Model#

class Config#

Bases: object

arbitrary_types_allowed = True#
smart_union = True#
validate_assignment = True#
class FisherModel(ode_x0: List | numpy.ndarray | eDPM.model.preprocessing.MultiVariableDefinition | NoneType, ode_t0: List[float] | numpy.ndarray | float | eDPM.model.preprocessing.VariableDefinition | NoneType, times: tuple | List[float] | List[List[float]] | numpy.ndarray | eDPM.model.preprocessing.VariableDefinition | NoneType, inputs: List, parameters: Tuple[float, ...], ode_fun: collections.abc.Callable, ode_dfdx: collections.abc.Callable, ode_dfdp: collections.abc.Callable, obs_fun: collections.abc.Callable | int | List[int] = None, obs_dgdx: collections.abc.Callable = None, obs_dgdp: collections.abc.Callable = None, ode_dfdx0: collections.abc.Callable = None, obs_dgdx0: collections.abc.Callable = None, ode_args: Any = None, identical_times: bool = False, covariance: dict | eDPM.model.preprocessing.CovarianceDefinition | NoneType = None)#

Bases: _FisherModelOptions, _FisherModelBase

classmethod all_observables_defined(values)#
classmethod validate_covariance(cov)#
classmethod validate_inputs(inp)#
classmethod validate_ode_t0(ode_t0)#
classmethod validate_ode_x0(ode_x0)#
classmethod validate_times(times)#
class FisherModelParametrized(ode_fun: collections.abc.Callable, ode_dfdx: collections.abc.Callable, ode_dfdp: collections.abc.Callable, variable_definitions: eDPM.model.fisher_model.FisherVariables, variable_values: eDPM.model.fisher_model.FisherVariables, obs_fun: collections.abc.Callable | int | List[int] = None, obs_dgdx: collections.abc.Callable = None, obs_dgdp: collections.abc.Callable = None, ode_dfdx0: collections.abc.Callable = None, obs_dgdx0: collections.abc.Callable = None, ode_args: Any = None, identical_times: bool = False, covariance: dict | eDPM.model.preprocessing.CovarianceDefinition | NoneType = None)#

Bases: _FisherModelParametrizedOptions, _FisherModelParametrizedBase

init_from()#

Initialize a parametrized FisherModel with initial guesses for the sampled variables.

Parameters:

fsm (FisherModel) – A user-defined fisher model.

Raises:

TypeError – Currently does not accept sampling over initial values ode_x0.

Returns:

Fully parametrized model with initial guesses which can be numerically solved.

Return type:

FisherModelParametrized

class FisherResultSingle(ode_x0: List | numpy.ndarray | eDPM.model.preprocessing.MultiVariableDefinition | NoneType, ode_t0: List[float] | numpy.ndarray | float | eDPM.model.preprocessing.VariableDefinition | NoneType, times: tuple | List[float] | List[List[float]] | numpy.ndarray | eDPM.model.preprocessing.VariableDefinition | NoneType, inputs: List, parameters: Tuple[float, ...], ode_solution: Any, sensitivities: numpy.ndarray, observables: numpy.ndarray, ode_args: Any = None, identical_times: bool = False, covariance: dict | eDPM.model.preprocessing.CovarianceDefinition | NoneType = None)#

Bases: _FisherResultSingleOptions, _FisherResultSingleBase

class FisherResults(ode_fun: collections.abc.Callable, ode_dfdx: collections.abc.Callable, ode_dfdp: collections.abc.Callable, variable_definitions: eDPM.model.fisher_model.FisherVariables, variable_values: eDPM.model.fisher_model.FisherVariables, criterion: float, S: numpy.ndarray, C: numpy.ndarray, criterion_fun: collections.abc.Callable, individual_results: list, relative_sensitivities: bool, obs_fun: collections.abc.Callable | int | List[int] = None, obs_dgdx: collections.abc.Callable = None, obs_dgdp: collections.abc.Callable = None, ode_dfdx0: collections.abc.Callable = None, obs_dgdx0: collections.abc.Callable = None, ode_args: Any = None, identical_times: bool = False, covariance: dict | eDPM.model.preprocessing.CovarianceDefinition | NoneType = None, penalty_discrete_summary: Any = None)#

Bases: _FisherResultsOptions, _FisherResultsBase

class FisherVariables(ode_x0: List | ndarray | MultiVariableDefinition | None, ode_t0: List[float] | ndarray | float | VariableDefinition | None, times: tuple | List[float] | List[List[float]] | ndarray | VariableDefinition | None, inputs: List, parameters: Tuple[float, ...], ode_args: Any = None, identical_times: bool = False, covariance: dict | CovarianceDefinition | None = None)#

Bases: _FisherVariablesOptions, _FisherVariablesBase

Contains all necessary and optional numerical values needed to fully specify the model. Note that it is not possible to directly use this class to numerically solve the model since a initial guess for the corresponding values needs to be made.

Parameters:

  • ode_x0 (float, List[float], List[List[float]]) – Initial values of the ODE.

  • ode_t0 (float, List[float]) – Initial time point of the ODE.

  • times – Time points at which the ODE should be evaluated.

  • _FisherVariablesOptions (_type_) –

list_to_list_of_vectors(ls: list) → List[ndarray]#
list_to_nparray_of_float(ls: list) → List[float]#
nparray_correct_shape_and_float(ls: ndarray) → List[float]#
nparray_to_list_of_vectors(npa: ndarray) → List[ndarray]#
times_nparray_to_correct_shape(times: ndarray) → ndarray#