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