API Reference

API Reference#

codegeneration#

class goss.codegeneration.BodyRepr(value)[source]#: An enumeration.

class goss.codegeneration.GossCodeGenerator(ode, field_states: Optional[list[str]] = None, field_parameters: Optional[list[str]] = None, monitored: Optional[list[str]] = None, code_params: Optional[dict[str, Any]] = None, add_signature_to_name: bool = False)[source]#

Class for generating an implementation of a goss ODE

class_code()[source]#: Generate the goss class code

code_dict(ode, monitored=None, include_init=True, include_index_map=True, indent=0)#

Generates a dict of code snippets

Parameters:

ode (gotran.ODE) – The ODE for which code will be generated
monitored (list) – A list of name of monitored intermediates for which evaluation code will be generated.
include_init (bool) – If True, code for initializing the states and parameters will be generated.
include_index_map (bool) – If True, code for mapping a str to a index for the corresponding, state, parameters or monitored will be generated.
indent (int) – The indentation level for the generated code

file_code()[source]#: Generate the goss file code

property float_type#: Return the float type

classmethod indent_and_split_lines(code_lines, indent=0, ret_lines=None, no_line_ending=False)#: Combine a set of lines into a single string

init_parameters_code(ode, indent=0)#: Generate code for setting parameters

init_states_code(ode, indent=0)#: Generate code for setting initial condition

monitor_name_to_index_code(ode, monitored, indent=0)#: Return code for index handling for monitored

monitored_enum_code(monitored, indent=0)#: Generate enum for model parameters

param_name_to_index_code(ode, indent=0)#: Return code for index handling for a parameter

parameters_enum_code(ode, indent=0)#: Generate enum for model parameters

state_name_to_index_code(ode, indent=0)#: Return code for index handling for states

states_enum_code(ode, indent=0)#: Generate enum for state variables

classmethod wrap_body_with_function_prototype(body_lines, name, args, return_type='', comment='', const=False)#: Wrap a passed body of lines with a function prototype

class goss.codegeneration.GossCodeGeneratorParameters(*, state_repr: StateRepr = StateRepr.named, body_repr: BodyRepr = BodyRepr.named, use_cse: bool = False, generate_forward_backward_subst: bool = False, generate_jacobian: bool = False, generate_lu_factorization: bool = False, optimize_exprs: OptimizeExprs = OptimizeExprs.none)[source]#

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

property model_computed_fields: dict[str, pydantic.fields.ComputedFieldInfo]#

Get the computed fields of this model instance.

Returns:: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Model#

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters:

_fields_set – The set of field names accepted for the Model instance.
values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) → Model#

Usage docs: https://docs.pydantic.dev/2.4/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep – Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → dict[str, Any]#

Usage docs: https://docs.pydantic.dev/2.4/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode – The mode in which to_python should run. If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any Python objects.
include – A list of fields to include in the output.
exclude – A list of fields to exclude from the output.
by_alias – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset – Whether to exclude fields that are unset or None from the output.
exclude_defaults – Whether to exclude fields that are set to their default value from the output.
exclude_none – Whether to exclude fields that have a value of None from the output.
round_trip – Whether to enable serialization and deserialization round-trip support.
warnings – Whether to log warnings when invalid fields are encountered.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → str#

Usage docs: https://docs.pydantic.dev/2.4/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent – Indentation to use in the JSON output. If None is passed, the output will be compact.
include – Field(s) to include in the JSON output. Can take either a string or set of strings.
exclude – Field(s) to exclude from the JSON output. Can take either a string or set of strings.
by_alias – Whether to serialize using field aliases.
exclude_unset – Whether to exclude fields that have not been explicitly set.
exclude_defaults – Whether to exclude fields that have the default value.
exclude_none – Whether to exclude fields that have a value of None.
round_trip – Whether to use serialization/deserialization between JSON and class instance.
warnings – Whether to show any warnings that occurred during serialization.

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None#

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields: ClassVar[dict[str, FieldInfo]] = {'body_repr': FieldInfo(annotation=BodyRepr, required=False, default=<BodyRepr.named: 'named'>, description='Representation of the body', json_schema_extra={'type': 'named|array|reused_array'}), 'generate_forward_backward_subst': FieldInfo(annotation=bool, required=False, default=False, description='Generate forward backward substitions for a factorized jacobian'), 'generate_jacobian': FieldInfo(annotation=bool, required=False, default=False, description='Generate Jacobian matrix'), 'generate_lu_factorization': FieldInfo(annotation=bool, required=False, default=False, description='Generate Lu factorization'), 'optimize_exprs': FieldInfo(annotation=OptimizeExprs, required=False, default=<OptimizeExprs.none: 'none'>, description='Optimize expressions', json_schema_extra={'type': 'none|numerals|numerals_symbols'}), 'state_repr': FieldInfo(annotation=StateRepr, required=False, default=<StateRepr.named: 'named'>, description='Representation of the state', json_schema_extra={'type': 'named|array'}), 'use_cse': FieldInfo(annotation=bool, required=False, default=False, description='Use sympy cse to optimize common sub expressions')}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str]#

Returns the set of fields that have been set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') → dict[str, Any]#

Generates a JSON schema for a model class.

Parameters:

by_alias – Whether to use attribute aliases or not.
ref_template – The reference template.
schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str#

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context: Any) → None#: Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: Optional[dict[str, Any]] = None) → bool | None#

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors – Whether to raise errors, defaults to True.
_parent_namespace_depth – The depth level of the parent namespace, defaults to 2.
_types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None) → Model#

Validate a pydantic model instance.

Parameters:

obj – The object to validate.
strict – Whether to raise an exception on invalid fields.
from_attributes – Whether to extract data from object attributes.
context – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model#

Validate the given JSON data against the Pydantic model.

Parameters:

json_data – The JSON data to validate.
strict – Whether to enforce types strictly.
context – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Raises:

ValueError – If json_data is not a JSON string.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model#

Validate the given object contains string data against the Pydantic model.

Parameters:

obj – The object contains string data to validate.
strict – Whether to enforce types strictly.
context – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

class goss.codegeneration.OptimizeExprs(value)[source]#: An enumeration.

class goss.codegeneration.StateRepr(value)[source]#: An enumeration.

compilemodule#

goss.compilemodule.cppyy_jit(ode: ODE, field_states: Optional[list[str]] = None, field_parameters: Optional[list[str]] = None, monitored: Optional[list[str]] = None, code_params: Optional[dict[str, Any]] = None) → c_void_p[source]#

Generate a goss::ODEParameterized from a gotran ode and JIT compile it

Parameters:

ode (gotran.ODE) – The gotran ode, either as an ODE or as an ODERepresentation
field_states (list) – A list of state names, which should be treated as field states
field_parameters (list) – A list of parameter names, which should be treated as field parameters
monitored (list) – A list of names of intermediates of the ODE. Code for monitoring the intermediates will be generated.
code_params (dict) – Parameters controlling the code generation

goss.compilemodule.jit(ode: ODE, field_states: Optional[list[str]] = None, field_parameters: Optional[list[str]] = None, monitored: Optional[list[str]] = None, code_params: Optional[dict[str, Any]] = None)[source]#

Generate a goss::ODEParameterized from a gotran ode and JIT compile it

Parameters:

ode (gotran.ODE) – The gotran ode, either as an ODE or as an ODERepresentation
field_states (list) – A list of state names, which should be treated as field states
field_parameters (list) – A list of parameter names, which should be treated as field parameters
monitored (list) – A list of names of intermediates of the ODE. Code for monitoring the intermediates will be generated.
code_params (dict) – Parameters controlling the code generation

dolfinutils#

class goss.dolfinutils.DOLFINODESystemSolver(mesh, odes, domains=None, params=None)[source]#

DOLFINODESystemSolver is an adapter class for goss.ODESystemSolver making interfacing DOLFIN easier

from_field_states()[source]#: Copy values in stored field states to v

initial_conditions_to_field_states()[source]#: Copy values from initial conditions to stored field states

restore_states()[source]#: Restore the states from any saved states

save_states()[source]#: Save the present state

solution_fields()[source]#: Return tuple of previous and current solution objects.

step(interval: Tuple[float, float]) → None[source]#

Solve on the given time step (t0, t1). End users are recommended to use solve instead.

Parameters:

interval (Tuple[float, float]) – The time interval (t0, t1) for the step
v (dolfin.Function) – The function to store the solution

to_field_states()[source]#: Copy values in v to stored field states

update_parameters()[source]#: Update the values in the any changed parameters

ode#

class goss.ode.LinearizedEval(rhs, linear)[source]#

count(value, /)#: Return number of occurrences of value.

index(value, start=0, stop=9223372036854775807, /)#

Return first index of value.

Raises ValueError if the value is not present.

linear: Optional[ndarray]#: Alias for field number 1

rhs: ndarray#: Alias for field number 0

solvers#

class goss.solvers.AdaptiveExplicitSolver(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.AdaptiveImplicitSolver(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

num_jac_comp() → int#: Return the number of times the jacobian has been computed

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.AdaptiveSolver(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.BasicImplicitEuler(*args, **kwargs)[source]#

FIXME: This is not working as expected

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

num_jac_comp() → int#: Return the number of times the jacobian has been computed

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.ESDIRK(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property ndtsa#: Number of accepted timesteps

property ndtsr#: Number of rejected timesteps

property nfevals#: Number of right hand side evaluations

num_jac_comp() → int#: Return the number of times the jacobian has been computed

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.ESDIRK23a(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any][source]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property ndtsa#: Number of accepted timesteps

property ndtsr#: Number of rejected timesteps

property nfevals#: Number of right hand side evaluations

num_jac_comp() → int#: Return the number of times the jacobian has been computed

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.ESDIRK4O32(*args, **kwargs)[source]#

FIXME: This is not working as expected

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property ndtsa#: Number of accepted timesteps

property ndtsr#: Number of rejected timesteps

property nfevals#: Number of right hand side evaluations

num_jac_comp() → int#: Return the number of times the jacobian has been computed

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.ExplicitEuler(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.GOSSAdaptiveSolvers(value)[source]#: An enumeration.

class goss.solvers.GOSSExplicitSolvers(value)[source]#: An enumeration.

class goss.solvers.GOSSImplicitSolvers(value)[source]#: An enumeration.

class goss.solvers.GOSSNonAdaptiveSolvers(value)[source]#: An enumeration.

class goss.solvers.GOSSSolvers(value)[source]#: An enumeration.

class goss.solvers.GRL1(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any][source]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.GRL2(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.ImplicitEuler(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any][source]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

num_jac_comp() → int#: Return the number of times the jacobian has been computed

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.ImplicitODESolver(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any][source]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

num_jac_comp() → int[source]#: Return the number of times the jacobian has been computed

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.ODESolver(*args, **kwargs)[source]#

Base class for ODESolver.

Your can instantiate an ODE solver in many different ways, but the general theme is that an ode solver need an ode-model that is typically coming from a gotran ode-file.

The default way to instantiate a solver is to provide the ode as the first argument

solver = ExplicitEuler(ode)

where ode is of type goss.ode.ODE. Another way is to create an empty ODESolver

solver = ExplicitEuler()

but then you need to attach an ode later in order to use it

solver.attach(ode)

copy()[source]#: Make a copy of the solver

static default_parameters() → dict[str, Any][source]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)[source]#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE[source]#: Get the ODE from the cpp object

get_parameter(name: str) → Any[source]#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)[source]#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray[source]#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])[source]#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.RK2(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.RK4(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.RKF32(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property ndtsa#: Number of accepted timesteps

property ndtsr#: Number of rejected timesteps

property nfevals#: Number of right hand side evaluations

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.RL1(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.RL2(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

class goss.solvers.ThetaSolver(*args, **kwargs)[source]#

copy()#: Make a copy of the solver

static default_parameters() → dict[str, Any][source]#: Dictionary with the default parameters

forward(y: ndarray, t: float, interval: float)#

Do one forward iteration

Parameters:

y (np.ndarray) – Array with the current state values. Note that this array will be mutated and contain the updated states after call to this function.
t (float) – Time point
interval (float) – Interval to step

get_ode() → ODE#: Get the ODE from the cpp object

get_parameter(name: str) → Any#

Get the current value of a parameter

Parameters:: name (str) – The name of the parameter of interest
Returns:: The value of the parameter
Return type:: Any

property internal_time_step: float#: Time step used internally by the solver

property is_adaptive: bool#: Flag to indicate whether the solver is adaptive for not

num_jac_comp() → int#: Return the number of times the jacobian has been computed

property num_states#: Number of states in the ODE

property parameters: dict[str, Any]#: Dictionaty with the current parameters

set_parameter(name: str, value: Any)#

Set the value of a parameter

Parameters:

name (str) – Name of the parameter you want to set
value (Any) – The new value of the parameter

Raises:

KeyError – If the name is not a valid parameter
TypeError – If the type of the new value does not match the original value

solve(t: ndarray, y0: Optional[ndarray] = None) → ndarray#

Solve the ode for a given number of time steps

Parameters:

t (np.ndarray) – The time steps
y0 (Optional[np.ndarray], optional) – Initial conditions. If not provided (default), then the default initial conditions will be used.

Returns:

The states at each time point.

Return type:

np.ndarray

update_parameters(parameters: dict[str, Any])#

Update the parameters given a dictionary

Parameters:: parameters (dict[str, Any]) – The new parameters

API Reference

Contents

API Reference#

codegeneration#

compilemodule#

dolfinutils#

ode#

solvers#

systemsolver#