cosapp.drivers.time.base

Classes

AbstractTimeDriver([name, owner, ...])

Generic implementation of a time driver with managed time step.

class cosapp.drivers.time.base.AbstractTimeDriver(name='Time driver', owner: System | None = None, time_interval: tuple[float, float] | None = None, dt: float | None = None, record_dt: bool = False, **options)

Bases: Driver

Generic implementation of a time driver with managed time step. Specialization of derived classes is achieved by the implementation of abstract method _update_transients

add_recorder(recorder: AnyRecorder, period: Number | None = None) → AnyRecorder

Add an internal recorder storing the time evolution of values of interest.

Parameters:

• [BaseRecorder] (- recorder) – The recorder to be added.

• [Number (- period) – Recording period. If None (default), data are recorded at all time steps.

• optional] – Recording period. If None (default), data are recorded at all time steps.

compute() → None

Simulate the time-evolution of owner System over a prescribed time interval

property dt: None | Number

Nominal time step (None if unspecified)

property event_data: DataFrame

DataFrame detailing all event cascades occurring during simulation

Type:

pandas.DataFrame

log_debug_message(handler: HandlerWithContextFilters, record: LogRecord, format=LogFormat.RAW) → bool

Callback method on the system to log more detailed information.

This method will be called by the log handler when log_context() is active if the logging level is lower or equals to VERBOSE_LEVEL. It allows the object to send additional log message to help debugging a simulation.

Parameters:

• handler (HandlerWithContextFilters) – Log handler on which additional message should be published.

• record (logging.LogRecord) – Log record

• format (LogFormat) – Format of the message

Returns:

Should the provided record be logged?

Return type:

bool

record_dt

property recorded_dt: ndarray

list of time steps recorded during driver execution

Type:

numpy.ndarray

property recorded_events: list[EventRecord]

list of recorded event cascades

Type:

list[EventRecord]

property recording_period: Number

Recording period of time driver’s internal recorder

Type:

float

run_once() → None

Run time driver once, assuming driver has already been initialized.

property scenario: Scenario

the simulation scenario, defining initial and boundary conditions

Type:

Scenario

set_scenario(name='scenario', init: dict[str, Any] = {}, values: dict[str, Any] = {}, stop: str | Event | None = None) → None

Define a simulation scenario, from initial and boundary conditions.

Parameters:

• init (dict) – Dictionary of initial conditions, of the kind {variable: value}

• values (dict) – Dictionary of boundary conditions, of the kind {variable: value} Explicit time dependency may be specified, as {variable: ‘cos(omega * t)’}, for example

• name (str) – Name of the scenario (by default, ‘scenario’)

setup_run() → None

Setup the driver once before starting the simulation and before calling the systems setup_run.

property system_update_signal

Signal emitted after the owner system is updated, at each time step

property time: Number

Current simulation time

property time_interval: tuple[Number, Number]

Time interval covered by driver

transition(time: float, events: Collection[Event] = ()) → None

Execute owner system transition and reinitialize sub-drivers

static value_and_derivative(var: TimeUnknown) → tuple[float, float] | tuple[ndarray, ndarray]