def main_sensitivity_analysis(config=Config()):
# -------------------------------Reading data-----------------------------------
reader = Reader()
writer = H5Writer()
head = reader.read_head(config.input.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_indices = x0_indices + e_indices
n_disease = len(disease_indices)
n_x0 = len(x0_indices)
n_e = len(e_indices)
all_regions_indices = np.array(range(head.connectivity.number_of_regions))
healthy_indices = np.delete(all_regions_indices, disease_indices).tolist()
n_healthy = len(healthy_indices)
# This is an example of x0_values mixed Excitability and Epileptogenicity Hypothesis:
hyp_x0_E = HypothesisBuilder(
head.connectivity.number_of_regions).set_x0_hypothesis(
x0_indices,
x0_values).set_e_hypothesis(e_indices,
e_values).build_hypothesis()
# Now running the sensitivity analysis:
logger.info("running sensitivity analysis PSE LSA...")
for m in METHODS:
try:
model_configuration_builder, model_configuration, lsa_service, lsa_hypothesis, sa_results, pse_results = \
sensitivity_analysis_pse_from_hypothesis(hyp_x0_E,
head.connectivity.normalized_weights,
head.connectivity.region_labels,
n_samples, method=m, param_range=0.1,
global_coupling=[{"indices": all_regions_indices,
"low": 0.0, "high": 2 * K_DEF}],
healthy_regions_parameters=[
{"name": "x0_values", "indices": healthy_indices}],
config=config, save_services=True)
Plotter(config).plot_lsa(
lsa_hypothesis,
model_configuration,
lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number,
region_labels=head.connectivity.region_labels,
pse_results=pse_results,
title=m + "_PSE_LSA_overview_" + lsa_hypothesis.name,
lsa_service=lsa_service)
# , show_flag=True, save_flag=False
result_file = os.path.join(
config.out.FOLDER_RES,
m + "_PSE_LSA_results_" + lsa_hypothesis.name + ".h5")
writer.write_dictionary(pse_results, result_file)
result_file = os.path.join(
config.out.FOLDER_RES,
m + "_SA_LSA_results_" + lsa_hypothesis.name + ".h5")
writer.write_dictionary(sa_results, result_file)
except:
logger.warning("Method " + m + " failed!")
def main_pse(config=Config()):
# -------------------------------Reading data-----------------------------------
reader = Reader()
writer = H5Writer()
head = reader.read_head(config.input.HEAD)
logger = initialize_logger(__name__, config.out.FOLDER_LOGS)
# --------------------------Manual Hypothesis definition-----------------------------------
n_samples = 100
x0_indices = [20]
x0_values = [0.9]
e_indices = [70]
e_values = [0.9]
disease_indices = x0_indices + e_indices
n_disease = len(disease_indices)
n_x0 = len(x0_indices)
n_e = len(e_indices)
all_regions_indices = np.array(range(head.number_of_regions))
healthy_indices = np.delete(all_regions_indices, disease_indices).tolist()
n_healthy = len(healthy_indices)
# This is an example of x0_values mixed Excitability and Epileptogenicity Hypothesis:
hyp_x0_E = HypothesisBuilder(
head.connectivity.number_of_regions).set_x0_hypothesis(
x0_indices,
x0_values).set_e_hypothesis(e_indices,
e_values).build_hypothesis()
# Now running the parameter search analysis:
logger.info("running PSE LSA...")
model_config, lsa_service, lsa_hypothesis, pse_res = pse_from_hypothesis(
hyp_x0_E,
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
}],
save_services=True)[:4]
logger.info("Plotting LSA...")
Plotter(config).plot_lsa(lsa_hypothesis,
model_config,
lsa_service.weighted_eigenvector_sum,
lsa_service.eigen_vectors_number,
region_labels=head.connectivity.region_labels,
pse_results=pse_res,
lsa_service=lsa_service)
logger.info("Saving LSA results ...")
writer.write_dictionary(
pse_res,
os.path.join(config.out.FOLDER_RES,
lsa_hypothesis.name + "_PSE_LSA_results.h5"))
def set_empirical_data(empirical_file,
ts_file,
head,
sensors_lbls,
sensor_id=0,
seizure_length=SEIZURE_LENGTH,
times_on_off=[],
label_strip_fun=None,
plotter=None,
title_prefix="",
**kwargs):
try:
return H5Reader().read_timeseries(ts_file)
except:
# ... or preprocess empirical data for the first time:
if len(sensors_lbls) == 0:
sensors_lbls = head.get_sensors_id(sensor_ids=sensor_id).labels
signals = prepare_seeg_observable_from_mne_file(
empirical_file,
head.get_sensors_id(sensor_ids=sensor_id),
sensors_lbls,
seizure_length,
times_on_off,
label_strip_fun=label_strip_fun,
plotter=plotter,
title_prefix=title_prefix,
**kwargs)
H5Writer().write_timeseries(signals, ts_file)
return signals
def _prepare_dummy_head(self):
reader = H5Reader()
connectivity = reader.read_connectivity(
os.path.join(self.config.input.HEAD, "Connectivity.h5"))
cort_surface = Surface([], [])
seeg_sensors = Sensors(numpy.array(["sens1", "sens2"]),
numpy.array([[0, 0, 0], [0, 1, 0]]))
head = Head(connectivity, cort_surface, sensorsSEEG=seeg_sensors)
return head
def main_h5_model(config=Config()):
# -------------------------------Reading data-----------------------------------
reader = TVBReader() if config.input.IS_TVB_MODE else H5Reader()
writer = H5Writer()
logger.info("Reading from: %s", config.input.HEAD)
head = reader.read_head(config.input.HEAD)
empty_hypothesis = HypothesisBuilder().build_hypothesis()
x0_indices = [20]
x0_values = [0.9]
e_indices = [70]
e_values = [0.9]
hyp_x0_E = HypothesisBuilder(
head.connectivity.number_of_regions).set_x0_hypothesis(
x0_indices,
x0_values).set_e_hypothesis(e_indices,
e_values).build_hypothesis()
obj = {
"hyp_x0_E": hyp_x0_E,
"test_dict": {
"list0": [
"l00", 1, {
"d020": "a",
"d021":
[True, False, np.inf, np.nan, None, [], (), {}, ""]
}
]
}
}
logger.info("Original object: %s", obj)
logger.info("Writing object to h5 file...")
writer.write_generic(
obj, os.path.join(config.out.FOLDER_RES, "test_h5_model.h5"))
h5_model1 = read_h5_model(config.out.FOLDER_RES + "/test_h5_model.h5")
obj1 = h5_model1.convert_from_h5_model(deepcopy(obj))
if assert_equal_objects(obj, obj1):
logger.info("Read identical object: %s", obj1)
else:
logger.error("Comparison failed!: %s", obj1)
h5_model2 = read_h5_model(config.out.FOLDER_RES + "/test_h5_model.h5")
obj2 = h5_model2.convert_from_h5_model(
obj={"DiseaseHypothesis": empty_hypothesis})
if assert_equal_objects(obj, obj2):
logger.info("Read object as dictionary: %s", obj2)
else:
logger.error("Comparison failed!: %s", obj2)
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 load_model_data_from_file(self, reset_path=False, **kwargs):
model_data_path = kwargs.get("model_data_path", self.model_data_path)
if reset_path:
self.model_data_path = model_data_path
extension = model_data_path.split(".", -1)[-1]
if isequal_string(extension, "R"):
model_data = rload(model_data_path)
elif isequal_string(extension, "npy"):
model_data = np.load(model_data_path).item()
elif isequal_string(extension, "mat"):
model_data = loadmat(model_data_path)
elif isequal_string(extension, "pkl"):
with open(model_data_path, 'wb') as f:
model_data = pickle.load(f)
elif isequal_string(extension, "h5"):
model_data = H5Reader().read_dictionary(model_data_path)
else:
raise_not_implemented_error("model_data file (" + model_data_path +
") that are not one of (.R, .npy, .mat, .pkl) cannot be read!")
for key in model_data.keys():
if key[:3] == "EPI":
del model_data[key]
return model_data
def correlate_sensors(
empirical_file="/Users/lia.domide/Downloads/TRECempirical/110223B-EEX_0004.EEG.mat",
existing_ep_file="/WORK/episense/episense-root/trunk/demo-data/SensorsSEEG_125.h5"
):
data = loadmat(empirical_file)
desired_labels = [str(i).strip().lower() for i in data["channel_names"]]
reader = H5Reader()
labels, locations = reader.read_sensors_of_type(existing_ep_file, "SEEG")
new_labels = []
new_locations = []
ignored_indices = []
for i, label in enumerate(desired_labels):
if label not in labels:
print "Ignored channel", label
ignored_indices.append(i)
continue
idx = numpy.where(labels == label)
new_labels.append(label)
new_locations.append(locations[idx])
H5Writer().write_sensors(
Sensors(new_labels, new_locations),
os.path.join(os.path.dirname(PATIENT_VIRTUAL_HEAD),
"SensorsSEEG_" + str(len(labels)) + ".h5"))
return ignored_indices
class SimulatorJava(ABCSimulator):
"""
From a VEP Hypothesis, write a custom JSON simulation configuration.
To run a simulation, we can also open a GUI and import the resulted JSON file.
"""
logger = initialize_logger(__name__)
reader = H5Reader()
json_custom_config_file = "SimulationConfiguration.json"
def __init__(self, connectivity, model_configuration, simulation_settings):
self.model = None
self.simulation_settings = simulation_settings
self.model_configuration = model_configuration
self.connectivity = connectivity
self.head_path = os.path.dirname(self.connectivity.file_path)
self.json_config_path = os.path.join(self.head_path,
self.json_custom_config_file)
self.configure_model()
def get_vois(self):
return self.model.vois
@staticmethod
def _save_serialized(ep_full_config, result_path):
json_text = json.dumps(obj_to_dict(ep_full_config), indent=2)
result_file = open(result_path, 'w')
result_file.write(json_text)
result_file.close()
def config_simulation(self):
ep_settings = Settings(
integration_step=self.simulation_settings.integration_step,
noise_seed=self.simulation_settings.noise_seed,
simulated_period=self.simulation_settings.simulated_period,
downsampling_period=self.simulation_settings.
monitor_sampling_period)
if isinstance(self.simulation_settings.noise_intensity, (float, int)):
self.logger.info("Using uniform noise %s" %
self.simulation_settings.noise_intensity)
ep_settings.noise_intensity = self.simulation_settings.noise_intensity
elif len(self.simulation_settings.noise_intensity
) == JavaEpileptor._nvar:
self.logger.info("Using noise/voi %s" %
self.simulation_settings.noise_intensity)
ep_settings.set_voi_noise_dispersions(
self.simulation_settings.noise_intensity,
self.connectivity.number_of_regions)
elif len(
self.simulation_settings.noise_intensity
) == JavaEpileptor._nvar * self.connectivity.number_of_regions:
self.logger.info("Using node noise %s" %
self.simulation_settings.noise_intensity)
ep_settings.set_node_noise_dispersions(
self.simulation_settings.noise_intensity)
else:
self.logger.warning("Could not set noise %s" %
self.simulation_settings.noise_intensity)
json_model = self.prepare_epileptor_model_for_json(
self.connectivity.number_of_regions)
# TODO: history length has to be computed given the time delays (i.e., the tract lengths...)
# TODO: when dfun is implemented for JavaEpileptor, we can use commented lines with initial_conditions
# initial_conditions = self.prepare_initial_conditions(history_length=1)
# custom_config = FullConfiguration(connectivity_path=os.path.abspath(self.connectivity.file_path),
# epileptor_params=json_model, settings=ep_settings,
# initial_states=initial_conditions.flatten(),
# initial_states_shape=numpy.array(initial_conditions.shape))
custom_config = FullConfiguration(connectivity_path=os.path.abspath(
self.connectivity.file_path),
epileptor_params=json_model,
settings=ep_settings,
initial_states=None,
initial_states_shape=None)
self._save_serialized(custom_config, self.json_config_path)
def launch_simulation(self):
opts = "java -Dncsa.hdf.hdf5lib.H5.hdf5lib=" + os.path.join(GenericConfig.LIB_PATH, GenericConfig.HDF5_LIB) + \
" " + "-Djava.library.path=" + GenericConfig.LIB_PATH + " " + "-cp" + " " + GenericConfig.JAR_PATH + \
" " + GenericConfig.JAVA_MAIN_SIM + " " + os.path.abspath(self.json_config_path) + " " + \
os.path.abspath(self.head_path)
try:
status = subprocess.call(opts, shell=True)
print(status)
except:
status = False
self.logger.warning("Something went wrong with this simulation...")
time, data = self.reader.read_ts(
os.path.join(self.head_path, "full-configuration", "ts.h5"))
return time, data, status
def prepare_epileptor_model_for_json(self, no_regions=88):
epileptor_params_list = []
self.logger.warning("No of regions is " + str(no_regions))
for idx in xrange(no_regions):
epileptor_params_list.append(
EpileptorParams(self.model.a[idx], self.model.b[idx],
self.model.c[idx], self.model.d[idx],
self.model.aa[idx], self.model.r[idx],
self.model.Kvf[idx], self.model.Kf[idx],
self.model.Ks[idx], self.model.tau[idx],
self.model.Iext[idx], self.model.Iext2[idx],
self.model.slope[idx], self.model.x0[idx],
self.model.tt[idx]))
return epileptor_params_list
def configure_model(self):
x0 = calc_x0_val_to_model_x0(
self.model_configuration.x0_values, self.model_configuration.yc,
self.model_configuration.Iext1, self.model_configuration.a,
self.model_configuration.b - self.model_configuration.d)
self.model = JavaEpileptor(a=self.model_configuration.a,
b=self.model_configuration.b,
d=self.model_configuration.d,
x0=x0,
iext=self.model_configuration.Iext1,
ks=self.model_configuration.K,
c=self.model_configuration.yc,
tt=self.model_configuration.tau1,
r=1.0 / self.model_configuration.tau0)
def configure_initial_conditions(self, initial_conditions=None):
if isinstance(initial_conditions, numpy.ndarray):
self.initial_conditions = initial_conditions
else:
# TODO: have a function to calculate the correct history length when we have time delays
self.initial_conditions = self.prepare_initial_conditions(
history_length=1)
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 build_hypothesis_from_file(self, hyp_file, e_indices=None):
self.set_diseased_regions_values(H5Reader().read_epileptogenicity(self.config.input.HEAD, name=hyp_file))
if e_indices:
self.set_e_indices(e_indices)
return self.build_hypothesis()
attributes = ["areas", "normalized_weights", "weights", "tract_lengths"]
stats = dict((attribute, []) for attribute in attributes)
head = None
for ii in range(1, 31):
patient = "TVB" + str(ii)
logger.info("Patient TVB " + str(ii) + ":")
head_folder = os.path.join(base_folder, patient, "Head")
try:
if head is not None:
del head
head = H5Reader().read_head(head_folder)
except:
head = None
logger.warning("Failed reading head of patient TVB " + str(ii) +
"!")
if not os.path.isdir(head_folder):
logger.warning("Head folder of patient TVB " + str(ii) +
" is not an existing directory!")
continue
if head.number_of_regions == NUMBER_OF_REGIONS:
logger.info("Number of regions: " + str(head.number_of_regions))
else:
logger.warning("Excluding patient TVB " + str(ii) +
" because of a head of " +
str(head.number_of_regions) + " regions!")
for vector in data:
result.append(tuple(vector))
if len(data.shape) == 3:
for vector in data:
subresult = []
for subvector in vector:
subresult.append(tuple(subvector))
result.append(tuple(subresult))
return result
if __name__ == "__main__":
data, total_time, nr_of_steps, start_time = read_ts(
"/WORK/Episense/trunk/demo-data/Head_TREC/epHH/ts.h5")
conn = H5Reader().read_connectivity(
"/WORK/Episense/trunk/demo-data/Head_TREC/Connectivity.h5")
signal = Timeseries(
data,
OrderedDict({
TimeseriesDimensions.SPACE.value:
list(conn.region_labels),
TimeseriesDimensions.VARIABLES.value:
[PossibleVariables.X1.value, PossibleVariables.X2.value, "c"]
}), start_time, total_time / float(nr_of_steps))
timeline = signal.time_line
sv = signal.get_state_variable("x1")
subspace = signal.get_subspace_by_labels(list(conn.region_labels)[:3])
timewindow = signal.get_time_window(10, 100)
class TestCustomH5Reader(BaseTest):
reader = H5Reader()
writer = H5Writer()
in_head = InputConfig().HEAD
not_existent_file = "NotExistent.h5"
def test_read_connectivity(self):
connectivity = self.reader.read_connectivity(
os.path.join(self.in_head, "Connectivity.h5"))
assert connectivity is not None
assert connectivity.number_of_regions == 76
def test_read_surface(self):
surface = self.reader.read_surface(
os.path.join(self.in_head, "CorticalSurface.h5"))
assert surface is not None
assert surface.vertices.shape[0] == 16
def test_read_surface_not_existent_file(self):
surface = self.reader.read_surface(
os.path.join(self.in_head, self.not_existent_file))
assert surface is None
def test_read_region_mapping(self):
region_mapping = self.reader.read_region_mapping(
os.path.join(self.in_head, "RegionMapping.h5"))
assert isinstance(region_mapping, numpy.ndarray)
assert region_mapping.size == 16
def test_read_region_mapping_not_existent_file(self):
region_mapping = self.reader.read_region_mapping(
os.path.join(self.in_head, self.not_existent_file))
assert isinstance(region_mapping, numpy.ndarray)
assert region_mapping.size == 0
def test_read_volume_mapping(self):
volume_mapping = self.reader.read_volume_mapping(
os.path.join(self.in_head, "VolumeMapping.h5"))
assert isinstance(volume_mapping, numpy.ndarray)
assert volume_mapping.shape == (6, 5, 4)
def test_read_volume_mapping_not_existent_file(self):
volume_mapping = self.reader.read_volume_mapping(
os.path.join(self.in_head, self.not_existent_file))
assert isinstance(volume_mapping, numpy.ndarray)
assert volume_mapping.shape == (0, )
def test_sensors_of_type(self):
sensors_file = os.path.join(self.in_head, "SensorsSEEG_20.h5")
sensors = self.reader.read_sensors_of_type(sensors_file,
Sensors.TYPE_SEEG)
assert sensors is not None
assert sensors.number_of_sensors == 20
def test_sensors_of_type_not_existent_file(self):
sensors_file = os.path.join(self.in_head, self.not_existent_file)
sensors = self.reader.read_sensors_of_type(sensors_file,
Sensors.TYPE_SEEG)
assert sensors is None
def test_read_sensors(self):
sensors_seeg, sensors_eeg, sensors_meg = self.reader.read_sensors(
self.in_head)
assert len(sensors_seeg) > 0
assert len(sensors_eeg) == 0
assert len(sensors_meg) == 0
assert sensors_seeg[0] is not None
assert sensors_seeg[0].number_of_sensors == 20
def test_read_hypothesis(self):
test_file = os.path.join(self.config.out.FOLDER_TEMP,
"TestHypothesis.h5")
hypothesis_builder = HypothesisBuilder(3, self.config)
dummy_hypothesis = hypothesis_builder.set_e_hypothesis(
[0], [0.6]).build_hypothesis()
self.writer.write_hypothesis(dummy_hypothesis, test_file)
hypothesis = self.reader.read_hypothesis(test_file)
assert dummy_hypothesis.number_of_regions == hypothesis.number_of_regions
assert numpy.array_equal(dummy_hypothesis.x0_values,
hypothesis.x0_values)
assert dummy_hypothesis.x0_indices == hypothesis.x0_indices
assert numpy.array_equal(dummy_hypothesis.e_values,
hypothesis.e_values)
assert dummy_hypothesis.e_indices == hypothesis.e_indices
assert numpy.array_equal(dummy_hypothesis.w_values,
hypothesis.w_values)
assert dummy_hypothesis.w_indices == hypothesis.w_indices
assert numpy.array_equal(dummy_hypothesis.lsa_propagation_indices,
hypothesis.lsa_propagation_indices)
if len(dummy_hypothesis.lsa_propagation_indices) == 0:
assert numpy.array_equal([0, 0, 0],
hypothesis.lsa_propagation_strengths)
else:
assert numpy.array_equal(
dummy_hypothesis.lsa_propagation_strengths,
hypothesis.lsa_propagation_strengths)
def test_read_model_configuration(self):
test_file = os.path.join(self.config.out.FOLDER_TEMP,
"TestModelConfiguration.h5")
dummy_mc = ModelConfiguration(x1eq=numpy.array([2.0, 3.0, 1.0]),
zmode=None,
zeq=numpy.array([3.0, 2.0, 1.0]),
model_connectivity=numpy.array(
[[1.0, 2.0, 3.0], [1.0, 2.0, 3.0],
[2.0, 2.0, 2.0]]),
Ceq=numpy.array([1.0, 2.0, 3.0]))
self.writer.write_model_configuration(dummy_mc, test_file)
mc = self.reader.read_model_configuration(test_file)
assert numpy.array_equal(dummy_mc.x1eq, mc.x1eq)
assert numpy.array_equal(dummy_mc.zeq, mc.zeq)
assert numpy.array_equal(dummy_mc.Ceq, mc.Ceq)
assert numpy.array_equal(dummy_mc.model_connectivity,
mc.model_connectivity)
def test_read_lsa_service(self):
test_file = os.path.join(self.config.out.FOLDER_TEMP,
"TestLSAService.h5")
dummy_lsa_service = LSAService()
self.writer.write_lsa_service(dummy_lsa_service, test_file)
lsa_service = self.reader.read_lsa_service(test_file)
assert dummy_lsa_service.eigen_vectors_number_selection == lsa_service.eigen_vectors_number_selection
assert dummy_lsa_service.eigen_vectors_number == lsa_service.eigen_vectors_number
assert dummy_lsa_service.eigen_values == lsa_service.eigen_values
assert dummy_lsa_service.eigen_vectors == lsa_service.eigen_vectors
assert dummy_lsa_service.weighted_eigenvector_sum == lsa_service.weighted_eigenvector_sum
assert dummy_lsa_service.normalize_propagation_strength == lsa_service.normalize_propagation_strength
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 test_read_simulation_settigs(self):
test_file = os.path.join(self.config.out.FOLDER_TEMP,
"TestSimSettings.h5")
dummy_sim_settings = SimulationSettings()
self.writer.write_simulation_settings(dummy_sim_settings, test_file)
sim_settings = self.reader.read_simulation_settings(test_file)
assert dummy_sim_settings.integration_step == sim_settings.integration_step
assert dummy_sim_settings.simulated_period == sim_settings.simulated_period
assert dummy_sim_settings.integrator_type == sim_settings.integrator_type
assert dummy_sim_settings.noise_type == sim_settings.noise_type
assert dummy_sim_settings.noise_ntau == sim_settings.noise_ntau
assert dummy_sim_settings.noise_intensity == sim_settings.noise_intensity
assert dummy_sim_settings.noise_seed == sim_settings.noise_seed
assert dummy_sim_settings.monitor_type == sim_settings.monitor_type
assert dummy_sim_settings.monitor_sampling_period == sim_settings.monitor_sampling_period
assert dummy_sim_settings.monitor_expressions == sim_settings.monitor_expressions
assert numpy.array_equal(dummy_sim_settings.initial_conditions,
sim_settings.initial_conditions)
import os
from tvb_epilepsy.base.constants.config import Config
from tvb_epilepsy.base.utils.log_error_utils import initialize_logger
from tvb_epilepsy.io.tvb_data_reader import TVBReader
from tvb_epilepsy.io.h5_reader import H5Reader
from tvb_epilepsy.io.h5_writer import H5Writer
from tvb_epilepsy.plot.plotter import Plotter
# input_folder = os.path.join(os.path.expanduser("~"), 'Dropbox', 'Work', 'VBtech', 'VEP', "results", "CC", "TVB3", "tvb")
# head_folder = os.path.join(os.path.expanduser("~"), 'Dropbox', 'Work', 'VBtech', 'VEP', "results", "CC", "TVB3", "Head")
input_folder = os.path.join(os.path.expanduser("~"), 'Dropbox', 'Work',
'VBtech', 'VEP', "results", "INS", "JUNCH", "tvb")
head_folder = os.path.join(os.path.expanduser("~"), 'Dropbox', 'Work',
'VBtech', 'VEP', "results", "INS", "JUNCH", "Head")
output_folder = os.path.join(os.path.expanduser("~"), 'Dropbox', 'Work',
'VBtech', 'VEP', "results", "tests")
config = Config(head_folder=input_folder,
output_base=output_folder,
data_mode="tvb") #, data_mode="java"
config.hypothesis.head_folder = head_folder
config.figures.MATPLOTLIB_BACKEND = "inline"
config.figures.SHOW_FLAG = True
logger = initialize_logger(__name__, config.out.FOLDER_LOGS)
reader = TVBReader() if config.input.IS_TVB_MODE else H5Reader()
writer = H5Writer()
plotter = Plotter(config)
logger.info("Reading from: " + config.input.HEAD)
head = reader.read_head(config.input.HEAD,
seeg_sensors_files=[("seeg_xyz.txt", )])
print("OK!")
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})
user_home = os.path.expanduser("~")
head_folder = os.path.join(user_home, 'Dropbox', 'Work', 'VBtech', 'VEP', "results", "CC", "TVB3", "Head")
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"))
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 from_model_configuration_to_simulation(model_configuration,
head,
lsa_hypothesis,
sim_type="realistic",
ts_file=None,
seeg_gain_mode="lin",
hpf_flag=False,
hpf_low=10.0,
hpf_high=512.0,
config=Config(),
plotter=False):
# 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
# Optional variations:
# ------------------------------Simulation--------------------------------------
hypname = lsa_hypothesis.name.replace("_LSA", "")
logger.info("\n\nConfiguring simulation...")
if isequal_string(sim_type, "realistic"):
sim, sim_settings, dynamical_model = build_simulator_TVB_realistic(
model_configuration, head.connectivity)
elif isequal_string(sim_type, "fitting"):
sim, sim_settings, dynamical_model = build_simulator_TVB_fitting(
model_configuration, head.connectivity)
elif isequal_string(sim_type, "paper"):
sim, sim_settings, dynamical_model = build_simulator_TVB_paper(
model_configuration, head.connectivity)
else:
sim, sim_settings, dynamical_model = build_simulator_TVB_default(
model_configuration, head.connectivity)
writer = H5Writer()
writer.write_simulator_model(
sim.model, sim.connectivity.number_of_regions,
os.path.join(config.out.FOLDER_RES,
hypname + dynamical_model._ui_name + "_model.h5"))
sim_output = []
seeg = []
if ts_file is not None and os.path.isfile(ts_file):
logger.info(
"\n\nLoading previously simulated time series from file: " +
ts_file)
sim_output = H5Reader().read_timeseries(ts_file)
seeg = TimeseriesService().compute_seeg(sim_output.get_source(),
head.sensorsSEEG,
sum_mode=seeg_gain_mode)
else:
logger.info("\n\nSimulating...")
sim_output, status = sim.launch_simulation(
report_every_n_monitor_steps=100)
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])
sim_output, seeg = compute_seeg_and_write_ts_to_h5(
sim_output,
sim.model,
head.sensorsSEEG,
ts_file,
seeg_gain_mode=seeg_gain_mode,
hpf_flag=hpf_flag,
hpf_low=hpf_low,
hpf_high=hpf_high)
if plotter:
if not isinstance(plotter, Plotter):
plotter = Plotter(config)
# Plot results
plotter.plot_simulated_timeseries(
sim_output,
sim.model,
lsa_hypothesis.lsa_propagation_indices,
seeg_list=seeg,
spectral_raster_plot=False,
title_prefix=hypname,
spectral_options={"log_scale": True})
return {"source": sim_output, "seeg": seeg}, sim
def from_model_configuration_to_simulation(model_configuration,
head,
lsa_hypothesis,
sim_type="realistic",
dynamical_model="EpileptorDP2D",
ts_file=None,
plot_flag=True,
config=Config()):
# 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
# Optional variations:
if dynamical_model is "EpileptorDP2D":
spectral_raster_plot = False
trajectories_plot = True
else:
spectral_raster_plot = False # "lfp"
trajectories_plot = False
# ------------------------------Simulation--------------------------------------
logger.info("\n\nConfiguring simulation...")
if isequal_string(sim_type, "realistic"):
sim, sim_settings, dynamical_model = build_simulator_TVB_realistic(
model_configuration, head.connectivity)
elif isequal_string(sim_type, "fitting"):
sim, sim_settings, dynamical_model = build_simulator_TVB_fitting(
model_configuration, head.connectivity)
elif isequal_string(sim_type, "paper"):
sim, sim_settings, dynamical_model = build_simulator_TVB_paper(
model_configuration, head.connectivity)
else:
sim, sim_settings, dynamical_model = build_simulator_TVB_default(
model_configuration, head.connectivity)
writer = H5Writer()
writer.write_generic(sim.model, config.out.FOLDER_RES,
dynamical_model._ui_name + "_model.h5")
vois_ts_dict = {}
if ts_file is not None and os.path.isfile(ts_file):
logger.info("\n\nLoading previously simulated time series...")
vois_ts_dict = H5Reader().read_dictionary(ts_file)
else:
logger.info("\n\nSimulating...")
ttavg, tavg_data, status = sim.launch_simulation(
report_every_n_monitor_steps=100)
if not status:
logger.warning("\nSimulation failed!")
else:
time = np.array(ttavg, dtype='float32').flatten()
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:
vois_ts_dict = prepare_vois_ts_dict(
dynamical_model.variables_of_interest, tavg_data)
vois_ts_dict['time'] = time
vois_ts_dict['time_units'] = 'msec'
vois_ts_dict = compute_seeg_and_write_ts_h5_file(
config.out.FOLDER_RES,
dynamical_model._ui_name + "_ts.h5",
sim.model,
vois_ts_dict,
output_sampling_time,
sim_settings.simulated_period,
hpf_flag=True,
hpf_low=10.0,
hpf_high=512.0,
sensors_list=head.sensorsSEEG,
save_flag=True)
if isinstance(ts_file, basestring):
writer.write_dictionary(
vois_ts_dict,
os.path.join(os.path.dirname(ts_file),
os.path.basename(ts_file)))
if plot_flag and len(vois_ts_dict) > 0:
# Plot results
Plotter(config).plot_sim_results(
sim.model,
lsa_hypothesis.lsa_propagation_indices,
vois_ts_dict,
sensorsSEEG=head.sensorsSEEG,
hpf_flag=False,
trajectories_plot=trajectories_plot,
spectral_raster_plot=spectral_raster_plot,
log_scale=True,
region_labels=head.connectivity.region_labels)
return vois_ts_dict