def _prepare_model_for_simulation(connectivity):
hypothesis = HypothesisBuilder(connectivity.number_of_regions).set_e_hypothesis([1, 1],
[0, 10]).build_hypothesis()
model_configuration_builder = ModelConfigurationBuilder(connectivity.number_of_regions)
model_configuration = model_configuration_builder.build_model_from_hypothesis(hypothesis,
connectivity.normalized_weights)
return model_configuration
def from_hypothesis_to_model_config_lsa(hyp,
head,
eigen_vectors_number=None,
weighted_eigenvector_sum=True,
config=Config(),
save_flag=None,
plot_flag=None,
**kwargs):
logger.info("\n\nRunning hypothesis: " + hyp.name)
logger.info("\n\nCreating model configuration...")
if save_flag is None:
save_flag = config.figures.SAVE_FLAG
if plot_flag is None:
plot_flag = config.figures.SHOW_FLAG
builder = ModelConfigurationBuilder(hyp.number_of_regions, **kwargs)
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)
logger.info("\n\nRunning LSA...")
lsa_service = LSAService(eigen_vectors_number=eigen_vectors_number,
weighted_eigenvector_sum=weighted_eigenvector_sum)
lsa_hypothesis = lsa_service.run_lsa(hyp, model_configuration)
if save_flag:
writer = H5Writer()
path_mc = os.path.join(config.out.FOLDER_RES,
hyp.name + "_ModelConfig.h5")
writer.write_model_configuration(model_configuration, path_mc)
writer.write_hypothesis(
lsa_hypothesis,
os.path.join(config.out.FOLDER_RES, lsa_hypothesis.name + ".h5"))
if plot_flag:
plotter = Plotter(config)
# 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")
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)
return model_configuration, lsa_hypothesis, builder, lsa_service
def test_plot_sim_results(self):
lsa_hypothesis = HypothesisBuilder(
config=self.config).build_lsa_hypothesis()
mc = ModelConfigurationBuilder().build_model_from_E_hypothesis(
lsa_hypothesis, numpy.array([1]))
model = build_EpileptorDP2D(mc)
# TODO: this figure_name is constructed inside plot method, so it can change
figure_name = "Simulated_TAVG"
file_name = os.path.join(self.config.out.FOLDER_FIGURES,
figure_name + ".png")
assert not os.path.exists(file_name)
data_3D = numpy.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9], [0, 1, 2]],
[[3, 4, 5], [6, 7, 8], [9, 0, 1], [2, 3, 4]],
[[5, 6, 7], [8, 9, 0], [1, 2, 3], [4, 5, 6]]])
self.plotter.plot_simulated_timeseries(
Timeseries(
data_3D, {
TimeseriesDimensions.SPACE.value: ["r1", "r2", "r3", "r4"],
TimeseriesDimensions.VARIABLES.value: ["x1", "x2", "z"]
}, 0, 1), model, [0])
assert os.path.exists(file_name)
def test_read_model_configuration_builder(self):
test_file = os.path.join(self.config.out.FOLDER_TEMP,
"TestModelConfigService.h5")
dummy_mc_service = ModelConfigurationBuilder(3)
self.writer.write_model_configuration_builder(dummy_mc_service,
test_file)
mc_service = self.reader.read_model_configuration_builder(test_file)
assert dummy_mc_service.number_of_regions == mc_service.number_of_regions
assert numpy.array_equal(dummy_mc_service.x0_values,
mc_service.x0_values)
assert numpy.array_equal(dummy_mc_service.K_unscaled,
mc_service.K_unscaled)
assert numpy.array_equal(dummy_mc_service.e_values,
mc_service.e_values)
assert dummy_mc_service.yc == mc_service.yc
assert dummy_mc_service.Iext1 == mc_service.Iext1
assert dummy_mc_service.Iext2 == mc_service.Iext2
assert dummy_mc_service.a == mc_service.a
assert dummy_mc_service.b == mc_service.b
assert dummy_mc_service.d == mc_service.d
assert dummy_mc_service.slope == mc_service.slope
assert dummy_mc_service.s == mc_service.s
assert dummy_mc_service.gamma == mc_service.gamma
assert dummy_mc_service.tau1 == mc_service.tau1
assert dummy_mc_service.tau0 == mc_service.tau0
assert dummy_mc_service.zmode == mc_service.zmode
assert dummy_mc_service.x1eq_mode == mc_service.x1eq_mode
assert dummy_mc_service.K.all() == mc_service.K.all()
assert dummy_mc_service.x0cr == mc_service.x0cr
assert dummy_mc_service.rx0 == mc_service.rx0
def start_lsa_run(hypothesis, model_connectivity, config=Config()):
logger.info("creating model configuration...")
model_configuration_builder = ModelConfigurationBuilder(
hypothesis.number_of_regions)
model_configuration = model_configuration_builder.build_model_from_hypothesis(
hypothesis, model_connectivity)
logger.info("running LSA...")
lsa_service = LSAService(
eigen_vectors_number_selection=config.calcul.
EIGENVECTORS_NUMBER_SELECTION,
eigen_vectors_number=None,
weighted_eigenvector_sum=config.calcul.WEIGHTED_EIGENVECTOR_SUM,
normalize_propagation_strength=False)
lsa_hypothesis = lsa_service.run_lsa(hypothesis, model_configuration)
return model_configuration_builder, model_configuration, lsa_service, lsa_hypothesis
def test_write_model_configuration_builder(self):
test_file = os.path.join(self.config.out.FOLDER_TEMP, "TestModelConfigurationService.h5")
dummy_mc_service = ModelConfigurationBuilder(3)
assert not os.path.exists(test_file)
self.writer.write_model_configuration_builder(dummy_mc_service, test_file)
assert os.path.exists(test_file)
def test_plot_lsa(self):
figure_name = "LSAPlot"
hypo_builder = HypothesisBuilder(config=self.config).set_name(figure_name)
lsa_hypothesis = hypo_builder.build_lsa_hypothesis()
mc = ModelConfigurationBuilder().build_model_from_E_hypothesis(lsa_hypothesis, numpy.array([1]))
figure_file = os.path.join(self.config.out.FOLDER_FIGURES, figure_name + ".png")
assert not os.path.exists(figure_file)
self.plotter.plot_lsa(lsa_hypothesis, mc, True, None, region_labels=numpy.array(["a"]), title="")
assert not os.path.exists(figure_file)
def test_plot_state_space(self):
lsa_hypothesis = HypothesisBuilder(config=self.config).build_lsa_hypothesis()
mc = ModelConfigurationBuilder().build_model_from_E_hypothesis(lsa_hypothesis, numpy.array([1]))
model = "6d"
zmode = "lin"
# TODO: this figure_name is constructed inside plot method, so it can change
figure_name = "_" + "Epileptor_" + model + "_z-" + str(zmode)
file_name = os.path.join(self.config.out.FOLDER_FIGURES, figure_name + ".png")
assert not os.path.exists(file_name)
self.plotter.plot_state_space(mc, region_labels=numpy.array(["a"]), special_idx=[0], model=model, zmode=zmode,
figure_name="")
assert os.path.exists(file_name)
def test_plot_sim_results(self):
lsa_hypothesis = HypothesisBuilder(config=self.config).build_lsa_hypothesis()
mc = ModelConfigurationBuilder().build_model_from_E_hypothesis(lsa_hypothesis, numpy.array([1]))
model = build_EpileptorDP2D(mc)
res = prepare_vois_ts_dict(VOIS["EpileptorDP2D"], numpy.array([[[1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3]]]))
res['time'] = numpy.array([1, 2, 3])
res['time_units'] = 'msec'
# TODO: this figure_name is constructed inside plot method, so it can change
figure_name = "EpileptorDP2D_Simulated_TAVG"
file_name = os.path.join(self.config.out.FOLDER_FIGURES, figure_name + ".png")
assert not os.path.exists(file_name)
self.plotter.plot_sim_results(model, [0], res)
assert os.path.exists(file_name)
def read_model_configuration_builder(self, path):
"""
:param path: Path towards a ModelConfigurationService H5 file
:return: ModelConfigurationService object
"""
self.logger.info("Starting to read ModelConfigurationService from: %s" % path)
h5_file = h5py.File(path, 'r', libver='latest')
from tvb_epilepsy.service.model_configuration_builder import ModelConfigurationBuilder
mc_service = ModelConfigurationBuilder()
for dataset in h5_file.keys():
mc_service.set_attribute(dataset, h5_file["/" + dataset][()])
for attr in h5_file.attrs.keys():
mc_service.set_attribute(attr, h5_file.attrs[attr])
h5_file.close()
return mc_service
def update_hypo_model_config(
self,
hypothesis,
params,
conn_matrix,
model_config_service_input=None,
yc=YC_DEF,
Iext1=I_EXT1_DEF,
K=K_DEF,
a=A_DEF,
b=B_DEF,
tau1=TAU1_DEF,
tau0=TAU0_DEF,
x1eq_mode="optimize",
):
# Copy and update hypothesis
hypo_copy = deepcopy(hypothesis)
hypo_copy.update_for_pse(params, self.params_paths,
self.params_indices)
# Create a ModelConfigService and update it
if isinstance(model_config_service_input, ModelConfigurationBuilder):
model_configuration_builder = deepcopy(model_config_service_input)
else:
model_configuration_builder = ModelConfigurationBuilder(
hypo_copy.number_of_regions,
yc=yc,
Iext1=Iext1,
K=K,
a=a,
b=b,
tau1=tau1,
tau0=tau0,
x1eq_mode=x1eq_mode)
model_configuration_builder.set_attributes_from_pse(
params, self.params_paths, self.params_indices)
# Obtain Modelconfiguration
if hypo_copy.type == "Epileptogenicity":
model_configuration = model_configuration_builder.build_model_from_E_hypothesis(
hypo_copy, conn_matrix)
else:
model_configuration = model_configuration_builder.build_model_from_hypothesis(
hypo_copy, conn_matrix)
return hypo_copy, model_configuration
if user_home == "/home/denis":
output = os.path.join(user_home, 'Dropbox', 'Work', 'VBtech', 'VEP', "results", "INScluster")
config = Config(head_folder=head_folder, output_base=output, separate_by_run=False)
elif user_home == "/Users/lia.domide":
config = Config(head_folder="/WORK/episense/tvb-epilepsy/data/TVB3/Head",
raw_data_folder="/WORK/episense/tvb-epilepsy/data/TVB3/ts_seizure")
else:
output = os.path.join(user_home, 'Dropbox', 'Work', 'VBtech', 'VEP', "results", "fit")
config = Config(head_folder=head_folder, output_base=output, separate_by_run=False)
# Read head
reader = H5Reader()
head = reader.read_head(config.input.HEAD)
hyp_builder = HypothesisBuilder(head.connectivity.number_of_regions, config).set_normalize(0.99)
e_indices = [1, 26] # [1, 2, 25, 26]
hypothesis = hyp_builder.build_hypothesis_from_file("clinical_hypothesis_postseeg", e_indices)
model_configuration = \
ModelConfigurationBuilder(head.connectivity.number_of_regions).\
build_model_from_E_hypothesis(hypothesis, head.connectivity.normalized_weights)
statistical_model = SDEProbabilisticModelBuilder(model_config=model_configuration).generate_model()
H5Writer().write_probabilistic_model(statistical_model, config.out.FOLDER_RES, "TestStatsModelorig.h5")
statistical_model2 = reader.read_probabilistic_model(os.path.join(config.out.FOLDER_RES, "TestStatsModelorig.h5"))
H5Writer().write_probabilistic_model(statistical_model2, config.out.FOLDER_RES, "TestStatsModelread.h5")
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 set_model_config_LSA(head,
hyp,
reader,
config,
K_unscaled=K_DEF,
tau1=TAU1_DEF,
tau0=TAU0_DEF,
pse_flag=True,
plotter=None,
writer=None):
model_configuration_builder = None
lsa_service = None
# --------------------------Model configuration and LSA-----------------------------------
model_config_file = os.path.join(config.out.FOLDER_RES,
hyp.name + "_ModelConfig.h5")
hyp_file = os.path.join(config.out.FOLDER_RES, hyp.name + "_LSA.h5")
if os.path.isfile(hyp_file) and os.path.isfile(model_config_file):
# Read existing model configuration and LSA hypotheses...
model_configuration = reader.read_model_configuration(
model_config_file)
lsa_hypothesis = reader.read_hypothesis(hyp_file)
else:
# ...or generate new ones
model_configuration, lsa_hypothesis, model_configuration_builder, lsa_service = \
from_hypothesis_to_model_config_lsa(hyp, head, eigen_vectors_number=None, weighted_eigenvector_sum=True,
config=config, K=K_unscaled, tau1=tau1, tau0=tau0,
save_flag=True, plot_flag=True)
# --------------Parameter Search Exploration (PSE)-------------------------------
if pse_flag:
psa_lsa_file = os.path.join(config.out.FOLDER_RES,
hyp.name + "_PSE_LSA_results.h5")
try:
pse_results = reader.read_dictionary(psa_lsa_file)
except:
logger.info("\n\nRunning PSE LSA...")
n_samples = 100
all_regions_indices = np.array(range(head.number_of_regions))
disease_indices = lsa_hypothesis.regions_disease_indices
healthy_indices = np.delete(all_regions_indices,
disease_indices).tolist()
if model_configuration_builder is None:
model_configuration_builder = ModelConfigurationBuilder(
hyp.number_of_regions, K=K_unscaled)
if lsa_service is None:
lsa_service = LSAService(eigen_vectors_number=None,
weighted_eigenvector_sum=True)
pse_results = \
pse_from_lsa_hypothesis(n_samples, lsa_hypothesis, head.connectivity.normalized_weights,
model_configuration_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=False)[0]
if plotter:
plotter.plot_lsa(lsa_hypothesis, model_configuration,
lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number,
head.connectivity.region_labels, pse_results)
if writer:
writer.write_dictionary(pse_results, psa_lsa_file)
else:
pse_results = {}
return model_configuration, lsa_hypothesis, pse_results
