, reset=True)
# Now some scripts for settting and preprocessing target signals:
if os.path.isfile(empirical_file):
probabilistic_model.target_data_type = TARGET_DATA_TYPE.EMPIRICAL.value
# -------------------------- Get empirical data (preprocess edf if necessary) --------------------------
signals = set_empirical_data(
empirical_file,
path("ts_empirical"),
head,
sensors_lbls,
sensor_id,
probabilistic_model.time_length,
times_on_off,
label_strip_fun=lambda s: s.split("POL ")[-1],
plotter=plotter,
title_prefix=hyp.name,
bipolar=False)
else:
# -------------------------- Get simulated data (simulate if necessary) -------------------------------
probabilistic_model.target_data_type = TARGET_DATA_TYPE.SYNTHETIC.value
signals, simulator = \
set_simulated_target_data(path("ts"), model_configuration, head, lsa_hypothesis, probabilistic_model,
sensor_id, sim_type="fitting", times_on_off=times_on_off, config=config,
plotter=plotter, title_prefix=hyp.name, bipolar=False, filter_flag=False,
envelope_flag=False, smooth_flag=False, **kwargs)
# -------------------------- Select and set target data from signals ---------------------------------------
if probabilistic_model.observation_model in OBSERVATION_MODELS.SEEG.value:
model_inversion.auto_selection = "correlation-power"
model_inversion.sensors_per_electrode = 2
target_data, probabilistic_model, gain_matrix = \
model_inversion.set_target_data_and_time(signals, probabilistic_model, head=head, sensors=sensors)
plotter.plot_probabilistic_model(probabilistic_model,
hyp.name + " Probabilistic Model")
plotter.plot_raster({'Target Signals': target_data.squeezed},
target_data.time_line,
time_units=target_data.time_unit,
title=hyp.name + ' Target Signals raster',
offset=0.1,
labels=target_data.space_labels)
plotter.plot_timeseries({'Target Signals': target_data.squeezed},
target_data.time_line,
time_units=target_data.time_unit,
title=hyp.name + ' Target Signals',
labels=target_data.space_labels)
writer.\
write_probabilistic_model(probabilistic_model, model_configuration.number_of_regions, problstc_model_file)
writer.write_timeseries(target_data, target_data_file)
# Construct the stan model data dict:
model_data = build_stan_model_data_dict(
probabilistic_model,
target_data.squeezed,
model_configuration.model_connectivity,
gain_matrix,
time=target_data.time_line)
# # ...or interface with INS stan models
# model_data = build_stan_model_data_dict_to_interface_ins(probabilistic_model, target_data.squeezed,
# model_configuration.model_connectivity, gain_matrix,
# time=target_data.time_line)
writer.write_dictionary(model_data, model_data_file)
# -------------------------- Fit and get estimates: ------------------------------------------------------------
n_chains_or_runs = 4
output_samples = max(int(np.round(1000.0 / n_chains_or_runs)), 500)
# Sampling (HMC)
num_samples = output_samples
num_warmup = 1000
max_depth = 12
delta = 0.9
# ADVI or optimization:
iter = 1000000
tol_rel_obj = 1e-6
if fitmethod.find("sampl") >= 0:
skip_samples = num_warmup
else:
skip_samples = 0
prob_model_name = probabilistic_model.name.split(".")[0]
if fit_flag:
estimates, samples, summary = stan_service.fit(
debug=0,
simulate=0,
model_data=model_data,
refresh=1,
n_chains_or_runs=n_chains_or_runs,
iter=iter,
tol_rel_obj=tol_rel_obj,
num_warmup=num_warmup,
num_samples=num_samples,
max_depth=max_depth,
delta=delta,
save_warmup=1,
plot_warmup=1,
**kwargs)
writer.write_generic(estimates, path(prob_model_name + "_FitEst"))
writer.write_generic(samples,
path(prob_model_name + "_FitSamples"))
if summary is not None:
writer.write_generic(summary,
path(prob_model_name + "_FitSummary"))
else:
estimates, samples, summary = stan_service.read_output()
if fitmethod.find("sampl") >= 0:
plotter.plot_HMC(samples,
figure_name=hyp.name + "-" + prob_model_name +
" HMC NUTS trace")
# Model comparison:
# scale_signal, offset_signal, time_scale, epsilon, sigma -> 5 (+ K = 6)
# x0[active] -> probabilistic_model.model.number_of_active_regions
# x1init[active], zinit[active] -> 2 * probabilistic_model.number_of_active_regions
# dZt[active, t] -> probabilistic_model.number_of_active_regions * (probabilistic_model.time_length-1)
number_of_total_params =\
5 + probabilistic_model.number_of_active_regions * (3 + (probabilistic_model.time_length-1))
info_crit = \
stan_service.compute_information_criteria(samples, number_of_total_params, skip_samples=skip_samples,
# parameters=["amplitude_star", "offset_star", "epsilon_star",
# "sigma_star", "time_scale_star", "x0_star",
# "x_init_star", "z_init_star", "z_eta_star"],
merge_chains_or_runs_flag=False)
writer.write_generic(info_crit, path(prob_model_name + "_InfoCrit"))
Rhat = stan_service.get_Rhat(summary)
# Interface backwards with INS stan models
# estimates, samples, Rhat, model_data = \
# convert_params_names_from_ins([estimates, samples, Rhat, model_data])
if fitmethod.find("opt") < 0:
stats = {"Rhat": Rhat}
else:
stats = None
# -------------------------- Plot fitting results: ------------------------------------------------------------
# if stan_service.fitmethod.find("opt") < 0:
plotter.plot_fit_results(
estimates,
samples,
model_data,
target_data,
probabilistic_model,
info_crit,
stats=stats,
pair_plot_params=["tau1", "sigma", "epsilon", "scale",
"offset"], # "K",
region_violin_params=["x0", "x1_init", "z_init"],
regions_labels=head.connectivity.region_labels,
skip_samples=skip_samples,
title_prefix=hyp.name + "-" + prob_model_name)
# -------------------------- Reconfigure model after fitting:---------------------------------------------------
for id_est, est in enumerate(ensure_list(estimates)):
K = est.get("K", model_configuration.K)
tau1 = est.get("tau1", model_configuration.tau1)
tau0 = est.get("tau0", model_configuration.tau0)
fit_model_configuration_builder = \
ModelConfigurationBuilder(hyp.number_of_regions, K=K * hyp.number_of_regions, tau1=tau1, tau0=tau0)
x0_values_fit = model_configuration.x0_values
x0_values_fit[probabilistic_model.active_regions] = \
fit_model_configuration_builder._compute_x0_values_from_x0_model(est['x0'])
hyp_fit = HypothesisBuilder().set_nr_of_regions(head.connectivity.number_of_regions).\
set_name('fit' + str(id_est+1) + "_" + hyp.name).\
set_x0_hypothesis(list(probabilistic_model.active_regions),
x0_values_fit[probabilistic_model.active_regions]).\
build_hypothesis()
base_path = os.path.join(config.out.FOLDER_RES, hyp_fit.name)
writer.write_hypothesis(hyp_fit, path(""))
model_configuration_fit = \
fit_model_configuration_builder.build_model_from_hypothesis(hyp_fit, # est["MC"]
model_configuration.model_connectivity)
writer.write_model_configuration(model_configuration_fit,
path("ModelConfig"))
# Plot nullclines and equilibria of model configuration
plotter.plot_state_space(
model_configuration_fit,
region_labels=head.connectivity.region_labels,
special_idx=probabilistic_model.active_regions,
model="6d",
zmode="lin",
figure_name=hyp_fit.name + "_Nullclines and equilibria")
logger.info("Done!")