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)