dynabench.initial

Module containing different initial condition generators.

Classes

`Composite`(*components)	Composite initial condition generator consisting of multiple initial conditions for the same grid.
`Constant`([value])	Initial condition with a constant value.
`InitialCondition`([parameters])	Base class for all initial conditions.
`RandomUniform`([low, high])	Initial condition with random values drawn from a uniform distribution.
`SumOfGaussians`([components, zero_level])	Initial condition generator for the sum of gaussians.
`WrappedGaussians`([components, zero_level, ...])	Initial condition generator for the sum of wrapped gaussians.

class dynabench.initial.Composite(*components: list)[source]

Bases: InitialCondition

Composite initial condition generator consisting of multiple initial conditions for the same grid. Convenience class to generate multiple initial conditions for different variables.

Parameters:: components (list) – List of single initial conditions.

generate(grid: Grid, random_state: int = 42)[source]

Generate the initial condition.

Parameters:: grid (dynabench.grid.Grid) – The grid on which the initial condition is to be generated.
Returns:: The initial condition.
Return type:: np.ndarray

property num_variables: Get the number of variables.

class dynabench.initial.Constant(value: float = 0.0, **kwargs)[source]

Bases: InitialCondition

Initial condition with a constant value.

Parameters:: value (float, default 0.0) – The value of the constant.

generate(grid: Grid)[source]

Generate the initial condition.

Parameters:: grid (dynabench.grid.Grid) – The grid on which the initial condition is to be generated.
Returns:: The initial condition.
Return type:: np.ndarray

class dynabench.initial.InitialCondition(parameters: dict = {}, **kwargs)[source]

Bases: object

Base class for all initial conditions.

Parameters:: parameters (dict, default {}) – Dictionary of parameters for the initial condition.

generate(grid: Grid, random_state: int = 42)[source]

Generate the initial condition.

Parameters:: grid (dynabench.grid.Grid) – The grid on which the initial condition is to be generated.
Returns:: The initial condition.
Return type:: np.ndarray

property num_variables: Get the number of variables.

class dynabench.initial.RandomUniform(low: float = 0.0, high: float = 1.0, **kwargs)[source]

Bases: InitialCondition

Initial condition with random values drawn from a uniform distribution.

Parameters:

low (float, default 0.0) – The lower bound of the uniform distribution.
high (float, default 1.0) – The upper bound of the uniform distribution.

generate(grid: Grid, random_state: int = 42)[source]

Generate the initial condition.

Parameters:: grid (dynabench.grid.Grid) – The grid on which the initial condition is to be generated.
Returns:: The initial condition.
Return type:: np.ndarray

class dynabench.initial.SumOfGaussians(components: int = 1, zero_level: float = 0.0, **kwargs)[source]

Bases: InitialCondition

Initial condition generator for the sum of gaussians.

Parameters:

grid_size (tuple, default (64, 64)) – The size of the grid.
components (int, default 1) – The number of gaussian components.
zero_level (float, default 0.0) – The zero level of the initial condition.

generate(grid: Grid, random_state: int = 42)[source]

Generate the initial condition.

Parameters:: grid (dynabench.grid.Grid) – The grid on which the initial condition is to be generated.
Returns:: The initial condition.
Return type:: np.ndarray

class dynabench.initial.WrappedGaussians(components: int = 1, zero_level: float = 0.0, periodic_levels: int = 10, **kwargs)[source]

Bases: InitialCondition

Initial condition generator for the sum of wrapped gaussians.

Parameters:

components (int, default 1) – The number of gaussian components.
zero_level (float, default 0.0) – The zero level of the initial condition.
periodic_levels (int or list, default 10) – The number of periodic levels to calculate the wrapped distribution. \(p_w(\theta)=\sum_{k=-\infty}^\infty {p(\theta+2\pi k)}\)

generate(grid: Grid, random_state: int = 42)[source]

Generate the initial condition.

Parameters:: grid (dynabench.grid.Grid) – The grid on which the initial condition is to be generated.
Returns:: The initial condition.
Return type:: np.ndarray