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Diffused Noise¤

exponax.ic.DiffusedNoise ¤

Bases: BaseRandomICGenerator

Source code in exponax/ic/_diffused_noise.py 
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class DiffusedNoise(BaseRandomICGenerator):
    num_spatial_dims: int
    domain_extent: float
    intensity: float
    zero_mean: bool
    std_one: bool
    max_one: bool

    def __init__(
        self,
        num_spatial_dims: int,
        *,
        domain_extent: float = 1.0,
        intensity=0.001,
        zero_mean: bool = True,
        std_one: bool = False,
        max_one: bool = False,
    ):
        """
        Randomly generated initial condition consisting of a diffused noise
        field.

        The original noise is drawn in state space with a uniform normal
        distribution. After the application of the diffusion operator, the
        spectrum decays exponentially with a rate of `intensity`.

        **Arguments**:
            - `num_spatial_dims`: The number of spatial dimensions `d`.
            - `domain_extent`: The extent of the domain. Defaults to `1.0`. This
                indirectly affects the intensity of the noise. It is best to
                keep it at `1.0` and just adjust the `intensity` instead.
            - `intensity`: The intensity of the noise. Defaults to `0.001`.
            - `zero_mean`: Whether to zero the mean of the noise. Defaults to
                `True`.
            - `std_one`: Whether to normalize the noise to have a standard
                deviation of one. Defaults to `False`.
            - `max_one`: Whether to normalize the noise to the maximum absolute
                value of one. Defaults to `False`.
        """
        if not zero_mean and std_one:
            raise ValueError("Cannot have `zero_mean=False` and `std_one=True`.")
        if std_one and max_one:
            raise ValueError("Cannot have `std_one=True` and `max_one=True`.")
        self.num_spatial_dims = num_spatial_dims
        self.domain_extent = domain_extent
        self.intensity = intensity
        self.zero_mean = zero_mean
        self.std_one = std_one
        self.max_one = max_one

    def __call__(
        self, num_points: int, *, key: PRNGKeyArray
    ) -> Float[Array, "1 ... N"]:
        noise_shape = (1,) + spatial_shape(self.num_spatial_dims, num_points)
        noise = jr.normal(key, shape=noise_shape)

        diffusion_stepper = Diffusion(
            self.num_spatial_dims,
            self.domain_extent,
            num_points,
            1.0,
            diffusivity=self.intensity,
        )
        ic = diffusion_stepper(noise)

        if self.zero_mean:
            ic = ic - jnp.mean(ic)

        if self.std_one:
            ic = ic / jnp.std(ic)

        if self.max_one:
            ic = ic / jnp.max(jnp.abs(ic))

        return ic
__init__ ¤
__init__(
    num_spatial_dims: int,
    *,
    domain_extent: float = 1.0,
    intensity=0.001,
    zero_mean: bool = True,
    std_one: bool = False,
    max_one: bool = False
)

Randomly generated initial condition consisting of a diffused noise field.

The original noise is drawn in state space with a uniform normal distribution. After the application of the diffusion operator, the spectrum decays exponentially with a rate of intensity.

Arguments: - num_spatial_dims: The number of spatial dimensions d. - domain_extent: The extent of the domain. Defaults to 1.0. This indirectly affects the intensity of the noise. It is best to keep it at 1.0 and just adjust the intensity instead. - intensity: The intensity of the noise. Defaults to 0.001. - zero_mean: Whether to zero the mean of the noise. Defaults to True. - std_one: Whether to normalize the noise to have a standard deviation of one. Defaults to False. - max_one: Whether to normalize the noise to the maximum absolute value of one. Defaults to False.

Source code in exponax/ic/_diffused_noise.py 
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def __init__(
    self,
    num_spatial_dims: int,
    *,
    domain_extent: float = 1.0,
    intensity=0.001,
    zero_mean: bool = True,
    std_one: bool = False,
    max_one: bool = False,
):
    """
    Randomly generated initial condition consisting of a diffused noise
    field.

    The original noise is drawn in state space with a uniform normal
    distribution. After the application of the diffusion operator, the
    spectrum decays exponentially with a rate of `intensity`.

    **Arguments**:
        - `num_spatial_dims`: The number of spatial dimensions `d`.
        - `domain_extent`: The extent of the domain. Defaults to `1.0`. This
            indirectly affects the intensity of the noise. It is best to
            keep it at `1.0` and just adjust the `intensity` instead.
        - `intensity`: The intensity of the noise. Defaults to `0.001`.
        - `zero_mean`: Whether to zero the mean of the noise. Defaults to
            `True`.
        - `std_one`: Whether to normalize the noise to have a standard
            deviation of one. Defaults to `False`.
        - `max_one`: Whether to normalize the noise to the maximum absolute
            value of one. Defaults to `False`.
    """
    if not zero_mean and std_one:
        raise ValueError("Cannot have `zero_mean=False` and `std_one=True`.")
    if std_one and max_one:
        raise ValueError("Cannot have `std_one=True` and `max_one=True`.")
    self.num_spatial_dims = num_spatial_dims
    self.domain_extent = domain_extent
    self.intensity = intensity
    self.zero_mean = zero_mean
    self.std_one = std_one
    self.max_one = max_one
__call__ ¤
__call__(
    num_points: int, *, key: PRNGKeyArray
) -> Float[Array, "1 ... N"]
Source code in exponax/ic/_diffused_noise.py 
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def __call__(
    self, num_points: int, *, key: PRNGKeyArray
) -> Float[Array, "1 ... N"]:
    noise_shape = (1,) + spatial_shape(self.num_spatial_dims, num_points)
    noise = jr.normal(key, shape=noise_shape)

    diffusion_stepper = Diffusion(
        self.num_spatial_dims,
        self.domain_extent,
        num_points,
        1.0,
        diffusivity=self.intensity,
    )
    ic = diffusion_stepper(noise)

    if self.zero_mean:
        ic = ic - jnp.mean(ic)

    if self.std_one:
        ic = ic / jnp.std(ic)

    if self.max_one:
        ic = ic / jnp.max(jnp.abs(ic))

    return ic