Class ReturnData

Class Documentation

class amici::ReturnData

Stores all data to be returned by amici::runAmiciSimulation.

NOTE: multidimensional arrays are stored in row-major order (FORTRAN-style)

Public Functions

ReturnData() = default

default constructor

ReturnData(std::vector<realtype> ts, int np, int nk, int nx, int nx_solver, int nxtrue, int nx_solver_reinit, int ny, int nytrue, int nz, int nztrue, int ne, int nJ, int nplist, int nmaxevent, int nt, int newton_maxsteps, int nw, std::vector<ParameterScaling> pscale, SecondOrderMode o2mode, SensitivityOrder sensi, SensitivityMethod sensi_meth, RDataReporting rdrm, bool quadratic_llh)

ReturnData.

Parameters

ReturnData(Solver const &solver, const Model &model)

constructor that uses information from model and solver to appropriately initialize fields

Parameters

  • solver: solver instance

  • model: model instance

~ReturnData() = default
void processSimulationObjects(SteadystateProblem const *preeq, ForwardProblem const *fwd, BackwardProblem const *bwd, SteadystateProblem const *posteq, Model &model, Solver const &solver, ExpData const *edata)

constructor that uses information from model and solver to appropriately initialize fields

Parameters

  • preeq: simulated preequilibration problem, pass nullptr to ignore

  • fwd: simulated forward problem, pass nullptr to ignore

  • bwd: simulated backward problem, pass nullptr to ignore

  • posteq: simulated postequilibration problem, pass nullptr to ignore

  • model: matching model instance

  • solver: matching solver instance

  • edata: matching experimental data

Public Members

std::vector<realtype> ts

timepoints (dimension: nt)

std::vector<realtype> xdot

time derivative (dimension: nx)

std::vector<realtype> J

Jacobian of differential equation right hand side (dimension: nx x nx, row-major)

std::vector<realtype> w

w data from the model (recurring terms in xdot, for imported SBML models from python, this contains the flux vector) (dimensions: nt x nw, row major)

std::vector<realtype> z

event output (dimension: nmaxevent x nz, row-major)

std::vector<realtype> sigmaz

event output sigma standard deviation (dimension: nmaxevent x nz, row-major)

std::vector<realtype> sz

parameter derivative of event output (dimension: nmaxevent x nz, row-major)

std::vector<realtype> ssigmaz

parameter derivative of event output standard deviation (dimension: nmaxevent x nz, row-major)

std::vector<realtype> rz

event trigger output (dimension: nmaxevent x nz, row-major)

std::vector<realtype> srz

parameter derivative of event trigger output (dimension: nmaxevent x nz x nplist, row-major)

std::vector<realtype> s2rz

second order parameter derivative of event trigger output (dimension: nmaxevent x nztrue x nplist x nplist, row-major)

std::vector<realtype> x

state (dimension: nt x nx, row-major)

std::vector<realtype> sx

parameter derivative of state (dimension: nt x nplist x nx, row-major)

std::vector<realtype> y

observable (dimension: nt x ny, row-major)

std::vector<realtype> sigmay

observable standard deviation (dimension: nt x ny, row-major)

std::vector<realtype> sy

parameter derivative of observable (dimension: nt x nplist x ny, row-major)

std::vector<realtype> ssigmay

parameter derivative of observable standard deviation (dimension: nt x nplist x ny, row-major)

std::vector<realtype> res

observable (dimension: nt*ny, row-major)

std::vector<realtype> sres

parameter derivative of residual (dimension: nt*ny x nplist, row-major)

std::vector<realtype> FIM

fisher information matrix (dimension: nplist x nplist, row-major)

std::vector<int> numsteps

number of integration steps forward problem (dimension: nt)

std::vector<int> numstepsB

number of integration steps backward problem (dimension: nt)

std::vector<int> numrhsevals

number of right hand side evaluations forward problem (dimension: nt)

std::vector<int> numrhsevalsB

number of right hand side evaluations backward problem (dimension: nt)

std::vector<int> numerrtestfails

number of error test failures forward problem (dimension: nt)

std::vector<int> numerrtestfailsB

number of error test failures backward problem (dimension: nt)

std::vector<int> numnonlinsolvconvfails

number of linear solver convergence failures forward problem (dimension: nt)

std::vector<int> numnonlinsolvconvfailsB

number of linear solver convergence failures backward problem (dimension: nt)

std::vector<int> order

employed order forward problem (dimension: nt)

double cpu_time = 0.0

computation time of forward solve [ms]

double cpu_timeB = 0.0

computation time of backward solve [ms]

std::vector<SteadyStateStatus> preeq_status

flags indicating success of steady state solver (preequilibration)

double preeq_cpu_time = 0.0

computation time of the steady state solver [ms] (preequilibration)

double preeq_cpu_timeB = 0.0

computation time of the steady state solver of the backward problem [ms] (preequilibration)

std::vector<SteadyStateStatus> posteq_status

flags indicating success of steady state solver (postequilibration)

double posteq_cpu_time = 0.0

computation time of the steady state solver [ms] (postequilibration)

double posteq_cpu_timeB = 0.0

computation time of the steady state solver of the backward problem [ms] (postequilibration)

std::vector<int> preeq_numsteps

number of Newton steps for steady state problem (preequilibration) [newton, simulation, newton] (length = 3)

std::vector<int> preeq_numlinsteps

number of linear steps by Newton step for steady state problem. this will only be filled for iterative solvers (preequilibration) (length = newton_maxsteps * 2)

int preeq_numstepsB = 0

number of simulation steps for adjoint steady state problem (preequilibration) [== 0 if analytical solution worked, > 0 otherwise]

std::vector<int> posteq_numsteps

number of Newton steps for steady state problem (preequilibration) [newton, simulation, newton] (length = 3) (postequilibration)

std::vector<int> posteq_numlinsteps

number of linear steps by Newton step for steady state problem. this will only be filled for iterative solvers (postequilibration) (length = newton_maxsteps * 2)

int posteq_numstepsB = 0

number of simulation steps for adjoint steady state problem (postequilibration) [== 0 if analytical solution worked, > 0 otherwise]

realtype preeq_t = NAN

time when steadystate was reached via simulation (preequilibration)

realtype preeq_wrms = NAN

weighted root-mean-square of the rhs when steadystate was reached (preequilibration)

realtype posteq_t = NAN

time when steadystate was reached via simulation (postequilibration)

realtype posteq_wrms = NAN

weighted root-mean-square of the rhs when steadystate was reached (postequilibration)

std::vector<realtype> x0

initial state (dimension: nx)

std::vector<realtype> x_ss

preequilibration steady state found by Newton solver (dimension: nx)

std::vector<realtype> sx0

initial sensitivities (dimension: nplist x nx, row-major)

std::vector<realtype> sx_ss

preequilibration sensitivities found by Newton solver (dimension: nplist x nx, row-major)

realtype llh = 0.0

loglikelihood value

realtype chi2 = 0.0

chi2 value

std::vector<realtype> sllh

parameter derivative of loglikelihood (dimension: nplist)

std::vector<realtype> s2llh

second order parameter derivative of loglikelihood (dimension: (nJ-1) x nplist, row-major)

int status = 0

status code

int np = {0}

total number of model parameters

int nk = {0}

number of fixed parameters

int nx = {0}

number of states

int nx_solver = {0}

number of states with conservation laws applied

int nxtrue = {0}

number of states in the unaugmented system

int nx_solver_reinit = {0}

number of solver states to be reinitialized after preequilibration

int ny = {0}

number of observables

int nytrue = {0}

number of observables in the unaugmented system

int nz = {0}

number of event outputs

int nztrue = {0}

number of event outputs in the unaugmented system

int ne = {0}

number of events

int nJ = {0}

dimension of the augmented objective function for 2nd order ASA

int nplist = {0}

number of parameter for which sensitivities were requested

int nmaxevent = {0}

maximal number of occurring events (for every event type)

int nt = {0}

number of considered timepoints

int nw = {0}

number of columns in w

int newton_maxsteps = {0}

maximal number of newton iterations for steady state calculation

std::vector<ParameterScaling> pscale

scaling of parameterization (lin,log,log10)

SecondOrderMode o2mode = {SecondOrderMode::none}

flag indicating whether second order sensitivities were requested

SensitivityOrder sensi = {SensitivityOrder::none}

sensitivity order

SensitivityMethod sensi_meth = {SensitivityMethod::none}

sensitivity method

RDataReporting rdata_reporting = {RDataReporting::full}

reporting mode

Protected Functions

void initializeLikelihoodReporting(bool quadratic_llh)

initializes storage for likelihood reporting mode

Parameters

  • quadratic_llh: whether model defines a quadratic nllh and computing res, sres and FIM makes sense.

void initializeResidualReporting(bool enable_res)

initializes storage for residual reporting mode

Parameters

  • enable_res: wheter residuals are to be computed

void initializeFullReporting(bool enable_fim)

initializes storage for full reporting mode

Parameters

  • enable_fim: wheter FIM Hessian approximation is to be computed

void initializeObjectiveFunction(bool enable_chi2)

initialize values for chi2 and llh and derivatives

Parameters

  • enable_chi2: wheter chi2 values are to be computed

void processPreEquilibration(SteadystateProblem const &preeq, Model &model)

extracts data from a preequilibration steadystateproblem

Parameters

  • preeq: Steadystateproblem for preequilibration

  • model: Model instance to compute return values

void processPostEquilibration(SteadystateProblem const &posteq, Model &model, ExpData const *edata)

extracts data from a preequilibration steadystateproblem

Parameters

  • posteq: Steadystateproblem for postequilibration

  • model: Model instance to compute return values

  • edata: ExpData instance containing observable data

void processForwardProblem(ForwardProblem const &fwd, Model &model, ExpData const *edata)

extracts results from forward problem

Parameters

  • fwd: forward problem

  • model: model that was used for forward simulation

  • edata: ExpData instance containing observable data

void processBackwardProblem(ForwardProblem const &fwd, BackwardProblem const &bwd, SteadystateProblem const *preeq, Model &model)

extracts results from backward problem

Parameters

  • fwd: forward problem

  • bwd: backward problem

  • preeq: Steadystateproblem for preequilibration

  • model: model that was used for forward/backward simulation

void processSolver(Solver const &solver)

extracts results from solver

Parameters

  • solver: solver that was used for forward/backward simulation

template<class T>
void storeJacobianAndDerivativeInReturnData(T const &problem, Model &model)

Evaluates and stores the Jacobian and right hand side at final timepoint.

Parameters

  • problem: forward problem or steadystate problem

  • model: model that was used for forward/backward simulation

void readSimulationState(SimulationState const &state, Model &model)

sets member variables and model state according to provided simulation state

Parameters

  • state: simulation state provided by Problem

  • model: model that was used for forward/backward simulation

void fres(int it, Model &model, const ExpData &edata)

Residual function.

Parameters

  • it: time index

  • model: model that was used for forward/backward simulation

  • edata: ExpData instance containing observable data

void fchi2(int it)

Chi-squared function.

Parameters

  • it: time index

void fsres(int it, Model &model, const ExpData &edata)

Residual sensitivity function.

Parameters

  • it: time index

  • model: model that was used for forward/backward simulation

  • edata: ExpData instance containing observable data

void fFIM(int it, Model &model, const ExpData &edata)

Fisher information matrix function.

Parameters

  • it: time index

  • model: model that was used for forward/backward simulation

  • edata: ExpData instance containing observable data

void invalidate(int it_start)

Set likelihood, state variables, outputs and respective sensitivities to NaN (typically after integration failure)

Parameters

  • it_start: time index at which to start invalidating

void invalidateLLH()

Set likelihood and chi2 to NaN (typically after integration failure)

void invalidateSLLH()

Set likelihood sensitivities to NaN (typically after integration failure)

void applyChainRuleFactorToSimulationResults(const Model &model)

applies the chain rule to account for parameter transformation in the sensitivities of simulation results

Parameters

bool computingFSA() const

Checks whether forward sensitivity analysis is performed.

Return

boolean indicator

void getDataOutput(int it, Model &model, ExpData const *edata)

Extracts output information for data-points, expects that x_solver and sx_solver were were set appropriately.

Parameters

  • it: timepoint index

  • model: model that was used in forward solve

  • edata: ExpData instance carrying experimental data

void getDataSensisFSA(int it, Model &model, ExpData const *edata)

Extracts data information for forward sensitivity analysis, expects that x_solver and sx_solver were were set appropriately.

Parameters

  • it: index of current timepoint

  • model: model that was used in forward solve

  • edata: ExpData instance carrying experimental data

void getEventOutput(int iroot, realtype t, const std::vector<int> rootidx, Model &model, ExpData const *edata)

Extracts output information for events, expects that x_solver and sx_solver were were set appropriately.

Parameters

  • iroot: event index

  • t: event timepoint

  • rootidx: information about which roots fired (1 indicating fired, 0/-1 for not)

  • model: model that was used in forward solve

  • edata: ExpData instance carrying experimental data

void getEventSensisFSA(int iroot, int ie, realtype t, Model &model, ExpData const *edata)

Extracts event information for forward sensitivity analysis, expects that x_solver and sx_solver were set appropriately.

Parameters

  • iroot: event index

  • ie: index of event type

  • t: event timepoint

  • model: model that was used in forward solve

  • edata: ExpData instance carrying experimental data

void handleSx0Backward(const Model &model, SteadystateProblem const &preeq, AmiVector &xQB) const

Updates contribution to likelihood from quadratures (xQB), if preequilibration was run in adjoint mode.

Parameters

  • model: model that was used for forward/backward simulation

  • preeq: Steadystateproblem for preequilibration

  • xQB: vector with quadratures from adjoint computation

void handleSx0Forward(const Model &model, std::vector<realtype> &llhS0, AmiVector &xB) const

Updates contribution to likelihood for initial state sensitivities (llhS0), if no preequilibration was run or if forward sensitivities were used.

Parameters

  • model: model that was used for forward/backward simulation

  • llhS0: contribution to likelihood for initial state sensitivities

  • xB: vector with final adjoint state (exluding conservation laws)

Protected Attributes

realtype t_

timepoint for model evaluation

AmiVector x_solver_

partial state vector, excluding states eliminated from conservation laws

AmiVector dx_solver_

partial time derivative of state vector, excluding states eliminated from conservation laws

AmiVectorArray sx_solver_

partial sensitivity state vector array, excluding states eliminated from conservation laws

AmiVector x_rdata_

full state vector, including states eliminated from conservation laws

AmiVectorArray sx_rdata_

full sensitivity state vector array, including states eliminated from conservation laws

std::vector<int> nroots_

array of number of found roots for a certain event type (dimension: ne)

Friends

template<class Archive>
friend void serialize(Archive &ar, ReturnData &r, unsigned int version)

Serialize ReturnData (see boost::serialization::serialize)

Parameters

  • ar: Archive to serialize to

  • r: Data to serialize

  • version: Version number