Class Model

Defined in File model.h

Inheritance Relationships

Base Types

public amici::AbstractModel (Class AbstractModel)
public amici::ModelDimensions (Struct ModelDimensions)

Derived Types

public amici::Model_DAE (Class Model_DAE)
public amici::Model_ODE (Class Model_ODE)

Class Documentation

class Model : public amici::AbstractModel, public amici::ModelDimensions 

The Model class represents an AMICI ODE/DAE model.

The model can compute various model related quantities based on symbolically generated code.

Subclassed by amici::Model_DAE, amici::Model_ODE

Public Functions

Model() = default: Default constructor

Model(ModelDimensions const &model_dimensions, SimulationParameters simulation_parameters, amici::SecondOrderMode o2mode, std::vector<amici::realtype> idlist, std::vector<int> z2event, bool pythonGenerated = false, int ndxdotdp_explicit = 0, int ndxdotdx_explicit = 0, int w_recursion_depth = 0, std::map<realtype, std::vector<int>> state_independent_events = {})

Constructor with model dimensions.

Parameters:

model_dimensions – Model dimensions
simulation_parameters – Simulation parameters
o2mode – Second order sensitivity mode
idlist – Indexes indicating algebraic components (DAE only)
z2event – Mapping of event outputs to events
pythonGenerated – Flag indicating matlab or python wrapping
ndxdotdp_explicit – Number of nonzero elements in dxdotdp_explicit
ndxdotdx_explicit – Number of nonzero elements in dxdotdx_explicit
w_recursion_depth – Recursion depth of fw
state_independent_events – Map of events with state-independent triggers functions, mapping trigger timepoints to event indices.

~Model() override = default: Destructor.

Model &operator=(Model const &other) = delete

Copy assignment is disabled until const members are removed.

Parameters:: other – Object to copy from
Returns:

virtual Model *clone() const = 0

Clone this instance.

Returns:: The clone

void initialize(AmiVector &x, AmiVector &dx, AmiVectorArray &sx, AmiVectorArray &sdx, bool computeSensitivities, std::vector<int> &roots_found)

Initialize model properties.

Parameters:

x – Reference to state variables
dx – Reference to time derivative of states (DAE only)
sx – Reference to state variable sensitivities
sdx – Reference to time derivative of state sensitivities (DAE only)
computeSensitivities – Flag indicating whether sensitivities are to be computed
roots_found – boolean indicators indicating whether roots were found at t0 by this fun

void reinitialize(realtype t, AmiVector &x, AmiVectorArray &sx, bool computeSensitivities)

Re-initialize model properties after changing simulation context.

Parameters:

t – Timepoint
x – Reference to state variables
sx – Reference to state variable sensitivities
computeSensitivities – Flag indicating whether sensitivities are to be computed

void initializeB(AmiVector &xB, AmiVector &dxB, AmiVector &xQB, bool posteq) const

Initialize model properties.

Parameters:

xB – Adjoint state variables
dxB – Time derivative of adjoint states (DAE only)
xQB – Adjoint quadratures
posteq – Flag indicating whether postequilibration was performed

void initializeStates(AmiVector &x)

Initialize initial states.

Parameters:: x – State vector to be initialized

void initializeStateSensitivities(AmiVectorArray &sx, AmiVector const &x)

Initialize initial state sensitivities.

Parameters:

sx – Reference to state variable sensitivities
x – Reference to state variables

void initializeSplines(): Initialization of spline functions.

void initializeSplineSensitivities(): Initialization of spline sensitivity functions.

void initEvents(AmiVector const &x, AmiVector const &dx, std::vector<int> &roots_found)

Initialize the Heaviside variables h at the initial time t0.

Heaviside variables activate/deactivate on event occurrences.

Parameters:

x – Reference to state variables
dx – Reference to time derivative of states (DAE only)
roots_found – boolean indicators indicating whether roots were found at t0 by this fun

int nplist() const

Get number of parameters wrt to which sensitivities are computed.

Returns:: Length of sensitivity index vector

int np() const

Get total number of model parameters.

Returns:: Length of parameter vector

int nk() const

Get number of constants.

Returns:: Length of constant vector

int ncl() const

Get number of conservation laws.

Returns:: Number of conservation laws (i.e., difference between nx_rdata and nx_solver).

int nx_reinit() const

Get number of solver states subject to reinitialization.

Returns:: Model member nx_solver_reinit

double const *k() const

Get fixed parameters.

Returns:: Pointer to constants array

int nMaxEvent() const

Get maximum number of events that may occur for each type.

Returns:: Maximum number of events that may occur for each type

void setNMaxEvent(int nmaxevent)

Set maximum number of events that may occur for each type.

Parameters:: nmaxevent – Maximum number of events that may occur for each type

int nt() const

Get number of timepoints.

Returns:: Number of timepoints

std::vector<ParameterScaling> const &getParameterScale() const

Get parameter scale for each parameter.

Returns:: Vector of parameter scales

void setParameterScale(ParameterScaling pscale)

Set parameter scale for each parameter.

NOTE: Resets initial state sensitivities.

Parameters:: pscale – Scalar parameter scale to be set for all parameters

void setParameterScale(std::vector<ParameterScaling> const &pscaleVec)

Set parameter scale for each parameter.

NOTE: Resets initial state sensitivities.

Parameters:: pscaleVec – Vector of parameter scales

std::vector<realtype> const &getUnscaledParameters() const

Get parameters with transformation according to parameter scale applied.

Returns:: Unscaled parameters

std::vector<realtype> const &getParameters() const

Get parameter vector.

Returns:: The user-set parameters (see also Model::getUnscaledParameters)

realtype getParameterById(std::string const &par_id) const

Get value of first model parameter with the specified ID.

Parameters:: par_id – Parameter ID
Returns:: Parameter value

realtype getParameterByName(std::string const &par_name) const

Get value of first model parameter with the specified name.

Parameters:: par_name – Parameter name
Returns:: Parameter value

void setParameters(std::vector<realtype> const &p)

Set the parameter vector.

Parameters:: p – Vector of parameters

void setParameterById(std::map<std::string, realtype> const &p, bool ignoreErrors = false)

Set model parameters according to the parameter IDs and mapped values.

Parameters:

p – Map of parameters IDs and values
ignoreErrors – Ignore errors such as parameter IDs in p which are not model parameters

void setParameterById(std::string const &par_id, realtype value)

Set value of first model parameter with the specified ID.

Parameters:

par_id – Parameter ID
value – Parameter value

int setParametersByIdRegex(std::string const &par_id_regex, realtype value)

Set all values of model parameters with IDs matching the specified regular expression.

Parameters:

par_id_regex – Parameter ID regex
value – Parameter value

Returns:

Number of parameter IDs that matched the regex

void setParameterByName(std::string const &par_name, realtype value)

Set value of first model parameter with the specified name.

Parameters:

par_name – Parameter name
value – Parameter value

void setParameterByName(std::map<std::string, realtype> const &p, bool ignoreErrors = false)

Set model parameters according to the parameter name and mapped values.

Parameters:

p – Map of parameters names and values
ignoreErrors – Ignore errors such as parameter names in p which are not model parameters

int setParametersByNameRegex(std::string const &par_name_regex, realtype value)

Set all values of all model parameters with names matching the specified regex.

Parameters:

par_name_regex – Parameter name regex
value – Parameter value

Returns:

Number of fixed parameter names that matched the regex

std::vector<realtype> const &getFixedParameters() const

Get values of fixed parameters.

Returns:: Vector of fixed parameters with same ordering as in Model::getFixedParameterIds

realtype getFixedParameterById(std::string const &par_id) const

Get value of fixed parameter with the specified ID.

Parameters:: par_id – Parameter ID
Returns:: Parameter value

realtype getFixedParameterByName(std::string const &par_name) const

Get value of fixed parameter with the specified name.

If multiple parameters have the same name, the first parameter with matching name is returned.

Parameters:: par_name – Parameter name
Returns:: Parameter value

void setFixedParameters(std::vector<realtype> const &k)

Set values for constants.

Parameters:: k – Vector of fixed parameters

void setFixedParameterById(std::string const &par_id, realtype value)

Set value of first fixed parameter with the specified ID.

Parameters:

par_id – Fixed parameter id
value – Fixed parameter value

int setFixedParametersByIdRegex(std::string const &par_id_regex, realtype value)

Set values of all fixed parameters with the ID matching the specified regex.

Parameters:

par_id_regex – Fixed parameter name regex
value – Fixed parameter value

Returns:

Number of fixed parameter IDs that matched the regex

void setFixedParameterByName(std::string const &par_name, realtype value)

Set value of first fixed parameter with the specified name.

Parameters:

par_name – Fixed parameter ID
value – Fixed parameter value

int setFixedParametersByNameRegex(std::string const &par_name_regex, realtype value)

Set value of all fixed parameters with name matching the specified regex.

Parameters:

par_name_regex – Fixed parameter name regex
value – Fixed parameter value

Returns:

Number of fixed parameter names that matched the regex

virtual std::string getName() const

Get the model name.

Returns:: Model name

virtual bool hasParameterNames() const

Report whether the model has parameter names set.

Returns:: Boolean indicating whether parameter names were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getParameterNames() const

Get names of the model parameters.

Returns:: The parameter names

virtual bool hasStateNames() const

Report whether the model has state names set.

Returns:: Boolean indicating whether state names were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getStateNames() const

Get names of the model states.

Returns:: State names

virtual std::vector<std::string> getStateNamesSolver() const

Get names of the solver states.

Returns:: State names

virtual bool hasFixedParameterNames() const

Report whether the model has fixed parameter names set.

Returns:: Boolean indicating whether fixed parameter names were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getFixedParameterNames() const

Get names of the fixed model parameters.

Returns:: Fixed parameter names

virtual bool hasObservableNames() const

Report whether the model has observable names set.

Returns:: Boolean indicating whether observable names were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getObservableNames() const

Get names of the observables.

Returns:: Observable names

virtual bool hasExpressionNames() const

Report whether the model has expression names set.

Returns:: Boolean indicating whether expression names were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getExpressionNames() const

Get names of the expressions.

Returns:: Expression names

virtual bool hasParameterIds() const

Report whether the model has parameter IDs set.

Returns:: Boolean indicating whether parameter IDs were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getParameterIds() const

Get IDs of the model parameters.

Returns:: Parameter IDs

virtual bool hasStateIds() const

Report whether the model has state IDs set.

Returns:: Boolean indicating whether state IDs were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getStateIds() const

Get IDs of the model states.

Returns:: State IDs

virtual std::vector<std::string> getStateIdsSolver() const

Get IDs of the solver states.

Returns:: State IDs

virtual bool hasFixedParameterIds() const

Report whether the model has fixed parameter IDs set.

Returns:: Boolean indicating whether fixed parameter IDs were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getFixedParameterIds() const

Get IDs of the fixed model parameters.

Returns:: Fixed parameter IDs

virtual bool hasObservableIds() const

Report whether the model has observable IDs set.

Returns:: Boolean indicating whether observable ids were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getObservableIds() const

Get IDs of the observables.

Returns:: Observable IDs

virtual bool hasExpressionIds() const

Report whether the model has expression IDs set.

Returns:: Boolean indicating whether expression ids were set. Also returns true if the number of corresponding variables is just zero.

virtual std::vector<std::string> getExpressionIds() const

Get IDs of the expression.

Returns:: Expression IDs

virtual bool hasQuadraticLLH() const

Checks whether the defined noise model is gaussian, i.e., the nllh is quadratic.

Returns:: boolean flag

std::vector<realtype> const &getTimepoints() const

Get the timepoint vector.

Returns:: Timepoint vector

realtype getTimepoint(int it) const

Get simulation timepoint for time index it.

Parameters:: it – Time index
Returns:: Timepoint

void setTimepoints(std::vector<realtype> const &ts)

Set the timepoint vector.

Parameters:: ts – New timepoint vector

double t0() const

Get simulation start time.

Returns:: Simulation start time

void setT0(double t0)

Set simulation start time.

Output timepoints are absolute timepoints, independent of \( t_{0} \). For output timepoints \( t < t_{0} \), the initial state will be returned.

Parameters:: t0 – Simulation start time

std::vector<bool> const &getStateIsNonNegative() const

Get flags indicating whether states should be treated as non-negative.

Returns:: Vector of flags

void setStateIsNonNegative(std::vector<bool> const &stateIsNonNegative)

Set flags indicating whether states should be treated as non-negative.

Parameters:: stateIsNonNegative – Vector of flags

void setAllStatesNonNegative(): Set flags indicating that all states should be treated as non-negative.

inline ModelState const &getModelState() const

Get the current model state.

Returns:: Current model state

inline void setModelState(ModelState const &state)

Set the current model state.

Parameters:: state – Model state

inline void setMinimumSigmaResiduals(double min_sigma)

Sets the estimated lower boundary for sigma_y. When :meth:setAddSigmaResiduals is activated, this lower boundary must ensure that log(sigma) + min_sigma > 0.

Parameters:: min_sigma – lower boundary

inline realtype getMinimumSigmaResiduals() const

Gets the specified estimated lower boundary for sigma_y.

Returns:: lower boundary

inline void setAddSigmaResiduals(bool sigma_res)

Specifies whether residuals should be added to account for parameter dependent sigma.

If set to true, additional residuals of the form \( \sqrt{\log(\sigma) + C} \) will be added. This enables least-squares optimization for variables with Gaussian noise assumption and parameter dependent standard deviation sigma. The constant \( C \) can be set via :meth:setMinimumSigmaResiduals.

Parameters:: sigma_res – if true, additional residuals are added

inline bool getAddSigmaResiduals() const

Checks whether residuals should be added to account for parameter dependent sigma.

Returns:: sigma_res

std::vector<int> const &getParameterList() const

Get the list of parameters for which sensitivities are computed.

Returns:: List of parameter indices

int plist(int pos) const

Get entry in parameter list by index.

Parameters:: pos – Index in sensitivity parameter list
Returns:: Index in parameter list

void setParameterList(std::vector<int> const &plist)

Set the list of parameters for which sensitivities are to be computed.

NOTE: Resets initial state sensitivities.

Parameters:: plist – List of parameter indices

std::vector<realtype> getInitialStates()

Get the initial states.

Returns:: Initial state vector

void setInitialStates(std::vector<realtype> const &x0)

Set the initial states.

Parameters:: x0 – Initial state vector

bool hasCustomInitialStates() const

Return whether custom initial states have been set.

Returns:: true if has custom initial states, otherwise false

std::vector<realtype> getInitialStateSensitivities()

Get the initial states sensitivities.

Returns:: vector of initial state sensitivities

void setInitialStateSensitivities(std::vector<realtype> const &sx0)

Set the initial state sensitivities.

Parameters:: sx0 – vector of initial state sensitivities with chainrule applied. This could be a slice of ReturnData::sx or ReturnData::sx0

bool hasCustomInitialStateSensitivities() const

Return whether custom initial state sensitivities have been set.

Returns:: true if has custom initial state sensitivities, otherwise false.

void setUnscaledInitialStateSensitivities(std::vector<realtype> const &sx0)

Set the initial state sensitivities.

Parameters:: sx0 – Vector of initial state sensitivities without chainrule applied. This could be the readin from a model.sx0data saved to HDF5.

void setSteadyStateComputationMode(SteadyStateComputationMode mode)

Set the mode how steady state is computed in the steadystate simulation.

Parameters:: mode – Steadystate computation mode

SteadyStateComputationMode getSteadyStateComputationMode() const

Gets the mode how steady state is computed in the steadystate simulation.

Returns:: Mode

void setSteadyStateSensitivityMode(SteadyStateSensitivityMode mode)

Set the mode how sensitivities are computed in the steadystate simulation.

Parameters:: mode – Steadystate sensitivity mode

SteadyStateSensitivityMode getSteadyStateSensitivityMode() const

Gets the mode how sensitivities are computed in the steadystate simulation.

Returns:: Mode

void setReinitializeFixedParameterInitialStates(bool flag)

Set whether initial states depending on fixed parameters are to be reinitialized after preequilibration and presimulation.

Parameters:: flag – Fixed parameters reinitialized?

bool getReinitializeFixedParameterInitialStates() const

Get whether initial states depending on fixedParameters are to be reinitialized after preequilibration and presimulation.

Returns:: flag true / false

void requireSensitivitiesForAllParameters()

Require computation of sensitivities for all parameters p [0..np[ in natural order.

NOTE: Resets initial state sensitivities.

void getExpression(gsl::span<realtype> w, realtype const t, AmiVector const &x)

Get time-resolved w.

Parameters:

w – Buffer (shape nw)
t – Current timepoint
x – Current state

void getObservable(gsl::span<realtype> y, realtype const t, AmiVector const &x)

Get time-resolved observables.

Parameters:

y – Buffer (shape ny)
t – Current timepoint
x – Current state

virtual ObservableScaling getObservableScaling(int iy) const

Get scaling type for observable.

Parameters:: iy – observable index
Returns:: scaling type

void getObservableSensitivity(gsl::span<realtype> sy, realtype const t, AmiVector const &x, AmiVectorArray const &sx)

Get sensitivity of time-resolved observables.

Total derivative \( sy = dydx * sx + dydp\) (only for forward sensitivities).

Parameters:

sy – buffer (shape ny x nplist, row-major)
t – Timpoint
x – State variables
sx – State sensitivities

void getObservableSigma(gsl::span<realtype> sigmay, int const it, ExpData const *edata)

Get time-resolved observable standard deviations.

Parameters:

sigmay – Buffer (shape ny)
it – Timepoint index
edata – Pointer to experimental data instance (optional, pass nullptr to ignore)

void getObservableSigmaSensitivity(gsl::span<realtype> ssigmay, gsl::span<realtype const> sy, int const it, ExpData const *edata)

Sensitivity of time-resolved observable standard deviation.

Total derivative (can be used with both adjoint and forward sensitivity).

Parameters:

ssigmay – Buffer (shape ny x nplist, row-major)
sy – Sensitivity of time-resolved observables for current timepoint
it – Timepoint index
edata – Pointer to experimental data instance (optional, pass nullptr to ignore)

void addObservableObjective(realtype &Jy, int const it, AmiVector const &x, ExpData const &edata)

Add time-resolved measurement negative log-likelihood \( Jy \).

Parameters:

Jy – Buffer (shape 1)
it – Timepoint index
x – State variables
edata – Experimental data

void addObservableObjectiveSensitivity(std::vector<realtype> &sllh, std::vector<realtype> &s2llh, int const it, AmiVector const &x, AmiVectorArray const &sx, ExpData const &edata)

Add sensitivity of time-resolved measurement negative log-likelihood \( Jy \).

Parameters:

sllh – First-order buffer (shape nplist)
s2llh – Second-order buffer (shape nJ - 1 x nplist, row-major)
it – Timepoint index
x – State variables
sx – State sensitivities
edata – Experimental data

void addPartialObservableObjectiveSensitivity(std::vector<realtype> &sllh, std::vector<realtype> &s2llh, int const it, AmiVector const &x, ExpData const &edata)

Add sensitivity of time-resolved measurement negative log-likelihood \( Jy \).

Partial derivative (to be used with adjoint sensitivities).

Parameters:

sllh – First order output buffer (shape nplist)
s2llh – Second order output buffer (shape nJ - 1 x nplist, row-major)
it – Timepoint index
x – State variables
edata – Experimental data

void getAdjointStateObservableUpdate(gsl::span<realtype> dJydx, int const it, AmiVector const &x, ExpData const &edata)

Get state sensitivity of the negative loglikelihood \( Jy \), partial derivative (to be used with adjoint sensitivities).

Parameters:

dJydx – Output buffer (shape nJ x nx_solver, row-major)
it – Timepoint index
x – State variables
edata – Experimental data instance

void getEvent(gsl::span<realtype> z, int const ie, realtype const t, AmiVector const &x)

Get event-resolved observables.

Parameters:

z – Output buffer (shape nz)
ie – Event index
t – Timepoint
x – State variables

void getEventSensitivity(gsl::span<realtype> sz, int const ie, realtype const t, AmiVector const &x, AmiVectorArray const &sx)

Get sensitivities of event-resolved observables.

Total derivative (only forward sensitivities).

Parameters:

sz – Output buffer (shape nz x nplist, row-major)
ie – Event index
t – Timepoint
x – State variables
sx – State sensitivities

void getUnobservedEventSensitivity(gsl::span<realtype> sz, int const ie)

Get sensitivity of z at final timepoint.

Ignores sensitivity of timepoint. Total derivative.

Parameters:

sz – Output buffer (shape nz x nplist, row-major)
ie – Event index

void getEventRegularization(gsl::span<realtype> rz, int const ie, realtype const t, AmiVector const &x)

Get regularization for event-resolved observables.

Parameters:

rz – Output buffer (shape nz)
ie – Event index
t – Timepoint
x – State variables

void getEventRegularizationSensitivity(gsl::span<realtype> srz, int const ie, realtype const t, AmiVector const &x, AmiVectorArray const &sx)

Get sensitivities of regularization for event-resolved observables.

Total derivative. Only forward sensitivities.

Parameters:

srz – Output buffer (shape nz x nplist, row-major)
ie – Event index
t – Timepoint
x – State variables
sx – State sensitivities

void getEventSigma(gsl::span<realtype> sigmaz, int const ie, int const nroots, realtype const t, ExpData const *edata)

Get event-resolved observable standard deviations.

Parameters:

sigmaz – Output buffer (shape nz)
ie – Event index
nroots – Event occurrence
t – Timepoint
edata – Pointer to experimental data (optional, pass nullptr to ignore)

void getEventSigmaSensitivity(gsl::span<realtype> ssigmaz, int const ie, int const nroots, realtype const t, ExpData const *edata)

Get sensitivities of event-resolved observable standard deviations.

Total derivative (only forward sensitivities).

Parameters:

ssigmaz – Output buffer (shape nz x nplist, row-major)
ie – Event index
nroots – Event occurrence
t – Timepoint
edata – Pointer to experimental data (optional, pass nullptr to ignore)

void addEventObjective(realtype &Jz, int const ie, int const nroots, realtype const t, AmiVector const &x, ExpData const &edata)

Add event-resolved observable negative log-likelihood.

Parameters:

Jz – Output buffer (shape 1)
ie – Event index
nroots – Event occurrence
t – Timepoint
x – State variables
edata – Experimental data

void addEventObjectiveRegularization(realtype &Jrz, int const ie, int const nroots, realtype const t, AmiVector const &x, ExpData const &edata)

Add event-resolved observable negative log-likelihood.

Parameters:

Jrz – Output buffer (shape 1)
ie – Event index
nroots – Event occurrence
t – Timepoint
x – State variables
edata – Experimental data

void addEventObjectiveSensitivity(std::vector<realtype> &sllh, std::vector<realtype> &s2llh, int const ie, int const nroots, realtype const t, AmiVector const &x, AmiVectorArray const &sx, ExpData const &edata)

Add sensitivity of time-resolved measurement negative log-likelihood \( Jy \).

Total derivative (to be used with forward sensitivities).

Parameters:

sllh – First order buffer (shape nplist)
s2llh – Second order buffer (shape nJ-1 x nplist, row-major)
ie – Event index
nroots – Event occurrence
t – Timepoint
x – State variables
sx – State sensitivities
edata – Experimental data

void addPartialEventObjectiveSensitivity(std::vector<realtype> &sllh, std::vector<realtype> &s2llh, int const ie, int const nroots, realtype const t, AmiVector const &x, ExpData const &edata)

Add sensitivity of time-resolved measurement negative log-likelihood \( Jy \).

Partial derivative (to be used with adjoint sensitivities).

Parameters:

sllh – First order buffer (shape nplist)
s2llh – Second order buffer (shape (nJ-1) x nplist, row-major)
ie – Event index
nroots – Event occurrence
t – Timepoint
x – State variables
edata – Experimental data

void getAdjointStateEventUpdate(gsl::span<realtype> dJzdx, int const ie, int const nroots, realtype const t, AmiVector const &x, ExpData const &edata)

State sensitivity of the negative loglikelihood \( Jz \).

Partial derivative (to be used with adjoint sensitivities).

Parameters:

dJzdx – Output buffer (shape nJ x nx_solver, row-major)
ie – Event index
nroots – Event occurrence
t – Timepoint
x – State variables
edata – Experimental data

void getEventTimeSensitivity(std::vector<realtype> &stau, realtype const t, int const ie, AmiVector const &x, AmiVectorArray const &sx)

Sensitivity of event timepoint, total derivative.

Only forward sensitivities.

Parameters:

stau – Timepoint sensitivity (shape nplist)
t – Timepoint
ie – Event index
x – State variables
sx – State sensitivities

void addStateEventUpdate(AmiVector &x, int const ie, realtype const t, AmiVector const &xdot, AmiVector const &xdot_old)

Update state variables after event.

Parameters:

x – Current state (will be overwritten)
ie – Event index
t – Current timepoint
xdot – Current residual function values
xdot_old – Value of residual function before event

void addStateSensitivityEventUpdate(AmiVectorArray &sx, int const ie, realtype const t, AmiVector const &x_old, AmiVector const &xdot, AmiVector const &xdot_old, std::vector<realtype> const &stau)

Update state sensitivity after event.

Parameters:

sx – Current state sensitivity (will be overwritten)
ie – Event index
t – Current timepoint
x_old – Current state
xdot – Current residual function values
xdot_old – Value of residual function before event
stau – Timepoint sensitivity, to be computed with Model::getEventTimeSensitivity

void addAdjointStateEventUpdate(AmiVector &xB, int const ie, realtype const t, AmiVector const &x, AmiVector const &xdot, AmiVector const &xdot_old)

Update adjoint state after event.

Parameters:

xB – Current adjoint state (will be overwritten)
ie – Event index
t – Current timepoint
x – Current state
xdot – Current residual function values
xdot_old – Value of residual function before event

void addAdjointQuadratureEventUpdate(AmiVector xQB, int const ie, realtype const t, AmiVector const &x, AmiVector const &xB, AmiVector const &xdot, AmiVector const &xdot_old)

Update adjoint quadratures after event.

Parameters:

xQB – Current quadrature state (will be overwritten)
ie – Event index
t – Current timepoint
x – Current state
xB – Current adjoint state
xdot – Current residual function values
xdot_old – Value of residual function before event

void updateHeaviside(std::vector<int> const &rootsfound)

Update the Heaviside variables h on event occurrences.

Parameters:: rootsfound – Provides the direction of the zero-crossing, so adding it will give the right update to the Heaviside variables (zero if no root was found)

void updateHeavisideB(int const *rootsfound)

Updates the Heaviside variables h on event occurrences in the backward problem.

Parameters:: rootsfound – Provides the direction of the zero-crossing, so adding it will give the right update to the Heaviside variables (zero if no root was found)

int checkFinite(gsl::span<realtype const> array, ModelQuantity model_quantity, realtype t) const

Check if the given array has only finite elements.

For (1D) spans.

Parameters:

array –
model_quantity – The model quantity array corresponds to
t – Current timepoint

Returns:

int checkFinite(gsl::span<realtype const> array, ModelQuantity model_quantity, size_t num_cols, realtype t) const

Check if the given array has only finite elements.

For flattened 2D arrays.

Parameters:

array – Flattened matrix
model_quantity – The model quantity array corresponds to
num_cols – Number of columns of the non-flattened matrix
t – Current timepoint

Returns:

int checkFinite(SUNMatrix m, ModelQuantity model_quantity, realtype t) const

Check if the given array has only finite elements.

For SUNMatrix.

Parameters:

m – Matrix to check
model_quantity – The model quantity m corresponds to
t – current timepoint

Returns:

void setAlwaysCheckFinite(bool alwaysCheck)

Set whether the result of every call to Model::f* should be checked for finiteness.

Parameters:: alwaysCheck –

bool getAlwaysCheckFinite() const

Get setting of whether the result of every call to Model::f* should be checked for finiteness.

Returns:: that

void fx0(AmiVector &x)

Compute/get initial states.

Parameters:: x – Output buffer.

void fx0_fixedParameters(AmiVector &x)

Set only those initial states that are specified via fixed parameters.

Parameters:: x – Output buffer.

void fsx0(AmiVectorArray &sx, AmiVector const &x)

Compute/get initial value for initial state sensitivities.

Parameters:

sx – Output buffer for state sensitivities
x – State variables

void fsx0_fixedParameters(AmiVectorArray &sx, AmiVector const &x)

Get only those initial states sensitivities that are affected from amici::Model::fx0_fixedParameters.

Parameters:

sx – Output buffer for state sensitivities
x – State variables

virtual void fsdx0()

Compute sensitivity of derivative initial states sensitivities sdx0.

Only necessary for DAEs.

void fx_rdata(AmiVector &x_rdata, AmiVector const &x_solver)

Expand conservation law for states.

Parameters:

x_rdata – Output buffer for state variables with conservation laws expanded (stored in amici::ReturnData).
x_solver – State variables with conservation laws applied (solver returns this)

void fsx_rdata(AmiVectorArray &sx_rdata, AmiVectorArray const &sx_solver, AmiVector const &x_solver)

Expand conservation law for state sensitivities.

Parameters:

sx_rdata – Output buffer for state variables sensitivities with conservation laws expanded (stored in amici::ReturnData).
sx_solver – State variables sensitivities with conservation laws applied (solver returns this)
x_solver – State variables with conservation laws applied (solver returns this)

void setReinitializationStateIdxs(std::vector<int> const &idxs)

Set indices of states to be reinitialized based on provided constants / fixed parameters.

Parameters:: idxs – Array of state indices

std::vector<int> const &getReinitializationStateIdxs() const

Return indices of states to be reinitialized based on provided constants / fixed parameters.

Returns:: Those indices.

AmiVectorArray const &get_dxdotdp() const

getter for dxdotdp (matlab generated)

Returns:: dxdotdp

SUNMatrixWrapper const &get_dxdotdp_full() const

getter for dxdotdp (python generated)

Returns:: dxdotdp

virtual std::vector<double> get_trigger_timepoints() const

Get trigger times for events that don’t require root-finding.

Returns:: List of unique trigger points for events that don’t require root-finding (i.e. that trigger at predetermined timepoints), in ascending order.

inline std::vector<double> get_steadystate_mask() const

Get steady-state mask as std::vector.

See set_steadystate_mask for details.

Returns:: Steady-state mask

inline AmiVector const &get_steadystate_mask_av() const

Get steady-state mask as AmiVector.

See set_steadystate_mask for details.

Returns:: Steady-state mask

void set_steadystate_mask(std::vector<double> const &mask)

Set steady-state mask.

The mask is used to exclude certain state variables from the steady-state convergence check. Positive values indicate that the corresponding state variable should be included in the convergence check, while non-positive values indicate that the corresponding state variable should be excluded. An empty mask is interpreted as including all state variables.

Parameters:: mask – Mask of length nx_solver.

virtual void fdeltaqB(realtype *deltaqB, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, int ip, int ie, realtype const *xdot, realtype const *xdot_old, realtype const *xB)

Model-specific implementation of fdeltaqB.

Parameters:

deltaqB – sensitivity update
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
ip – sensitivity index
ie – event index
xdot – new model right hand side
xdot_old – previous model right hand side
xB – adjoint state

virtual void fdeltasx(realtype *deltasx, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *w, int ip, int ie, realtype const *xdot, realtype const *xdot_old, realtype const *sx, realtype const *stau, realtype const *tcl)

Model-specific implementation of fdeltasx.

Parameters:

deltasx – sensitivity update
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
w – repeating elements vector
ip – sensitivity index
ie – event index
xdot – new model right hand side
xdot_old – previous model right hand side
sx – state sensitivity
stau – event-time sensitivity
tcl – total abundances for conservation laws

virtual void fdeltax(realtype *deltax, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, int ie, realtype const *xdot, realtype const *xdot_old)

Model-specific implementation of fdeltax.

Parameters:

deltax – state update
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
ie – event index
xdot – new model right hand side
xdot_old – previous model right hand side

virtual void fdeltaxB(realtype *deltaxB, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, int ie, realtype const *xdot, realtype const *xdot_old, realtype const *xB)

Model-specific implementation of fdeltaxB.

Parameters:

deltaxB – adjoint state update
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
ie – event index
xdot – new model right hand side
xdot_old – previous model right hand side
xB – current adjoint state

virtual void fdJrzdsigma(realtype *dJrzdsigma, int iz, realtype const *p, realtype const *k, realtype const *rz, realtype const *sigmaz)

Model-specific implementation of fdJrzdsigma.

Parameters:

dJrzdsigma – Sensitivity of event penalization Jrz w.r.t. standard deviation sigmaz
iz – event output index
p – parameter vector
k – constant vector
rz – model root output at timepoint
sigmaz – event measurement standard deviation at timepoint

virtual void fdJrzdz(realtype *dJrzdz, int iz, realtype const *p, realtype const *k, realtype const *rz, realtype const *sigmaz)

Model-specific implementation of fdJrzdz.

Parameters:

dJrzdz – partial derivative of event penalization Jrz
iz – event output index
p – parameter vector
k – constant vector
rz – model root output at timepoint
sigmaz – event measurement standard deviation at timepoint

virtual void fdJydsigma(realtype *dJydsigma, int iy, realtype const *p, realtype const *k, realtype const *y, realtype const *sigmay, realtype const *my)

Model-specific implementation of fdJydsigma.

Parameters:

dJydsigma – Sensitivity of time-resolved measurement negative log-likelihood Jy w.r.t. standard deviation sigmay
iy – output index
p – parameter vector
k – constant vector
y – model output at timepoint
sigmay – measurement standard deviation at timepoint
my – measurement at timepoint

virtual void fdJydy(realtype *dJydy, int iy, realtype const *p, realtype const *k, realtype const *y, realtype const *sigmay, realtype const *my)

Model-specific implementation of fdJydy.

Parameters:

dJydy – partial derivative of time-resolved measurement negative log-likelihood Jy
iy – output index
p – parameter vector
k – constant vector
y – model output at timepoint
sigmay – measurement standard deviation at timepoint
my – measurement at timepoint

virtual void fdJydy_colptrs(SUNMatrixWrapper &dJydy, int index)

Model-specific implementation of fdJydy colptrs.

Parameters:

dJydy – sparse matrix to which colptrs will be written
index – ytrue index

virtual void fdJydy_rowvals(SUNMatrixWrapper &dJydy, int index)

Model-specific implementation of fdJydy rowvals.

Parameters:

dJydy – sparse matrix to which rowvals will be written
index – ytrue index

virtual void fdJzdsigma(realtype *dJzdsigma, int iz, realtype const *p, realtype const *k, realtype const *z, realtype const *sigmaz, realtype const *mz)

Model-specific implementation of fdJzdsigma.

Parameters:

dJzdsigma – Sensitivity of event measurement negative log-likelihood Jz w.r.t. standard deviation sigmaz
iz – event output index
p – parameter vector
k – constant vector
z – model event output at timepoint
sigmaz – event measurement standard deviation at timepoint
mz – event measurement at timepoint

virtual void fdJzdz(realtype *dJzdz, int iz, realtype const *p, realtype const *k, realtype const *z, realtype const *sigmaz, realtype const *mz)

Model-specific implementation of fdJzdz.

Parameters:

dJzdz – partial derivative of event measurement negative log-likelihood Jz
iz – event output index
p – parameter vector
k – constant vector
z – model event output at timepoint
sigmaz – event measurement standard deviation at timepoint
mz – event measurement at timepoint

virtual void fdrzdp(realtype *drzdp, int ie, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, int ip)

Model-specific implementation of fdrzdp.

Parameters:

drzdp – partial derivative of root output rz w.r.t. model parameters p
ie – event index
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
ip – parameter index w.r.t. which the derivative is requested

virtual void fdrzdx(realtype *drzdx, int ie, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h)

Model-specific implementation of fdrzdx.

Parameters:

drzdx – partial derivative of root output rz w.r.t. model states x
ie – event index
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector

virtual void fdsigmaydp(realtype *dsigmaydp, realtype const t, realtype const *p, realtype const *k, realtype const *y, int ip)

Model-specific implementation of fdsigmaydp.

Parameters:

dsigmaydp – partial derivative of standard deviation of measurements
t – current time
p – parameter vector
k – constant vector
y – model output at timepoint t
ip – sensitivity index

virtual void fdsigmaydy(realtype *dsigmaydy, realtype const t, realtype const *p, realtype const *k, realtype const *y)

Model-specific implementation of fsigmay.

Parameters:

dsigmaydy – partial derivative of standard deviation of measurements w.r.t. model outputs
t – current time
p – parameter vector
k – constant vector
y – model output at timepoint t

virtual void fdsigmazdp(realtype *dsigmazdp, realtype const t, realtype const *p, realtype const *k, int ip)

Model-specific implementation of fsigmaz.

Parameters:

dsigmazdp – partial derivative of standard deviation of event measurements
t – current time
p – parameter vector
k – constant vector
ip – sensitivity index

virtual void fdtotal_cldp(realtype *dtotal_cldp, realtype const *x_rdata, realtype const *p, realtype const *k, int const ip)

Compute dtotal_cl / dp.

Parameters:

dtotal_cldp – dtotal_cl / dp
x_rdata – State variables with conservation laws applied
p – parameter vector
k – constant vector
ip – Sensitivity index

virtual void fdtotal_cldx_rdata(realtype *dtotal_cldx_rdata, realtype const *x_rdata, realtype const *p, realtype const *k, realtype const *tcl)

Compute dtotal_cl / dx_rdata.

Parameters:

dtotal_cldx_rdata – dtotal_cl / dx_rdata
x_rdata – State variables with conservation laws applied
p – parameter vector
k – constant vector
tcl – Total abundances for conservation laws

virtual void fdtotal_cldx_rdata_colptrs(SUNMatrixWrapper &dtotal_cldx_rdata)

Model-specific implementation of fdtotal_cldx_rdata, colptrs part.

Parameters:: dtotal_cldx_rdata – sparse matrix to which colptrs will be written

virtual void fdtotal_cldx_rdata_rowvals(SUNMatrixWrapper &dtotal_cldx_rdata)

Model-specific implementation of fdtotal_cldx_rdata, rowvals part.

Parameters:: dtotal_cldx_rdata – sparse matrix to which rowvals will be written

virtual void fdwdp(realtype *dwdp, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *w, realtype const *tcl, realtype const *stcl, realtype const *spl, realtype const *sspl, bool include_static = true)

Model-specific sparse implementation of dwdp.

Parameters:

dwdp – Recurring terms in xdot, parameter derivative
t – timepoint
x – vector with the states
p – parameter vector
k – constants vector
h – Heaviside vector
w – vector with helper variables
tcl – total abundances for conservation laws
stcl – sensitivities of total abundances for conservation laws
spl – spline value vector
sspl – sensitivities of spline values vector w.r.t. parameters \( p \)
include_static – Whether to (re-)evaluate only dynamic expressions (false) or also static expressions (true). Dynamic expressions are those that depend directly or indirectly on time, static expressions are those that don’t.

virtual void fdwdp_colptrs(SUNMatrixWrapper &dwdp)

Model-specific implementation for dwdp, column pointers.

Parameters:: dwdp – sparse matrix to which colptrs will be written

virtual void fdwdp_rowvals(SUNMatrixWrapper &dwdp)

Model-specific implementation for dwdp, row values.

Parameters:: dwdp – sparse matrix to which rowvals will be written

virtual void fdwdw(realtype *dwdw, realtype t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *w, realtype const *tcl, bool include_static = true)

Model-specific implementation of fdwdw, no w chainrule (Py)

Parameters:

dwdw – partial derivative w wrt w
t – timepoint
x – Vector with the states
p – parameter vector
k – constants vector
h – Heaviside vector
w – vector with helper variables
tcl – Total abundances for conservation laws
include_static – Whether to (re-)evaluate only dynamic expressions (false) or also static expressions (true). Dynamic expressions are those that depend directly or indirectly on time, static expressions are those that don’t.

virtual void fdwdw_colptrs(SUNMatrixWrapper &dwdw)

Model-specific implementation of fdwdw, colptrs part.

Parameters:: dwdw – sparse matrix to which colptrs will be written

virtual void fdwdw_rowvals(SUNMatrixWrapper &dwdw)

Model-specific implementation of fdwdw, rowvals part.

Parameters:: dwdw – sparse matrix to which rowvals will be written

virtual void fdwdx(realtype *dwdx, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *w, realtype const *tcl, realtype const *spl, bool include_static = true)

Model-specific implementation of dwdx, data part.

Parameters:

dwdx – Recurring terms in xdot, state derivative
t – timepoint
x – vector with the states
p – parameter vector
k – constants vector
h – Heaviside vector
w – vector with helper variables
tcl – total abundances for conservation laws
spl – spline value vector
include_static – Whether to (re-)evaluate only dynamic expressions (false) or also static expressions (true). Dynamic expressions are those that depend directly or indirectly on time, static expressions are those that don’t.

virtual void fdwdx_colptrs(SUNMatrixWrapper &dwdx)

Model-specific implementation for dwdx, column pointers.

Parameters:: dwdx – sparse matrix to which colptrs will be written

virtual void fdwdx_rowvals(SUNMatrixWrapper &dwdx)

Model-specific implementation for dwdx, row values.

Parameters:: dwdx – sparse matrix to which rowvals will be written

virtual void fdx_rdatadp(realtype *dx_rdatadp, realtype const *x, realtype const *tcl, realtype const *p, realtype const *k, int const ip)

Compute dx_rdata / dp.

Parameters:

dx_rdatadp – dx_rdata / dp
p – parameter vector
k – constant vector
x – State variables with conservation laws applied
tcl – Total abundances for conservation laws
ip – Sensitivity index

virtual void fdx_rdatadtcl(realtype *dx_rdatadtcl, realtype const *x, realtype const *tcl, realtype const *p, realtype const *k)

Compute dx_rdata / dtcl.

Parameters:

dx_rdatadtcl – dx_rdata / dtcl
p – parameter vector
k – constant vector
x – State variables with conservation laws applied
tcl – Total abundances for conservation laws

virtual void fdx_rdatadtcl_colptrs(SUNMatrixWrapper &dx_rdatadtcl)

Model-specific implementation of fdx_rdatadtcl, colptrs part.

Parameters:: dx_rdatadtcl – sparse matrix to which colptrs will be written

virtual void fdx_rdatadtcl_rowvals(SUNMatrixWrapper &dx_rdatadtcl)

Model-specific implementation of fdx_rdatadtcl, rowvals part.

Parameters:: dx_rdatadtcl – sparse matrix to which rowvals will be written

virtual void fdx_rdatadx_solver(realtype *dx_rdatadx_solver, realtype const *x, realtype const *tcl, realtype const *p, realtype const *k)

Compute dx_rdata / dx_solver.

Parameters:

dx_rdatadx_solver – dx_rdata / dx_solver
p – parameter vector
k – constant vector
x – State variables with conservation laws applied
tcl – Total abundances for conservation laws

virtual void fdx_rdatadx_solver_colptrs(SUNMatrixWrapper &dxrdatadxsolver)

Model-specific implementation of fdx_rdatadx_solver, colptrs part.

Parameters:: dxrdatadxsolver – sparse matrix to which colptrs will be written

virtual void fdx_rdatadx_solver_rowvals(SUNMatrixWrapper &dxrdatadxsolver)

Model-specific implementation of fdx_rdatadx_solver, rowvals part.

Parameters:: dxrdatadxsolver – sparse matrix to which rowvals will be written

virtual void fdydp(realtype *dydp, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, int ip, realtype const *w, realtype const *dwdp)

Model-specific implementation of fdydp (MATLAB-only)

Parameters:

dydp – partial derivative of observables y w.r.t. model parameters p
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
ip – parameter index w.r.t. which the derivative is requested
w – repeating elements vector
dwdp – Recurring terms in xdot, parameter derivative

virtual void fdydp(realtype *dydp, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, int ip, realtype const *w, realtype const *tcl, realtype const *dtcldp, realtype const *spl, realtype const *sspl)

Model-specific implementation of fdydp (Python)

Parameters:

dydp – partial derivative of observables y w.r.t. model parameters p
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
ip – parameter index w.r.t. which the derivative is requested
w – repeating elements vector
tcl – total abundances for conservation laws
dtcldp – Sensitivities of total abundances for conservation laws
spl – spline value vector
sspl – sensitivities of spline values vector w.r.t. parameters \( p \)

virtual void fdydx(realtype *dydx, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *w, realtype const *dwdx)

Model-specific implementation of fdydx.

Parameters:

dydx – partial derivative of observables y w.r.t. model states x
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
w – repeating elements vector
dwdx – Recurring terms in xdot, state derivative

virtual void fdzdp(realtype *dzdp, int ie, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, int ip)

Model-specific implementation of fdzdp.

Parameters:

dzdp – partial derivative of event-resolved output z w.r.t. model parameters p
ie – event index
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
ip – parameter index w.r.t. which the derivative is requested

virtual void fdzdx(realtype *dzdx, int ie, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h)

Model-specific implementation of fdzdx.

Parameters:

dzdx – partial derivative of event-resolved output z w.r.t. model states x
ie – event index
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector

virtual void fJrz(realtype *nllh, int iz, realtype const *p, realtype const *k, realtype const *z, realtype const *sigmaz)

Model-specific implementation of fJrz.

Parameters:

nllh – regularization for event measurements z
iz – event output index
p – parameter vector
k – constant vector
z – model event output at timepoint
sigmaz – event measurement standard deviation at timepoint

virtual void fJy(realtype *nllh, int iy, realtype const *p, realtype const *k, realtype const *y, realtype const *sigmay, realtype const *my)

Model-specific implementation of fJy.

Parameters:

nllh – negative log-likelihood for measurements y
iy – output index
p – parameter vector
k – constant vector
y – model output at timepoint
sigmay – measurement standard deviation at timepoint
my – measurements at timepoint

virtual void fJz(realtype *nllh, int iz, realtype const *p, realtype const *k, realtype const *z, realtype const *sigmaz, realtype const *mz)

Model-specific implementation of fJz.

Parameters:

nllh – negative log-likelihood for event measurements z
iz – event output index
p – parameter vector
k – constant vector
z – model event output at timepoint
sigmaz – event measurement standard deviation at timepoint
mz – event measurements at timepoint

virtual void frz(realtype *rz, int ie, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h)

Model-specific implementation of frz.

Parameters:

rz – value of root function at current timepoint (non-output events not included)
ie – event index
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector

virtual void fsigmay(realtype *sigmay, realtype const t, realtype const *p, realtype const *k, realtype const *y)

Model-specific implementation of fsigmay.

Parameters:

sigmay – standard deviation of measurements
t – current time
p – parameter vector
k – constant vector
y – model output at timepoint t

virtual void fsigmaz(realtype *sigmaz, realtype const t, realtype const *p, realtype const *k)

Model-specific implementation of fsigmaz.

Parameters:

sigmaz – standard deviation of event measurements
t – current time
p – parameter vector
k – constant vector

virtual void fsrz(realtype *srz, int ie, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *sx, int ip)

Model-specific implementation of fsrz.

Parameters:

srz – Sensitivity of rz, total derivative
ie – event index
t – current time
x – current state
p – parameter vector
k – constant vector
sx – current state sensitivity
h – Heaviside vector
ip – sensitivity index

virtual void fstau(realtype *stau, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *tcl, realtype const *sx, int ip, int ie)

Model-specific implementation of fstau.

Parameters:

stau – total derivative of event timepoint
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
tcl – total abundances for conservation laws
sx – current state sensitivity
ip – sensitivity index
ie – event index

virtual void fsx0(realtype *sx0, realtype const t, realtype const *x0, realtype const *p, realtype const *k, int ip)

Model-specific implementation of fsx0.

Parameters:

sx0 – initial state sensitivities
t – initial time
x0 – initial state
p – parameter vector
k – constant vector
ip – sensitivity index

virtual void fsx0_fixedParameters(realtype *sx0, realtype const t, realtype const *x0, realtype const *p, realtype const *k, int ip, gsl::span<int const> reinitialization_state_idxs)

Model-specific implementation of fsx0_fixedParameters.

Parameters:

sx0 – initial state sensitivities
t – initial time
x0 – initial state
p – parameter vector
k – constant vector
ip – sensitivity index
reinitialization_state_idxs – Indices of states to be reinitialized based on provided constants / fixed parameters.

virtual void fsz(realtype *sz, int ie, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *sx, int ip)

Model-specific implementation of fsz.

Parameters:

sz – Sensitivity of rz, total derivative
ie – event index
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
sx – current state sensitivity
ip – sensitivity index

virtual void fw(realtype *w, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *tcl, realtype const *spl, bool include_static = true)

Model-specific implementation of fw.

Parameters:

w – Recurring terms in xdot
t – timepoint
x – vector with the states
p – parameter vector
k – constants vector
h – Heaviside vector
tcl – total abundances for conservation laws
spl – spline value vector
include_static – Whether to (re-)evaluate only dynamic expressions (false) or also static expressions (true). Dynamic expressions are those that depend directly or indirectly on time, static expressions are those that don’t.

virtual void fx0(realtype *x0, realtype const t, realtype const *p, realtype const *k)

Model-specific implementation of fx0.

Parameters:

x0 – initial state
t – initial time
p – parameter vector
k – constant vector

virtual void fx0_fixedParameters(realtype *x0, realtype const t, realtype const *p, realtype const *k, gsl::span<int const> reinitialization_state_idxs)

Model-specific implementation of fx0_fixedParameters.

Parameters:

x0 – initial state
t – initial time
p – parameter vector
k – constant vector
reinitialization_state_idxs – Indices of states to be reinitialized based on provided constants / fixed parameters.

virtual void fy(realtype *y, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h, realtype const *w)

Model-specific implementation of fy.

Parameters:

y – model output at current timepoint
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector
w – repeating elements vector

virtual void fz(realtype *z, int ie, realtype const t, realtype const *x, realtype const *p, realtype const *k, realtype const *h)

Model-specific implementation of fz.

Parameters:

z – value of event output
ie – event index
t – current time
x – current state
p – parameter vector
k – constant vector
h – Heaviside vector

Public Members

bool pythonGenerated = false: Flag indicating Matlab- or Python-based model generation

SecondOrderMode o2mode = {SecondOrderMode::none}: Flag indicating whether for amici::Solver::sensi_ == amici::SensitivityOrder::second directional or full second order derivative will be computed

std::vector<realtype> idlist: Flag array for DAE equations

Logger *logger = nullptr: Logger

std::map<realtype, std::vector<int>> state_independent_events_ = {}: Map of trigger timepoints to event indices for events that don’t require root-finding.

Protected Functions

void writeSliceEvent(gsl::span<realtype const> slice, gsl::span<realtype> buffer, int const ie)

Write part of a slice to a buffer according to indices specified in z2event.

Parameters:

slice – Input data slice
buffer – Output data slice
ie – Event index

void writeSensitivitySliceEvent(gsl::span<realtype const> slice, gsl::span<realtype> buffer, int const ie)

Write part of a sensitivity slice to a buffer according to indices specified in z2event.

Parameters:

slice – source data slice
buffer – output data slice
ie – event index

void writeLLHSensitivitySlice(std::vector<realtype> const &dLLhdp, std::vector<realtype> &sllh, std::vector<realtype> &s2llh)

Separate first and second order objective sensitivity information and write them into the respective buffers.

Parameters:

dLLhdp – Data with mangled first- and second-order information
sllh – First order buffer
s2llh – Second order buffer

void checkLLHBufferSize(std::vector<realtype> const &sllh, std::vector<realtype> const &s2llh) const

Verify that the provided buffers have the expected size.

Parameters:

sllh – first order buffer
s2llh – second order buffer

void initializeVectors(): Set the nplist-dependent vectors to their proper sizes.

void fy(realtype t, AmiVector const &x)

Compute observables / measurements.

Parameters:

t – Current timepoint
x – Current state

void fdydp(realtype t, AmiVector const &x)

Compute partial derivative of observables \( y \) w.r.t. model parameters p.

Parameters:

t – Current timepoint
x – Current state

void fdydx(realtype t, AmiVector const &x)

Compute partial derivative of observables \( y \) w.r.t. state variables x.

Parameters:

t – Current timepoint
x – Current state

void fsigmay(int it, ExpData const *edata)

Compute standard deviation of measurements.

Parameters:

it – Timepoint index
edata – Experimental data

void fdsigmaydp(int it, ExpData const *edata)

Compute partial derivative of standard deviation of measurements w.r.t. model parameters.

Parameters:

it – Timepoint index
edata – pointer to amici::ExpData data instance holding sigma values

void fdsigmaydy(int it, ExpData const *edata)

Compute partial derivative of standard deviation of measurements w.r.t. model outputs.

Parameters:

it – Timepoint index
edata – pointer to amici::ExpData data instance holding sigma values

void fJy(realtype &Jy, int it, AmiVector const &y, ExpData const &edata)

Compute negative log-likelihood of measurements \( y \).

Parameters:

Jy – Variable to which llh will be added
it – Timepoint index
y – Simulated observable
edata – Pointer to experimental data instance

void fdJydy(int it, AmiVector const &x, ExpData const &edata)

Compute partial derivative of time-resolved measurement negative log-likelihood \( Jy \).

Parameters:

it – timepoint index
x – state variables
edata – Pointer to experimental data

void fdJydsigma(int it, AmiVector const &x, ExpData const &edata)

Sensitivity of time-resolved measurement negative log-likelihood Jy w.r.t. standard deviation sigma.

Parameters:

it – timepoint index
x – state variables
edata – pointer to experimental data instance

void fdJydp(int const it, AmiVector const &x, ExpData const &edata)

Compute sensitivity of time-resolved measurement negative log-likelihood \( Jy \) w.r.t. parameters for the given timepoint.

Parameters:

it – timepoint index
x – state variables
edata – pointer to experimental data instance

void fdJydx(int const it, AmiVector const &x, ExpData const &edata)

Sensitivity of time-resolved measurement negative log-likelihood \( Jy \) w.r.t. state variables.

Parameters:

it – Timepoint index
x – State variables
edata – Pointer to experimental data instance

void fz(int ie, realtype t, AmiVector const &x)

Compute event-resolved output.

Parameters:

ie – Event index
t – Current timepoint
x – Current state

void fdzdp(int ie, realtype t, AmiVector const &x)

Compute partial derivative of event-resolved output z w.r.t. model parameters p

Parameters:

ie – event index
t – current timepoint
x – current state

void fdzdx(int ie, realtype t, AmiVector const &x)

Compute partial derivative of event-resolved output z w.r.t. model states x.

Parameters:

ie – Event index
t – Current timepoint
x – Current state

void frz(int ie, realtype t, AmiVector const &x)

Compute event root function of events.

Equal to Model::froot but does not include non-output events.

Parameters:

ie – Event index
t – Current timepoint
x – Current state

void fdrzdp(int ie, realtype t, AmiVector const &x)

Compute sensitivity of event-resolved root output w.r.t. model parameters p.

Parameters:

ie – Event index
t – Current timepoint
x – Current state

void fdrzdx(int ie, realtype t, AmiVector const &x)

Compute sensitivity of event-resolved measurements \( rz \) w.r.t. model states x.

Parameters:

ie – Event index
t – Current timepoint
x – Current state

void fsigmaz(int const ie, int const nroots, realtype const t, ExpData const *edata)

Compute standard deviation of events.

Parameters:

ie – Event index
nroots – Event index
t – Current timepoint
edata – Experimental data

void fdsigmazdp(int ie, int nroots, realtype t, ExpData const *edata)

Compute sensitivity of standard deviation of events measurements w.r.t. model parameters p.

Parameters:

ie – Event index
nroots – Event occurrence
t – Current timepoint
edata – Pointer to experimental data instance

void fJz(realtype &Jz, int nroots, AmiVector const &z, ExpData const &edata)

Compute negative log-likelihood of event-resolved measurements z.

Parameters:

Jz – Variable to which llh will be added
nroots – Event index
z – Simulated event
edata – Experimental data

void fdJzdz(int const ie, int const nroots, realtype const t, AmiVector const &x, ExpData const &edata)

Compute partial derivative of event measurement negative log-likelihood \( Jz \).

Parameters:

ie – Event index
nroots – Event index
t – Current timepoint
x – State variables
edata – Experimental data

void fdJzdsigma(int const ie, int const nroots, realtype const t, AmiVector const &x, ExpData const &edata)

Compute sensitivity of event measurement negative log-likelihood \( Jz \) w.r.t. standard deviation sigmaz.

Parameters:

ie – Event index
nroots – Event index
t – Current timepoint
x – State variables
edata – Pointer to experimental data instance

void fdJzdp(int const ie, int const nroots, realtype t, AmiVector const &x, ExpData const &edata)

Compute sensitivity of event-resolved measurement negative log-likelihood Jz w.r.t. parameters.

Parameters:

ie – Event index
nroots – Event index
t – Current timepoint
x – State variables
edata – Pointer to experimental data instance

void fdJzdx(int const ie, int const nroots, realtype t, AmiVector const &x, ExpData const &edata)

Compute sensitivity of event-resolved measurement negative log-likelihood Jz w.r.t. state variables.

Parameters:

ie – Event index
nroots – Event index
t – Current timepoint
x – State variables
edata – Experimental data

void fJrz(realtype &Jrz, int nroots, AmiVector const &rz, ExpData const &edata)

Compute regularization of negative log-likelihood with roots of event-resolved measurements rz.

Parameters:

Jrz – Variable to which regularization will be added
nroots – Event index
rz – Regularization variable
edata – Experimental data

void fdJrzdz(int const ie, int const nroots, realtype const t, AmiVector const &x, ExpData const &edata)

Compute partial derivative of event measurement negative log-likelihood J.

Parameters:

ie – Event index
nroots – Event index
t – Current timepoint
x – State variables
edata – Experimental data

void fdJrzdsigma(int const ie, int const nroots, realtype const t, AmiVector const &x, ExpData const &edata)

Compute sensitivity of event measurement negative log-likelihood Jz w.r.t. standard deviation sigmaz.

Parameters:

ie – event index
nroots – event index
t – current timepoint
x – state variables
edata – pointer to experimental data instance

void fspl(realtype t)

Spline functions.

Parameters:: t – timepoint

void fsspl(realtype t)

Parametric derivatives of splines functions.

Parameters:: t – timepoint

void fw(realtype t, realtype const *x, bool include_static = true)

Compute recurring terms in xdot.

Parameters:

t – Timepoint
x – Array with the states
include_static – Whether to (re-)evaluate only dynamic expressions (false) or also static expressions (true). Dynamic expressions are those that depend directly or indirectly on time, static expressions are those that don’t.

void fdwdp(realtype t, realtype const *x, bool include_static = true)

Compute parameter derivative for recurring terms in xdot.

Parameters:

t – Timepoint
x – Array with the states
include_static – Whether to (re-)evaluate only dynamic expressions (false) or also static expressions (true). Dynamic expressions are those that depend directly or indirectly on time, static expressions are those that don’t.

void fdwdx(realtype t, realtype const *x, bool include_static = true)

Compute state derivative for recurring terms in xdot.

Parameters:

t – Timepoint
x – Array with the states
include_static – Whether to (re-)evaluate only dynamic expressions (false) or also static expressions (true). Dynamic expressions are those that depend directly or indirectly on time, static expressions are those that don’t.

void fdwdw(realtype t, realtype const *x, bool include_static = true)

Compute self derivative for recurring terms in xdot.

Parameters:

t – Timepoint
x – Array with the states
include_static – Whether to (re-)evaluate only dynamic expressions (false) or also static expressions (true). Dynamic expressions are those that depend directly or indirectly on time, static expressions are those that don’t.

virtual void fx_rdata(realtype *x_rdata, realtype const *x_solver, realtype const *tcl, realtype const *p, realtype const *k)

Compute fx_rdata.