amici.adapters.fiddy

Adapters for using AMICI with the fiddy package for finite difference checks.

NOTE: Like fiddy, this module is experimental and subject to change.

Functions

fiddy_array_transpose(array, variable)

flatten(arrays)

rdata_array_transpose(array, variable)

reshape(array, structure[, sensitivities])

run_simulation_to_cached_functions(...[, ...])

Convert amici.run_simulation to fiddy functions.

simulate_petab_to_cached_functions(...[, ...])

Convert amici.petab.simulations.simulate_petab() (PEtab v1 simulations) to fiddy functions.

simulate_petab_v2_to_cached_functions(...[, ...])

Create fiddy functions for PetabSimulator.

transform_gradient_lin_to_lin(gradient_value, _)

transform_gradient_lin_to_log(...)

transform_gradient_lin_to_log10(...)

amici.adapters.fiddy.run_simulation_to_cached_functions(amici_model, *, cache=True, parameter_ids=None, amici_solver=None, amici_edata=None, derivative_variables=None)[source]

Convert amici.run_simulation to fiddy functions.

Parameters:

  • amici_model (amici.amici.Model | amici.amici.ModelPtr) – The AMICI model to simulate.

  • amici_solver (typing.Union[amici.amici.Solver, amici.amici.SolverPtr]) – The AMICI solver to use. If None, a new solver will be created from the model.

  • amici_edata (typing.Union[amici.amici.ExpData, amici.amici.ExpDataPtr]) – The AMICI ExpData to use. If None, no data will be used.

  • derivative_variables (list[str]) – The variables that derivatives will be computed or approximated for. See the keys of all_rdata_derivatives for options.

  • parameter_ids (list[str]) – The IDs that correspond to the values in the parameter vector that is simulated.

  • cache (bool) – Whether to cache the function calls.

Returns:

function, derivatives and structure

amici.adapters.fiddy.simulate_petab_to_cached_functions(petab_problem, amici_model, parameter_ids=None, cache=True, precreate_edatas=True, precreate_parameter_mapping=True, simulate_petab=None, **kwargs)[source]

Convert amici.petab.simulations.simulate_petab() (PEtab v1 simulations) to fiddy functions.

Note that all gradients are provided on linear scale. The correction from ‘log10’ scale is automatically done.

Parameters:

  • amici_model (amici.amici.Model) – The AMICI model to simulate.

  • simulate_petab (collections.abc.Callable[[typing.Any], str]) – A method to simulate PEtab problems with AMICI, e.g. amici.petab_objective.simulate_petab.

  • parameter_ids (list[str]) – The IDs of the parameters, in the order that parameter values will be supplied. Defaults to petab_problem.parameter_df.index.

  • petab_problem (petab.v1.problem.Problem) – The PEtab problem.

  • cache (bool) – Whether to cache the function call.

  • precreate_edatas (bool) – Whether to create the AMICI measurements object in advance, to save time.

  • precreate_parameter_mapping (bool) – Whether to create the AMICI parameter mapping object in advance, to save time.

  • kwargs – Passed to simulate_petab.

Return type:

tuple[collections.abc.Callable[[numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]], numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]], collections.abc.Callable[[numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]], numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]]]

Returns:

A tuple of:

  • 1: A method to compute the function at a point.

  • 2: A method to compute the gradient at a point.