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 amici::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)

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

~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 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, const AmiVector &x)

Initialize initial state sensitivities.

Parameters

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

void initEvents(const AmiVector &x, const AmiVector &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

const double *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(const std::vector<ParameterScaling> &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.

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 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, const realtype t, const AmiVector &x)

Get time-resolved w.

Parameters

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

void getObservable(gsl::span<realtype> y, const realtype t, const AmiVector &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, const realtype t, const AmiVector &x, const AmiVectorArray &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, const int it, const ExpData *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<const realtype> sy, const int it, const ExpData *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, const int it, const AmiVector &x, const ExpData &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, const int it, const AmiVector &x, const AmiVectorArray &sx, const ExpData &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, const int it, const AmiVector &x, const ExpData &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, const int it, const AmiVector &x, const ExpData &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, const int ie, const realtype t, const AmiVector &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, const int ie, const realtype t, const AmiVector &x, const AmiVectorArray &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, const int 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, const int ie, const realtype t, const AmiVector &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, const int ie, const realtype t, const AmiVector &x, const AmiVectorArray &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, const int ie, const int nroots, const realtype t, const ExpData *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, const int ie, const int nroots, const realtype t, const ExpData *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, const int ie, const int nroots, const realtype t, const AmiVector &x, const ExpData &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, const int ie, const int nroots, const realtype t, const AmiVector &x, const ExpData &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, const int ie, const int nroots, const realtype t, const AmiVector &x, const AmiVectorArray &sx, const ExpData &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, const int ie, const int nroots, const realtype t, const AmiVector &x, const ExpData &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, const int ie, const int nroots, const realtype t, const AmiVector &x, const ExpData &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, const realtype t, const int ie, const AmiVector &x, const AmiVectorArray &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, const int ie, const realtype t, const AmiVector &xdot, const AmiVector &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, const int ie, const realtype t, const AmiVector &x_old, const AmiVector &xdot, const AmiVector &xdot_old, const std::vector<realtype> &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, const int ie, const realtype t, const AmiVector &x, const AmiVector &xdot, const AmiVector &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, const int ie, const realtype t, const AmiVector &x, const AmiVector &xB, const AmiVector &xdot, const AmiVector &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(const std::vector<int> &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(const int *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<const realtype> array, ModelQuantity model_quantity) const

Check if the given array has only finite elements.

For (1D) spans.

Parameters

array –
model_quantity – The model quantity array corresponds to

Returns

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

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, const AmiVector &x)

Compute/get initial value for initial state sensitivities.

Parameters

sx – Output buffer for state sensitivities
x – State variables

void fsx0_fixedParameters(AmiVectorArray &sx, const AmiVector &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, const AmiVector &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, const AmiVectorArray &sx_solver, const AmiVector &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(const std::vector<int> &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.

const AmiVectorArray &get_dxdotdp() const

getter for dxdotdp (matlab generated)

Returns: dxdotdp

const SUNMatrixWrapper &get_dxdotdp_full() const

getter for dxdotdp (python generated)

Returns: dxdotdp

virtual void fdeltaqB(realtype *deltaqB, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, int ip, int ie, const realtype *xdot, const realtype *xdot_old, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *w, int ip, int ie, const realtype *xdot, const realtype *xdot_old, const realtype *sx, const realtype *stau, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, int ie, const realtype *xdot, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, int ie, const realtype *xdot, const realtype *xdot_old, const realtype *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, const realtype *p, const realtype *k, const realtype *rz, const realtype *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, const realtype *p, const realtype *k, const realtype *rz, const realtype *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, const realtype *p, const realtype *k, const realtype *y, const realtype *sigmay, const realtype *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, const realtype *p, const realtype *k, const realtype *y, const realtype *sigmay, const realtype *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, const realtype *p, const realtype *k, const realtype *z, const realtype *sigmaz, const realtype *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, const realtype *p, const realtype *k, const realtype *z, const realtype *sigmaz, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *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, const realtype t, const realtype *p, const realtype *k, const realtype *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, const realtype t, const realtype *p, const realtype *k, const realtype *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, const realtype t, const realtype *p, const realtype *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 fdwdp(realtype *dwdp, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *w, const realtype *tcl, const realtype *stcl)

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

virtual void fdwdp(realtype *dwdp, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *w, const realtype *tcl, const realtype *stcl, int ip)

Model-specific sensitivity 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
ip – sensitivity parameter index

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 fdwdx(realtype *dwdx, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *w, const realtype *tcl)

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

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 fdwdw(realtype *dwdw, realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *w, const realtype *tcl)

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

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

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

virtual void fdydx(realtype *dydx, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *w, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *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, const realtype *p, const realtype *k, const realtype *z, const realtype *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, const realtype *p, const realtype *k, const realtype *y, const realtype *sigmay, const realtype *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, const realtype *p, const realtype *k, const realtype *z, const realtype *sigmaz, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *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, const realtype t, const realtype *p, const realtype *k, const realtype *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, const realtype t, const realtype *p, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *tcl, const realtype *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, const realtype t, const realtype *x0, const realtype *p, const realtype *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, const realtype t, const realtype *x0, const realtype *p, const realtype *k, int ip, gsl::span<const int> 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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *tcl)

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

virtual void fx0(realtype *x0, const realtype t, const realtype *p, const realtype *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, const realtype t, const realtype *p, const realtype *k, gsl::span<const int> 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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *h, const realtype *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, const realtype t, const realtype *x, const realtype *p, const realtype *k, const realtype *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

virtual void fdx_rdatadx_solver(realtype *dx_rdatadx_solver, const realtype *x, const realtype *tcl, const realtype *p, const realtype *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 fdx_rdatadp(realtype *dx_rdatadp, const realtype *x, const realtype *tcl, const realtype *p, const realtype *k, const int 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, const realtype *x, const realtype *tcl, const realtype *p, const realtype *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 fdtotal_cldx_rdata(realtype *dtotal_cldx_rdata, const realtype *x_rdata, const realtype *p, const realtype *k, const realtype *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 fdtotal_cldp(realtype *dtotal_cldp, const realtype *x_rdata, const realtype *p, const realtype *k, const int 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

Public Members

bool pythonGenerated: 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

AmiciApplication *app = &defaultContext : AMICI application context

Protected Functions

void writeSliceEvent(gsl::span<const realtype> slice, gsl::span<realtype> buffer, const int 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<const realtype> slice, gsl::span<realtype> buffer, const int 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(const std::vector<realtype> &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(const std::vector<realtype> &sllh, const std::vector<realtype> &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, const AmiVector &x)

Compute observables / measurements.

Parameters

t – Current timepoint
x – Current state

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

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

Parameters

t – Current timepoint
x – Current state

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

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

Parameters

t – Current timepoint
x – Current state

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

Compute standard deviation of measurements.

Parameters

it – Timepoint index
edata – Experimental data

void fdsigmaydp(int it, const ExpData *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, const ExpData *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, const AmiVector &y, const ExpData &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, const AmiVector &x, const ExpData &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, const AmiVector &x, const ExpData &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(const int it, const AmiVector &x, const ExpData &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(const int it, const AmiVector &x, const ExpData &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, const AmiVector &x)

Compute event-resolved output.

Parameters

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

void fdzdp(int ie, realtype t, const AmiVector &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, const AmiVector &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, const AmiVector &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, const AmiVector &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, const AmiVector &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(const int ie, const int nroots, const realtype t, const ExpData *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, const ExpData *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, const AmiVector &z, const ExpData &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(const int ie, const int nroots, const realtype t, const AmiVector &x, const ExpData &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(const int ie, const int nroots, const realtype t, const AmiVector &x, const ExpData &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(const int ie, const int nroots, realtype t, const AmiVector &x, const ExpData &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(const int ie, const int nroots, realtype t, const AmiVector &x, const ExpData &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, const AmiVector &rz, const ExpData &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(const int ie, const int nroots, const realtype t, const AmiVector &x, const ExpData &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(const int ie, const int nroots, const realtype t, const AmiVector &x, const ExpData &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 fw(realtype t, const realtype *x)

Compute recurring terms in xdot.

Parameters

t – Timepoint
x – Array with the states

void fdwdp(realtype t, const realtype *x)

Compute parameter derivative for recurring terms in xdot.

Parameters

t – Timepoint
x – Array with the states

void fdwdx(realtype t, const realtype *x)

Compute state derivative for recurring terms in xdot.

Parameters

t – Timepoint
x – Array with the states

void fdwdw(realtype t, const realtype *x)

Compute self derivative for recurring terms in xdot.

Parameters

t – Timepoint
x – Array with the states

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

Compute fx_rdata.