Functions Library

Define functions that can update the simulation parameters.

Base Operations

Define simple operations to initialize parameters in the configuration.

class Sum(add: Iterable[Any] = <factory>, subtract: Iterable[Any] | None = None)

Sum the elements in add and subtract the elements in subtract.

add: Iterable[Any]

the values to sum

subtract: Iterable[Any] | None = None

the values to subtract

property operation_value: Any

Value used with base operators.

Returns:

the current evaluation of the function

Return type:

Any

eval() → Any

Sum the elements in add and subtract the elements in subtract.

Returns:

the sum result

Return type:

Any

class Product(factors: Iterable[Any] = <factory>, inverses: Iterable[Any] | None = None)

Multiply all values provided in factors list and divide the result with the multiplication of the values in the inverses list.

..note:: It uses the numpy multiply function

factors: Iterable[Any]

The values to multiply

inverses: Iterable[Any] | None = None

The values to inverse then multiply

property operation_value: Any

Value used with base operators.

Returns:

the current evaluation of the function

Return type:

Any

eval() → Any

Child Functions have to overwrite this method to compute the function result.

Watchers

Watcher are useful functions to use to set Output Functions in the configuration.

class WatchQuantity(quantity: Quantity[Any])

Return the current value of the watch quantity.

quantity: Quantity[Any]

The quantities to watch

property operation_value: Any

Value used with base operators.

Returns:

the current evaluation of the function

Return type:

Any

eval() → Any

Just return the watched quantity current value

Returns:

the watched quantity current value

Return type:

np.float64 | np.ndarray

class SumBlocksQuantity(quantity_id: str, elements: Iterable[Block] = <factory>)

Sum the current values of quantities with the given name for each the blocks.

quantity_id: str

block local id of the quantity to sum

elements: Iterable[Block]

the blocks holding the quantity to sum

property operation_value: Any

Value used with base operators.

Returns:

the current evaluation of the function

Return type:

Any

eval() → Any

Sum the quantities

Returns:

the sum of the quantities

Return type:

Any

Piecewise

Declare functions to define piecewise functions in the configuration

class PiecewiseLinear(points_abscissas: NDArray[np.float64], points_ordinates: NDArray[np.float64], left_value: np.float64 | None = None, right_value: np.float64 | None = None)

Defines an evaluation method to get the value of a piecewise function for the given time.

points_abscissas: NDArray[np.float64]

The function abscissas

points_ordinates: NDArray[np.float64]

The function ordinates

left_value: np.float64 | None = None

Function value when the evaluation point is before the provided abscissas. If it is not provided, it is the first ordinate value.

right_value: np.float64 | None = None

Function value when the evaluation point is after the provided abscissas. If it is not provided, it is the last ordinate value.

eval(time: float64) → Any

Evaluate piecewise function for the given time.

Parameters:

time (np.float64) – the evaluated time

Returns:

the activation point_value

Return type:

np.float64

class PiecewiseLinearPeriodic(period: np.float64, points_abscissas: NDArray[np.float64], points_ordinates: NDArray[np.float64])

Defines an evaluation method to get the value of a piecewise periodic function for the given time.

period: np.float64

The function period

points_abscissas: NDArray[np.float64]

The function abscissas

points_ordinates: NDArray[np.float64]

The function ordinates

eval(time: float64) → Any

Evaluate piecewise periodic function for the given time.

Parameters:

time (np.float64) – the evaluated time

Returns:

the activation point_value

Return type:

np.float64

class RescaleTwoPhasesFunction(rescaled_period: float, reference_function: list[tuple[float, float]], alpha: float, phases: list[int])

Rescale each part of the input function (a linear interpollation) depending on the proportion of variation of phase 0 when the period differs from the reference function.

reference_function: list[tuple[float, float]]

The reference function. Coordinates format is (abscissa, ordinate)

phases: list[int]

For each intervals point of the reference, determine if it belong to phase 0 or 1.

alpha: float

Proportion of the variation of phase 0

rescaled_period: float

The actual function period

eval(time: float) → Any

Evaluate the function.

Parameters:

time (float) – the evaluated time

Returns:

the function value

Return type:

np.float64

Trigonometric

Declare configuration function to set parameter with trigonometric function.

class SinusOffset(offset_value: float, amplitude: float, frequency: float, phase_shift: float)

Defines an evaluation method to get the value of a offset sinus function.

offset_value: float

Offset value of the function

amplitude: float

Peak amplitude of the function

frequency: float

Frequency of the function

phase_shift: float

Phase shift of the function

eval(time: float) → Any

Evaluate function value at the given time.

Parameters:

time (float) – evaluation time

Returns:

the function value

Return type:

np.float64

First Order

class FirstOrder(times_start: NDArray[np.float64], amplitudes: NDArray[np.float64], time_constants: NDArray[np.float64], baseline_value: np.float64)

Defines an evaluation method to get the value of a sum of first order functions with heavyside for the given time.

times_start: NDArray[np.float64]

Start times of first order components

amplitudes: NDArray[np.float64]

Amplitudes of first order components

time_constants: NDArray[np.float64]

Time constants of first order components

baseline_value: np.float64

Baseline value of the function

eval(time: float64) → Any

Evaluate function value at the given time.

Parameters:

time (np.float64) – evaluation time

Returns:

the function value

Return type:

np.float64