def __init__(self, path):
super(TimeSeriesH5, self).__init__(path)
self.title = Scalar(TimeSeries.title)
self.data = DataSet(TimeSeries.data, expand_dimension=0)
self.nr_dimensions = Scalar(TimeSeries.nr_dimensions)
# omitted length_nd , these are indexing props, to be removed from datatype too
self.labels_ordering = Json(TimeSeries.labels_ordering)
self.labels_dimensions = Json(TimeSeries.labels_dimensions)
self.time = DataSet(TimeSeries.time, expand_dimension=0)
self.start_time = Scalar(TimeSeries.start_time)
self.sample_period = Scalar(TimeSeries.sample_period)
self.sample_period_unit = Scalar(TimeSeries.sample_period_unit)
self.sample_rate = Scalar(TimeSeries.sample_rate)
self._end_accessor_declarations()
# omitted has_surface_mapping, has_volume_mapping, indexing props, to be removed fro datatype too
# experiment: load header data eagerly, see surface for a lazy approach
# as we do not explicitly make a difference between opening for read or write
# the file might not yet exist, so loading headers makes no sense
if self.storage_manager.is_valid_hdf5_file():
self._sample_period = self.sample_period.load()
self._start_time = self.start_time.load()
def __init__(self, path):
super(TimeSeriesH5, self).__init__(path)
self.title = Scalar(TimeSeries.title, self)
self.data = DataSet(TimeSeries.data, self, expand_dimension=0)
self.nr_dimensions = Scalar(Int(), self, name="nr_dimensions")
# omitted length_nd , these are indexing props, to be removed from datatype too
self.labels_ordering = Json(TimeSeries.labels_ordering, self)
self.labels_dimensions = Json(TimeSeries.labels_dimensions, self)
self.time = DataSet(TimeSeries.time, self, expand_dimension=0)
self.start_time = Scalar(TimeSeries.start_time, self)
self.sample_period = Scalar(TimeSeries.sample_period, self)
self.sample_period_unit = Scalar(TimeSeries.sample_period_unit, self)
self.sample_rate = Scalar(Float(), self, name="sample_rate")
# omitted has_surface_mapping, has_volume_mapping, indexing props, to be removed fro datatype too
# experiment: load header data eagerly, see surface for a lazy approach
# as we do not explicitly make a difference between opening for read or write
# the file might not yet exist, so loading headers makes no sense
if not self.is_new_file:
self._sample_period = self.sample_period.load()
self._start_time = self.start_time.load()
def __init__(self, path):
super(SurfaceH5, self).__init__(path)
self.vertices = DataSet(Surface.vertices, self)
self.triangles = DataSet(Surface.triangles, self)
self.vertex_normals = DataSet(Surface.vertex_normals, self)
self.triangle_normals = DataSet(Surface.triangle_normals, self)
self.number_of_vertices = Scalar(Surface.number_of_vertices, self)
self.number_of_triangles = Scalar(Surface.number_of_triangles, self)
self.edge_mean_length = Scalar(Surface.edge_mean_length, self)
self.edge_min_length = Scalar(Surface.edge_min_length, self)
self.edge_max_length = Scalar(Surface.edge_max_length, self)
self.zero_based_triangles = Scalar(Surface.zero_based_triangles, self)
self.split_triangles = DataSet(NArray(dtype=int), self, name="split_triangles")
self.number_of_split_slices = Scalar(Int(), self, name="number_of_split_slices")
self.split_slices = Json(Attr(field_type=dict), self, name="split_slices")
self.bi_hemispheric = Scalar(Surface.bi_hemispheric, self)
self.surface_type = Scalar(Surface.surface_type, self)
self.valid_for_simulations = Scalar(Surface.valid_for_simulations, self)
# cached header like information, needed to interpret the rest of the file
# Load the data that is required in order to interpret the file format
# number_of_vertices and split_slices are needed for the get_vertices_slice read call
if not self.is_new_file:
self._split_slices = self.split_slices.load()
self._split_triangles = self.split_triangles.load()
self._number_of_vertices = self.number_of_vertices.load()
self._number_of_triangles = self.number_of_triangles.load()
self._number_of_split_slices = self.number_of_split_slices.load()
self._bi_hemispheric = self.bi_hemispheric.load()
class SimulatorH5(SimulatorConfigurationH5):
def __init__(self, path):
super(SimulatorH5, self).__init__(path)
self.connectivity = Reference(Simulator.connectivity, self)
self.conduction_speed = Scalar(Simulator.conduction_speed, self)
self.coupling = Reference(Simulator.coupling, self)
self.surface = Reference(Simulator.surface, self)
self.stimulus = Reference(Simulator.stimulus, self)
self.model = Reference(Simulator.model, self)
self.integrator = Reference(Simulator.integrator, self)
self.initial_conditions = DataSet(Simulator.initial_conditions, self)
self.monitors = Json(Simulator.monitors, self)
self.simulation_length = Scalar(Simulator.simulation_length, self)
self.simulation_state = Reference(Attr(field_type=uuid.UUID),
self,
name='simulation_state')
def store(self, datatype, scalars_only=False, store_references=False):
# type: (Simulator, bool, bool) -> None
self.gid.store(datatype.gid)
self.connectivity.store(datatype.connectivity)
self.conduction_speed.store(datatype.conduction_speed)
self.initial_conditions.store(datatype.initial_conditions)
self.simulation_length.store(datatype.simulation_length)
integrator_gid = self.store_config_as_reference(datatype.integrator)
self.integrator.store(integrator_gid)
coupling_gid = self.store_config_as_reference(datatype.coupling)
self.coupling.store(coupling_gid)
model_gid = self.store_config_as_reference(datatype.model)
self.model.store(model_gid)
# TODO: handle multiple monitors
monitor_gid = self.store_config_as_reference(datatype.monitors[0])
self.monitors.store([monitor_gid.hex])
if datatype.surface:
cortex_gid = self.store_config_as_reference(datatype.surface)
self.surface.store(cortex_gid)
if datatype.stimulus:
self.stimulus.store(datatype.stimulus)
self.type.store(self.get_full_class_name(type(datatype)))
def load_into(self, datatype):
# type: (Simulator) -> None
datatype.conduction_speed = self.conduction_speed.load()
datatype.initial_conditions = self.initial_conditions.load()
datatype.simulation_length = self.simulation_length.load()
datatype.integrator = self.load_from_reference(self.integrator.load())
datatype.coupling = self.load_from_reference(self.coupling.load())
datatype.model = self.load_from_reference(self.model.load())
# TODO: handle multiple monitors
datatype.monitors = [self.load_from_reference(self.monitors.load()[0])]
if self.surface.load():
datatype.surface = self.load_from_reference(self.surface.load())
def __init__(self, path):
super(SimulatorH5, self).__init__(path)
self.connectivity = Reference(Simulator.connectivity, self)
self.conduction_speed = Scalar(Simulator.conduction_speed, self)
self.coupling = Reference(Simulator.coupling, self)
self.surface = Reference(Simulator.surface, self)
self.stimulus = Reference(Simulator.stimulus, self)
self.model = Reference(Simulator.model, self)
self.integrator = Reference(Simulator.integrator, self)
self.initial_conditions = DataSet(Simulator.initial_conditions, self)
self.monitors = Json(Simulator.monitors, self)
self.simulation_length = Scalar(Simulator.simulation_length, self)
self.simulation_state = Reference(Attr(field_type=uuid.UUID),
self,
name='simulation_state')
def __init__(self, path):
super(EpileptorH5, self).__init__(path)
self.a = DataSet(Epileptor.a, self)
self.b = DataSet(Epileptor.b, self)
self.c = DataSet(Epileptor.c, self)
self.d = DataSet(Epileptor.d, self)
self.r = DataSet(Epileptor.r, self)
self.s = DataSet(Epileptor.s, self)
self.x0 = DataSet(Epileptor.x0, self)
self.Iext = DataSet(Epileptor.Iext, self)
self.slope = DataSet(Epileptor.slope, self)
self.Iext2 = DataSet(Epileptor.Iext2, self)
self.tau = DataSet(Epileptor.tau, self)
self.aa = DataSet(Epileptor.aa, self)
self.bb = DataSet(Epileptor.bb, self)
self.Kvf = DataSet(Epileptor.Kvf, self)
self.Kf = DataSet(Epileptor.Kf, self)
self.Ks = DataSet(Epileptor.Ks, self)
self.tt = DataSet(Epileptor.tt, self)
self.modification = DataSet(Epileptor.modification, self)
self.state_variable_range = JsonFinal(
Epileptor.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(Epileptor.variables_of_interest,
self)
def __init__(self, path):
super(ZetterbergJansenH5, self).__init__(path)
self.He = DataSet(ZetterbergJansen.He, self)
self.Hi = DataSet(ZetterbergJansen.Hi, self)
self.ke = DataSet(ZetterbergJansen.ke, self)
self.ki = DataSet(ZetterbergJansen.ki, self)
self.e0 = DataSet(ZetterbergJansen.e0, self)
self.rho_2 = DataSet(ZetterbergJansen.rho_2, self)
self.rho_1 = DataSet(ZetterbergJansen.rho_1, self)
self.gamma_1 = DataSet(ZetterbergJansen.gamma_1, self)
self.gamma_2 = DataSet(ZetterbergJansen.gamma_2, self)
self.gamma_3 = DataSet(ZetterbergJansen.gamma_3, self)
self.gamma_4 = DataSet(ZetterbergJansen.gamma_4, self)
self.gamma_5 = DataSet(ZetterbergJansen.gamma_5, self)
self.gamma_1T = DataSet(ZetterbergJansen.gamma_1T, self)
self.gamma_2T = DataSet(ZetterbergJansen.gamma_2T, self)
self.gamma_3T = DataSet(ZetterbergJansen.gamma_3T, self)
self.P = DataSet(ZetterbergJansen.P, self)
self.U = DataSet(ZetterbergJansen.U, self)
self.Q = DataSet(ZetterbergJansen.Q, self)
self.state_variable_range = JsonFinal(
ZetterbergJansen.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ZetterbergJansen.variables_of_interest, self)
def __init__(self, path):
super(EpileptorCodim3SlowModH5, self).__init__(path)
self.mu1_Ain = DataSet(EpileptorCodim3SlowMod.mu1_Ain, self)
self.mu2_Ain = DataSet(EpileptorCodim3SlowMod.mu2_Ain, self)
self.nu_Ain = DataSet(EpileptorCodim3SlowMod.nu_Ain, self)
self.mu1_Bin = DataSet(EpileptorCodim3SlowMod.mu1_Bin, self)
self.mu2_Bin = DataSet(EpileptorCodim3SlowMod.mu2_Bin, self)
self.nu_Bin = DataSet(EpileptorCodim3SlowMod.nu_Bin, self)
self.mu1_Aend = DataSet(EpileptorCodim3SlowMod.mu1_Aend, self)
self.mu2_Aend = DataSet(EpileptorCodim3SlowMod.mu2_Aend, self)
self.nu_Aend = DataSet(EpileptorCodim3SlowMod.nu_Aend, self)
self.mu1_Bend = DataSet(EpileptorCodim3SlowMod.mu1_Bend, self)
self.mu2_Bend = DataSet(EpileptorCodim3SlowMod.mu2_Bend, self)
self.nu_Bend = DataSet(EpileptorCodim3SlowMod.nu_Bend, self)
self.b = DataSet(EpileptorCodim3SlowMod.b, self)
self.R = DataSet(EpileptorCodim3SlowMod.R, self)
self.c = DataSet(EpileptorCodim3SlowMod.c, self)
self.cA = DataSet(EpileptorCodim3SlowMod.cA, self)
self.cB = DataSet(EpileptorCodim3SlowMod.cB, self)
self.dstar = DataSet(EpileptorCodim3SlowMod.dstar, self)
self.Ks = DataSet(EpileptorCodim3SlowMod.Ks, self)
self.N = DataSet(EpileptorCodim3SlowMod.N, self)
self.modification = DataSet(EpileptorCodim3SlowMod.modification, self)
self.state_variable_range = JsonFinal(
EpileptorCodim3SlowMod.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
EpileptorCodim3SlowMod.variables_of_interest, self)
def __init__(self, path):
super(DatatypeMeasureH5, self).__init__(path)
# Actual measure (dictionary Algorithm: single Value)
self.metrics = Json(DatatypeMeasure.metrics, self)
# DataType for which the measure was computed.
self.analyzed_datatype = Reference(DatatypeMeasure.analyzed_datatype,
self)
def __init__(self, path):
super(ReducedWongWangExcInhH5, self).__init__(path)
self.a_e = DataSet(ReducedWongWangExcInh.a_e, self)
self.b_e = DataSet(ReducedWongWangExcInh.b_e, self)
self.d_e = DataSet(ReducedWongWangExcInh.d_e, self)
self.gamma_e = DataSet(ReducedWongWangExcInh.gamma_e, self)
self.tau_e = DataSet(ReducedWongWangExcInh.tau_e, self)
self.w_p = DataSet(ReducedWongWangExcInh.w_p, self)
self.J_N = DataSet(ReducedWongWangExcInh.J_N, self)
self.W_e = DataSet(ReducedWongWangExcInh.W_e, self)
self.a_i = DataSet(ReducedWongWangExcInh.a_i, self)
self.b_i = DataSet(ReducedWongWangExcInh.b_i, self)
self.d_i = DataSet(ReducedWongWangExcInh.d_i, self)
self.gamma_i = DataSet(ReducedWongWangExcInh.gamma_i, self)
self.tau_i = DataSet(ReducedWongWangExcInh.tau_i, self)
self.J_i = DataSet(ReducedWongWangExcInh.J_i, self)
self.W_i = DataSet(ReducedWongWangExcInh.W_i, self)
self.I_o = DataSet(ReducedWongWangExcInh.I_o, self)
self.G = DataSet(ReducedWongWangExcInh.G, self)
self.lamda = DataSet(ReducedWongWangExcInh.lamda, self)
self.state_variable_range = JsonFinal(
ReducedWongWangExcInh.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ReducedWongWangExcInh.variables_of_interest, self)
def __init__(self, path):
super(WilsonCowanH5, self).__init__(path)
self.c_ee = DataSet(ModelsEnum.WILSON_COWAN.get_class().c_ee, self)
self.c_ie = DataSet(ModelsEnum.WILSON_COWAN.get_class().c_ie, self)
self.c_ei = DataSet(ModelsEnum.WILSON_COWAN.get_class().c_ei, self)
self.c_ii = DataSet(ModelsEnum.WILSON_COWAN.get_class().c_ii, self)
self.tau_e = DataSet(ModelsEnum.WILSON_COWAN.get_class().tau_e, self)
self.tau_i = DataSet(ModelsEnum.WILSON_COWAN.get_class().tau_i, self)
self.a_e = DataSet(ModelsEnum.WILSON_COWAN.get_class().a_e, self)
self.b_e = DataSet(ModelsEnum.WILSON_COWAN.get_class().b_e, self)
self.c_e = DataSet(ModelsEnum.WILSON_COWAN.get_class().c_e, self)
self.theta_e = DataSet(ModelsEnum.WILSON_COWAN.get_class().theta_e,
self)
self.a_i = DataSet(ModelsEnum.WILSON_COWAN.get_class().a_i, self)
self.b_i = DataSet(ModelsEnum.WILSON_COWAN.get_class().b_i, self)
self.theta_i = DataSet(ModelsEnum.WILSON_COWAN.get_class().theta_i,
self)
self.c_i = DataSet(ModelsEnum.WILSON_COWAN.get_class().c_i, self)
self.r_e = DataSet(ModelsEnum.WILSON_COWAN.get_class().r_e, self)
self.r_i = DataSet(ModelsEnum.WILSON_COWAN.get_class().r_i, self)
self.k_e = DataSet(ModelsEnum.WILSON_COWAN.get_class().k_e, self)
self.k_i = DataSet(ModelsEnum.WILSON_COWAN.get_class().k_i, self)
self.P = DataSet(ModelsEnum.WILSON_COWAN.get_class().P, self)
self.Q = DataSet(ModelsEnum.WILSON_COWAN.get_class().Q, self)
self.alpha_e = DataSet(ModelsEnum.WILSON_COWAN.get_class().alpha_e,
self)
self.alpha_i = DataSet(ModelsEnum.WILSON_COWAN.get_class().alpha_i,
self)
self.state_variable_range = JsonFinal(
ModelsEnum.WILSON_COWAN.get_class().state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.WILSON_COWAN.get_class().variables_of_interest, self)
def __init__(self, path):
super(ReducedSetHindmarshRoseH5, self).__init__(path)
self.r = DataSet(ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().r,
self)
self.a = DataSet(ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().a,
self)
self.b = DataSet(ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().b,
self)
self.c = DataSet(ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().c,
self)
self.d = DataSet(ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().d,
self)
self.s = DataSet(ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().s,
self)
self.xo = DataSet(ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().xo,
self)
self.K11 = DataSet(
ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().K11, self)
self.K12 = DataSet(
ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().K12, self)
self.K21 = DataSet(
ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().K21, self)
self.sigma = DataSet(
ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().sigma, self)
self.mu = DataSet(ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().mu,
self)
self.state_variable_range = JsonFinal(
ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class(
).state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.REDUCED_SET_HINDMARSH_ROSE.get_class().
variables_of_interest, self)
def __init__(self, path):
super(ReducedSetFitzHughNagumoH5, self).__init__(path)
self.tau = DataSet(
ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().tau, self)
self.a = DataSet(ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().a,
self)
self.b = DataSet(ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().b,
self)
self.K11 = DataSet(
ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().K11, self)
self.K12 = DataSet(
ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().K12, self)
self.K21 = DataSet(
ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().K21, self)
self.sigma = DataSet(
ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().sigma, self)
self.mu = DataSet(
ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().mu, self)
self.state_variable_range = JsonFinal(
ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class(
).state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.REDUCED_SET_FITZ_HUGH_NAGUMO.get_class().
variables_of_interest, self)
def __init__(self, path):
super(Generic2dOscillatorH5, self).__init__(path)
self.tau = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().tau,
self)
self.I = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().I, self)
self.a = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().a, self)
self.b = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().b, self)
self.c = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().c, self)
self.d = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().d, self)
self.e = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().e, self)
self.f = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().f, self)
self.g = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().g, self)
self.alpha = DataSet(
ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().alpha, self)
self.beta = DataSet(ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().beta,
self)
self.gamma = DataSet(
ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().gamma, self)
self.state_variable_range = JsonFinal(
ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.GENERIC_2D_OSCILLATOR.get_class().variables_of_interest,
self)
def __init__(self, path):
super(DatatypeMeasureH5, self).__init__(path)
# Actual measure (dictionary Algorithm: single Value)
self.metrics = Json(Attr(str), self, name='metrics')
# DataType for which the measure was computed.
self.analyzed_datatype = Reference(Attr(field_type=TimeSeries), self,
"analyzed_datatype")
def __init__(self, path):
super(EpileptorCodim3H5, self).__init__(path)
self.mu1_start = DataSet(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().mu1_start, self)
self.mu2_start = DataSet(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().mu2_start, self)
self.nu_start = DataSet(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().nu_start, self)
self.mu1_stop = DataSet(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().mu1_stop, self)
self.mu2_stop = DataSet(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().mu2_stop, self)
self.nu_stop = DataSet(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().nu_stop, self)
self.b = DataSet(ModelsEnum.EPILEPTOR_CODIM_3.get_class().b, self)
self.R = DataSet(ModelsEnum.EPILEPTOR_CODIM_3.get_class().R, self)
self.c = DataSet(ModelsEnum.EPILEPTOR_CODIM_3.get_class().c, self)
self.dstar = DataSet(ModelsEnum.EPILEPTOR_CODIM_3.get_class().dstar,
self)
self.Ks = DataSet(ModelsEnum.EPILEPTOR_CODIM_3.get_class().Ks, self)
self.N = DataSet(ModelsEnum.EPILEPTOR_CODIM_3.get_class().N, self)
self.modification = DataSet(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().modification, self)
self.state_variable_range = JsonFinal(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.EPILEPTOR_CODIM_3.get_class().variables_of_interest,
self)
def __init__(self, path):
super(WilsonCowanH5, self).__init__(path)
self.c_ee = DataSet(WilsonCowan.c_ee, self)
self.c_ie = DataSet(WilsonCowan.c_ie, self)
self.c_ei = DataSet(WilsonCowan.c_ei, self)
self.c_ii = DataSet(WilsonCowan.c_ii, self)
self.tau_e = DataSet(WilsonCowan.tau_e, self)
self.tau_i = DataSet(WilsonCowan.tau_i, self)
self.a_e = DataSet(WilsonCowan.a_e, self)
self.b_e = DataSet(WilsonCowan.b_e, self)
self.c_e = DataSet(WilsonCowan.c_e, self)
self.theta_e = DataSet(WilsonCowan.theta_e, self)
self.a_i = DataSet(WilsonCowan.a_i, self)
self.b_i = DataSet(WilsonCowan.b_i, self)
self.theta_i = DataSet(WilsonCowan.theta_i, self)
self.c_i = DataSet(WilsonCowan.c_i, self)
self.r_e = DataSet(WilsonCowan.r_e, self)
self.r_i = DataSet(WilsonCowan.r_i, self)
self.k_e = DataSet(WilsonCowan.k_e, self)
self.k_i = DataSet(WilsonCowan.k_i, self)
self.P = DataSet(WilsonCowan.P, self)
self.Q = DataSet(WilsonCowan.Q, self)
self.alpha_e = DataSet(WilsonCowan.alpha_e, self)
self.alpha_i = DataSet(WilsonCowan.alpha_i, self)
self.state_variable_range = JsonFinal(
WilsonCowan.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(WilsonCowan.variables_of_interest,
self)
def __init__(self, path):
super(EpileptorH5, self).__init__(path)
self.a = DataSet(ModelsEnum.EPILEPTOR.get_class().a, self)
self.b = DataSet(ModelsEnum.EPILEPTOR.get_class().b, self)
self.c = DataSet(ModelsEnum.EPILEPTOR.get_class().c, self)
self.d = DataSet(ModelsEnum.EPILEPTOR.get_class().d, self)
self.r = DataSet(ModelsEnum.EPILEPTOR.get_class().r, self)
self.s = DataSet(ModelsEnum.EPILEPTOR.get_class().s, self)
self.x0 = DataSet(ModelsEnum.EPILEPTOR.get_class().x0, self)
self.Iext = DataSet(ModelsEnum.EPILEPTOR.get_class().Iext, self)
self.slope = DataSet(ModelsEnum.EPILEPTOR.get_class().slope, self)
self.Iext2 = DataSet(ModelsEnum.EPILEPTOR.get_class().Iext2, self)
self.tau = DataSet(ModelsEnum.EPILEPTOR.get_class().tau, self)
self.aa = DataSet(ModelsEnum.EPILEPTOR.get_class().aa, self)
self.bb = DataSet(ModelsEnum.EPILEPTOR.get_class().bb, self)
self.Kvf = DataSet(ModelsEnum.EPILEPTOR.get_class().Kvf, self)
self.Kf = DataSet(ModelsEnum.EPILEPTOR.get_class().Kf, self)
self.Ks = DataSet(ModelsEnum.EPILEPTOR.get_class().Ks, self)
self.tt = DataSet(ModelsEnum.EPILEPTOR.get_class().tt, self)
self.modification = DataSet(
ModelsEnum.EPILEPTOR.get_class().modification, self)
self.state_variable_range = JsonFinal(
ModelsEnum.EPILEPTOR.get_class().state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.EPILEPTOR.get_class().variables_of_interest, self)
def __init__(self, path):
super(ZerlautFirstOrderH5, self).__init__(path)
self.g_L = DataSet(ZerlautFirstOrder.g_L, self)
self.E_L_e = DataSet(ZerlautFirstOrder.E_L_e, self)
self.E_L_i = DataSet(ZerlautFirstOrder.E_L_i, self)
self.C_m = DataSet(ZerlautFirstOrder.C_m, self)
self.b = DataSet(ZerlautFirstOrder.b, self)
self.tau_w = DataSet(ZerlautFirstOrder.tau_w, self)
self.E_e = DataSet(ZerlautFirstOrder.E_e, self)
self.E_i = DataSet(ZerlautFirstOrder.E_i, self)
self.Q_e = DataSet(ZerlautFirstOrder.Q_e, self)
self.Q_i = DataSet(ZerlautFirstOrder.Q_i, self)
self.tau_e = DataSet(ZerlautFirstOrder.tau_e, self)
self.tau_i = DataSet(ZerlautFirstOrder.tau_i, self)
self.N_tot = DataSet(ZerlautFirstOrder.N_tot, self)
self.p_connect = DataSet(ZerlautFirstOrder.p_connect, self)
self.g = DataSet(ZerlautFirstOrder.g, self)
self.T = DataSet(ZerlautFirstOrder.T, self)
self.P_e = DataSet(ZerlautFirstOrder.P_e, self)
self.P_i = DataSet(ZerlautFirstOrder.P_i, self)
self.external_input = DataSet(ZerlautFirstOrder.external_input, self)
self.state_variable_range = JsonFinal(
ZerlautFirstOrder.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ZerlautFirstOrder.variables_of_interest, self)
def __init__(self, path):
super(TimeSeriesRegionH5, self).__init__(path)
self.connectivity = Reference(TimeSeriesRegion.connectivity, self)
self.region_mapping_volume = Reference(
TimeSeriesRegion.region_mapping_volume, self)
self.region_mapping = Reference(TimeSeriesRegion.region_mapping, self)
self.labels_ordering = Json(TimeSeriesRegion.labels_ordering, self)
def __init__(self, path):
super(FcdH5, self).__init__(path)
self.array_data = DataSet(Fcd.array_data, self)
self.source = Reference(Fcd.source, self)
self.sw = Scalar(Fcd.sw, self)
self.sp = Scalar(Fcd.sp, self)
self.labels_ordering = Json(Fcd.labels_ordering, self)
def __init__(self, path):
super(CrossCorrelationH5, self).__init__(path)
self.array_data = DataSet(CrossCorrelation.array_data,
self,
expand_dimension=3)
self.source = Reference(CrossCorrelation.source, self)
self.time = DataSet(CrossCorrelation.time, self)
self.labels_ordering = Json(CrossCorrelation.labels_ordering, self)
def __init__(self, path):
super(TimeSeriesRegionH5, self).__init__(path)
self.connectivity = Reference(TimeSeriesRegion.connectivity)
self.region_mapping_volume = Reference(
TimeSeriesRegion.region_mapping_volume)
self.region_mapping = Reference(TimeSeriesRegion.region_mapping)
self.labels_ordering = Json(TimeSeriesRegion.labels_ordering)
self._end_accessor_declarations()
def __init__(self, path):
super(KuramotoH5, self).__init__(path)
self.omega = DataSet(Kuramoto.omega, self)
self.state_variable_range = JsonFinal(
Kuramoto.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(Kuramoto.variables_of_interest, self)
def __init__(self, path):
super(LinearH5, self).__init__(path)
self.gamma = DataSet(Linear.gamma, self)
self.state_variable_range = JsonFinal(
Linear.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(Linear.variables_of_interest, self)
def __init__(self, path):
super(ZerlautSecondOrderH5, self).__init__(path)
self.state_variable_range = JsonFinal(
ZerlautSecondOrder.state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ZerlautSecondOrder.variables_of_interest, self)
def __init__(self, path):
super(LinearH5, self).__init__(path)
self.gamma = DataSet(ModelsEnum.LINEAR.get_class().gamma, self)
self.state_variable_range = JsonFinal(
ModelsEnum.LINEAR.get_class().state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.LINEAR.get_class().variables_of_interest, self)
def __init__(self, path):
super(KuramotoH5, self).__init__(path)
self.omega = DataSet(ModelsEnum.KURAMOTO.get_class().omega, self)
self.state_variable_range = JsonFinal(
ModelsEnum.KURAMOTO.get_class().state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.KURAMOTO.get_class().variables_of_interest, self)
def __init__(self, path):
super(ZerlautAdaptationSecondOrderH5, self).__init__(path)
self.state_variable_range = JsonFinal(
ModelsEnum.ZERLAUT_SECOND_ORDER.get_class().state_variable_range,
self,
json_encoder=StateVariablesEncoder,
json_decoder=StateVariablesDecoder)
self.variables_of_interest = Json(
ModelsEnum.ZERLAUT_SECOND_ORDER.get_class().variables_of_interest,
self)
def __init__(self, path):
super(ProjectionMatrixH5, self).__init__(path)
self.projection_type = Scalar(ProjectionMatrix.projection_type, self)
self.brain_skull = Reference(ProjectionMatrix.brain_skull, self)
self.skull_skin = Reference(ProjectionMatrix.skull_skin, self)
self.skin_air = Reference(ProjectionMatrix.skin_air, self)
self.conductances = Json(ProjectionMatrix.conductances, self)
self.sources = Reference(ProjectionMatrix.sources, self)
self.sensors = Reference(ProjectionMatrix.sensors, self)
self.projection_data = DataSet(ProjectionMatrix.projection_data, self)
