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Aperture Layers

BaseDynamicAperture

Bases: ApertureLayer

An abstract base class that implements the coordinate transformation attribute.

Attributes:

Name Type Description
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class BaseDynamicAperture(ApertureLayer):
    """
    An abstract base class that implements the coordinate transformation attribute.

    Attributes
    ----------
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    transformation: CoordTransform

    def __init__(
        self: ApertureLayer,
        transformation: CoordTransform = None,
        normalise: bool = False,
    ):
        """
        Parameters
        ----------
        transformation: CoordTransform
            The object that applies the coordinate transformations to the aperture.
        normalise : bool = False
            Should the aperture normalise the wavefront after being applied.
        """
        super().__init__(normalise=normalise)
        if transformation is not None:
            if not isinstance(transformation, CoordTransform):
                raise TypeError(
                    "transformation must be a CoordTransform object. "
                    "Use the CoordTransform class to create one."
                )
        self.transformation = transformation

    def __getattr__(self: ApertureLayer, key: str) -> Any:
        """Raises transformation attributes to the ApertureLayer level."""
        if hasattr(self.transformation, key):
            return getattr(self.transformation, key)
        raise AttributeError(f"{key} not in {self.__class__.__name__}")

    def apply(self: ApertureLayer, wavefront: Wavefront) -> Wavefront:
        """
        Applies the layer to the wavefront.

        Parameters
        ----------
        wavefront : Wavefront
            The wavefront to operate on.

        Returns
        -------
        wavefront : Wavefront
            The transformed wavefront.
        """
        # Apply aperture
        wavefront *= self.transmission(
            wavefront.coordinates, wavefront.pixel_scale
        )
        if self.normalise:
            return wavefront.normalise()
        return wavefront

apply(wavefront)

Applies the layer to the wavefront.

Parameters:

Name Type Description Default
wavefront Wavefront

The wavefront to operate on.

 required

Returns:

Name Type Description
wavefront Wavefront

The transformed wavefront.
Source code in src/dLux/layers/apertures.py 
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def apply(self: ApertureLayer, wavefront: Wavefront) -> Wavefront:
    """
    Applies the layer to the wavefront.

    Parameters
    ----------
    wavefront : Wavefront
        The wavefront to operate on.

    Returns
    -------
    wavefront : Wavefront
        The transformed wavefront.
    """
    # Apply aperture
    wavefront *= self.transmission(
        wavefront.coordinates, wavefront.pixel_scale
    )
    if self.normalise:
        return wavefront.normalise()
    return wavefront
CircularAperture

Bases: DynamicAperture

A dynamically generated circular aperture parameterised by its radius. Both jit and grad compatible.

UML

UML

Attributes:

Name Type Description
radius (float, meters)

The radius of the aperture.
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.
occulting bool

Is the aperture occulting or transmissive. False results in a
softening (float, pixels)

The approximate pixel width of the soft boundary applied to the aperture.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class CircularAperture(DynamicAperture):
    """
    A dynamically generated circular aperture parameterised by its radius. Both jit and
    grad compatible.

    ??? abstract "UML"
        ![UML](../../assets/uml/CircularAperture.png)

    Attributes
    ----------
    radius: float, meters
        The radius of the aperture.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool
        Is the aperture occulting or transmissive. False results in a
    softening: float, pixels
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    radius: float

    def __init__(
        self: ApertureLayer,
        radius: float,
        transformation: CoordTransform = None,
        occulting: bool = False,
        softening: float = 1.0,
        normalise: bool = False,
    ):
        """
        Parameters
        ----------
        radius: Array, meters
            The radius of the aperture.

        occulting: bool = False
            Is the aperture occulting or transmissive. False results in a
            transmissive aperture, and True results in an occulting aperture.
        softening: Array, pixels = np.array(1.)
            The approximate pixel width of the soft boundary applied to the aperture.
        normalise : bool = False
            Whether to normalise the wavefront after passing through the aperture.
        """
        super().__init__(
            transformation=transformation,
            occulting=occulting,
            softening=softening,
            normalise=normalise,
        )
        self.radius = float(radius)

    def transmission(
        self: ApertureLayer, coords: Array, pixel_scale: float
    ) -> Array:
        """
        Calculates the transmission of the aperture at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the transmission on.
        pixel_scale : float
            The pixel scale of the coordinates.

        Returns
        -------
        transmission : Array
            The transmission of the aperture at the given coordinates.
        """
        if self.transformation is not None:
            coords = self.transformation.apply(coords)
        clip_val = pixel_scale * self.softness / 2
        return dlu.soft_circle(coords, self.radius, clip_val, self.occulting)

    @property
    def extent(self: ApertureLayer) -> float:
        """
        Returns the maximum extent of the aperture.

        Returns
        -------
        extent : float
            The maximum extent of the aperture.
        """
        return self.radius

    @property
    def nsides(self: ApertureLayer) -> int:
        """
        Returns the number of sides of the aperture.

        Returns
        -------
        nsides : int
            The number of sides of the aperture.
        """
        return 0

__init__(radius, transformation=None, occulting=False, softening=1.0, normalise=False)

Parameters:

Name Type Description Default
radius float

The radius of the aperture.

 required
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.

 False
softening float

The approximate pixel width of the soft boundary applied to the aperture.

 1.0
normalise bool = False

Whether to normalise the wavefront after passing through the aperture.

 False
Source code in src/dLux/layers/apertures.py 
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def __init__(
    self: ApertureLayer,
    radius: float,
    transformation: CoordTransform = None,
    occulting: bool = False,
    softening: float = 1.0,
    normalise: bool = False,
):
    """
    Parameters
    ----------
    radius: Array, meters
        The radius of the aperture.

    occulting: bool = False
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: Array, pixels = np.array(1.)
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool = False
        Whether to normalise the wavefront after passing through the aperture.
    """
    super().__init__(
        transformation=transformation,
        occulting=occulting,
        softening=softening,
        normalise=normalise,
    )
    self.radius = float(radius)

transmission(coords, pixel_scale)

Calculates the transmission of the aperture at the given coordinates.

Parameters:

Name Type Description Default
coords Array

The coordinates to calculate the transmission on.

 required
pixel_scale float

The pixel scale of the coordinates.

 required

Returns:

Name Type Description
transmission Array

The transmission of the aperture at the given coordinates.
Source code in src/dLux/layers/apertures.py 
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def transmission(
    self: ApertureLayer, coords: Array, pixel_scale: float
) -> Array:
    """
    Calculates the transmission of the aperture at the given coordinates.

    Parameters
    ----------
    coords : Array
        The coordinates to calculate the transmission on.
    pixel_scale : float
        The pixel scale of the coordinates.

    Returns
    -------
    transmission : Array
        The transmission of the aperture at the given coordinates.
    """
    if self.transformation is not None:
        coords = self.transformation.apply(coords)
    clip_val = pixel_scale * self.softness / 2
    return dlu.soft_circle(coords, self.radius, clip_val, self.occulting)
RectangularAperture

Bases: DynamicAperture

A dynamically generated rectangular aperture parameterised by it width and height. Both jit and grad compatible.

UML

UML

Attributes:

Name Type Description
height (float, meters)

The length of the aperture in the y-direction.
width (float, meters)

The length of the aperture in the x-direction.
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.
softening (float, pixels)

The approximate pixel width of the soft boundary applied to the aperture.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class RectangularAperture(DynamicAperture):
    """
    A dynamically generated rectangular aperture parameterised by it width and height.
    Both jit and grad compatible.

    ??? abstract "UML"
        ![UML](../../assets/uml/RectangularAperture.png)

    Attributes
    ----------
    height: float, meters
        The length of the aperture in the y-direction.
    width: float, meters
        The length of the aperture in the x-direction.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: float, pixels
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    height: float
    width: float

    def __init__(
        self: ApertureLayer,
        height: float,
        width: float,
        transformation: CoordTransform = None,
        occulting: bool = False,
        softening: float = 1.0,
        normalise: bool = False,
    ):
        """
        Parameters
        ----------
        height: Array, meters
            The length of the aperture in the y-direction.
        width: Array, meters
            The length of the aperture in the x-direction.
        transformation: CoordTransform
            The object that applies the coordinate transformations to the aperture.
        occulting: bool = False
            Is the aperture occulting or transmissive. False results in a
            transmissive aperture, and True results in an occulting aperture.
        softening: float, pixels = 1.0
            The approximate pixel width of the soft boundary applied to the aperture.
        normalise : bool = False
            Whether to normalise the wavefront after passing through the aperture.
        """
        self.height = float(height)
        self.width = float(width)

        super().__init__(
            transformation=transformation,
            occulting=occulting,
            softening=softening,
            normalise=normalise,
        )

    def transmission(
        self: ApertureLayer, coords: Array, pixel_scale: float
    ) -> Array:
        """
        Calculates the transmission of the aperture at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the transmission on.
        pixel_scale : float
            The pixel scale of the coordinates.

        Returns
        -------
        transmission : Array
            The transmission of the aperture at the given coordinates.
        """
        if self.transformation is not None:
            coords = self.transformation.apply(coords)
        clip_val = pixel_scale * self.softness / 2
        return dlu.soft_rectangle(
            coords, self.width, self.height, clip_val, self.occulting
        )

    @property
    def extent(self: ApertureLayer) -> float:
        """
        Returns the maximum extent of the aperture.

        Returns
        -------
        extent : float
            The maximum extent of the aperture.
        """
        return np.hypot(self.height / 2.0, self.width / 2.0)

    @property
    def nsides(self: ApertureLayer) -> int:
        """
        Returns the number of sides of the aperture.

        Returns
        -------
        nsides : int
            The number of sides of the aperture.
        """
        return 4

__init__(height, width, transformation=None, occulting=False, softening=1.0, normalise=False)

Parameters:

Name Type Description Default
height float

The length of the aperture in the y-direction.

 required
width float

The length of the aperture in the x-direction.

 required
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.

 None
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.

 False
softening float

The approximate pixel width of the soft boundary applied to the aperture.

 1.0
normalise bool = False

Whether to normalise the wavefront after passing through the aperture.

 False
Source code in src/dLux/layers/apertures.py 
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def __init__(
    self: ApertureLayer,
    height: float,
    width: float,
    transformation: CoordTransform = None,
    occulting: bool = False,
    softening: float = 1.0,
    normalise: bool = False,
):
    """
    Parameters
    ----------
    height: Array, meters
        The length of the aperture in the y-direction.
    width: Array, meters
        The length of the aperture in the x-direction.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool = False
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: float, pixels = 1.0
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool = False
        Whether to normalise the wavefront after passing through the aperture.
    """
    self.height = float(height)
    self.width = float(width)

    super().__init__(
        transformation=transformation,
        occulting=occulting,
        softening=softening,
        normalise=normalise,
    )

transmission(coords, pixel_scale)

Calculates the transmission of the aperture at the given coordinates.

Parameters:

Name Type Description Default
coords Array

The coordinates to calculate the transmission on.

 required
pixel_scale float

The pixel scale of the coordinates.

 required

Returns:

Name Type Description
transmission Array

The transmission of the aperture at the given coordinates.
Source code in src/dLux/layers/apertures.py 
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def transmission(
    self: ApertureLayer, coords: Array, pixel_scale: float
) -> Array:
    """
    Calculates the transmission of the aperture at the given coordinates.

    Parameters
    ----------
    coords : Array
        The coordinates to calculate the transmission on.
    pixel_scale : float
        The pixel scale of the coordinates.

    Returns
    -------
    transmission : Array
        The transmission of the aperture at the given coordinates.
    """
    if self.transformation is not None:
        coords = self.transformation.apply(coords)
    clip_val = pixel_scale * self.softness / 2
    return dlu.soft_rectangle(
        coords, self.width, self.height, clip_val, self.occulting
    )
RegPolyAperture

Bases: DynamicAperture

Creates a dynamically generated regular polygon aperture parameterised by its number of sides and maximum radius. Both jit and grad compatible.

UML

UML

Attributes:

Name Type Description
nsides int

The number of sides of the aperture.
rmax (float, meters)

The maximum radius to the vertices from its center.
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.
softening (float, pixels)

The approximate pixel width of the soft boundary applied to the aperture.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class RegPolyAperture(DynamicAperture):
    """
    Creates a dynamically generated regular polygon aperture parameterised by its
    number of sides and maximum radius. Both jit and grad compatible.

    ??? abstract "UML"
        ![UML](../../assets/uml/RegPolyAperture.png)

    Attributes
    ----------
    nsides: int
        The number of sides of the aperture.
    rmax: float, meters
        The maximum radius to the vertices from its center.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: float, pixels
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    nsides: int
    rmax: float

    def __init__(
        self: ApertureLayer,
        nsides: int,
        rmax: float,
        transformation: CoordTransform = None,
        occulting: bool = False,
        softening: float = 1.0,
        normalise: bool = False,
    ):
        """
        Parameters
        ----------
        nsides: int
            The number of sides of the aperture.
        rmax: float, meters
            The maximum radius to the vertices from its center.
        transformation: CoordTransform
            The object that applies the coordinate transformations to the aperture.
        occulting: bool = False
            Is the aperture occulting or transmissive. False results in a
            transmissive aperture, and True results in an occulting aperture.
        softening: float, pixels = 1.0
            The approximate pixel width of the soft boundary applied to the aperture.
        normalise : bool = False
            Whether to normalise the wavefront after passing through the aperture.
        """
        self.nsides = int(nsides)
        self.rmax = float(rmax)

        super().__init__(
            transformation=transformation,
            occulting=occulting,
            softening=softening,
            normalise=normalise,
        )

    def transmission(
        self: ApertureLayer, coords: Array, pixel_scale: float
    ) -> Array:
        """
        Calculates the transmission of the aperture at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the transmission on.
        pixel_scale : float
            The pixel scale of the coordinates.

        Returns
        -------
        transmission : Array
            The transmission of the aperture at the given coordinates.
        """
        if self.transformation is not None:
            coords = self.transformation.apply(coords)
        clip_val = pixel_scale * self.softness / 2
        return dlu.soft_reg_polygon(
            coords, self.rmax, self.nsides, clip_val, self.occulting
        )

    @property
    def extent(self: ApertureLayer) -> float:
        """
        Returns the maximum extent of the aperture.

        Returns
        -------
        extent : float
            The maximum extent of the aperture.
        """
        return self.rmax

__init__(nsides, rmax, transformation=None, occulting=False, softening=1.0, normalise=False)

Parameters:

Name Type Description Default
nsides int

The number of sides of the aperture.

 required
rmax float

The maximum radius to the vertices from its center.

 required
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.

 None
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.

 False
softening float

The approximate pixel width of the soft boundary applied to the aperture.

 1.0
normalise bool = False

Whether to normalise the wavefront after passing through the aperture.

 False
Source code in src/dLux/layers/apertures.py 
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def __init__(
    self: ApertureLayer,
    nsides: int,
    rmax: float,
    transformation: CoordTransform = None,
    occulting: bool = False,
    softening: float = 1.0,
    normalise: bool = False,
):
    """
    Parameters
    ----------
    nsides: int
        The number of sides of the aperture.
    rmax: float, meters
        The maximum radius to the vertices from its center.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool = False
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: float, pixels = 1.0
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool = False
        Whether to normalise the wavefront after passing through the aperture.
    """
    self.nsides = int(nsides)
    self.rmax = float(rmax)

    super().__init__(
        transformation=transformation,
        occulting=occulting,
        softening=softening,
        normalise=normalise,
    )

transmission(coords, pixel_scale)

Calculates the transmission of the aperture at the given coordinates.

Parameters:

Name Type Description Default
coords Array

The coordinates to calculate the transmission on.

 required
pixel_scale float

The pixel scale of the coordinates.

 required

Returns:

Name Type Description
transmission Array

The transmission of the aperture at the given coordinates.
Source code in src/dLux/layers/apertures.py 
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def transmission(
    self: ApertureLayer, coords: Array, pixel_scale: float
) -> Array:
    """
    Calculates the transmission of the aperture at the given coordinates.

    Parameters
    ----------
    coords : Array
        The coordinates to calculate the transmission on.
    pixel_scale : float
        The pixel scale of the coordinates.

    Returns
    -------
    transmission : Array
        The transmission of the aperture at the given coordinates.
    """
    if self.transformation is not None:
        coords = self.transformation.apply(coords)
    clip_val = pixel_scale * self.softness / 2
    return dlu.soft_reg_polygon(
        coords, self.rmax, self.nsides, clip_val, self.occulting
    )
Spider

Bases: DynamicAperture

Creates a dynamically generated spider aperture parameterised by its arm width and number of arms. Both jit and grad compatible.

UML

UML

Attributes:

Name Type Description
width (float, meters)

The width of the spider.
angles (Array, degrees)

The angle of each arm of the spider.
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.
softening (float, pixels)

The approximate pixel width of the soft boundary applied to the aperture.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class Spider(DynamicAperture):
    """
    Creates a dynamically generated spider aperture parameterised by its arm width and
    number of arms. Both jit and grad compatible.

    ??? abstract "UML"
        ![UML](../../assets/uml/Spider.png)

    Attributes
    ----------
    width: float, meters
        The width of the spider.
    angles: Array, degrees
        The angle of each arm of the spider.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: float, pixels
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    width: float
    angles: Array

    def __init__(
        self: ApertureLayer,
        width: float,
        angles: Array,
        transformation: CoordTransform = None,
        occulting: bool = True,
        softening: float = 1.0,
        normalise: bool = False,
    ):
        """
        Parameters
        ----------
        width: float, meters
            The width of the spider.
        angles: Array, degrees
            The angle of each arm of the spider.
        transformation: CoordTransform
            The object that applies the coordinate transformations to the aperture.
        occulting: bool = True
            Is the aperture occulting or transmissive. False results in a
            transmissive aperture, and True results in an occulting aperture.
        softening: float, pixels = 1.0
            The approximate pixel width of the soft boundary applied to the aperture.
        normalise : bool = False
            Whether to normalise the wavefront after passing through the aperture.
        """
        super().__init__(
            transformation=transformation,
            occulting=occulting,
            softening=softening,
            normalise=normalise,
        )

        self.width = float(width)
        self.angles = np.asarray(angles, dtype=float)

    def transmission(
        self: ApertureLayer, coords: Array, pixel_scale: float
    ) -> Array:
        """
        Calculates the transmission of the aperture at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the transmission on.
        pixel_scale : float
            The pixel scale of the coordinates.

        Returns
        -------
        transmission : Array
            The transmission of the aperture at the given coordinates.
        """
        if self.transformation is not None:
            coords = self.transformation.apply(coords)
        clip_val = pixel_scale * self.softness / 2
        return dlu.soft_spider(
            coords, self.width, self.angles, clip_val, self.occulting
        )

    @property
    def extent(self: ApertureLayer) -> float:
        """
        Returns the maximum extent of the aperture.

        Returns
        -------
        extent : float
            The maximum extent of the aperture.
        """
        raise TypeError("Spiders do not have an extent.")

    @property
    def nsides(self: ApertureLayer) -> int:
        """
        Returns the number of sides of the aperture.

        Returns
        -------
        nsides : int
            The number of sides of the aperture.
        """
        raise TypeError("Spiders do not have a number of sides.")

__init__(width, angles, transformation=None, occulting=True, softening=1.0, normalise=False)

Parameters:

Name Type Description Default
width float

The width of the spider.

 required
angles Array

The angle of each arm of the spider.

 required
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.

 None
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.

 True
softening float

The approximate pixel width of the soft boundary applied to the aperture.

 1.0
normalise bool = False

Whether to normalise the wavefront after passing through the aperture.

 False
Source code in src/dLux/layers/apertures.py 
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def __init__(
    self: ApertureLayer,
    width: float,
    angles: Array,
    transformation: CoordTransform = None,
    occulting: bool = True,
    softening: float = 1.0,
    normalise: bool = False,
):
    """
    Parameters
    ----------
    width: float, meters
        The width of the spider.
    angles: Array, degrees
        The angle of each arm of the spider.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool = True
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: float, pixels = 1.0
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool = False
        Whether to normalise the wavefront after passing through the aperture.
    """
    super().__init__(
        transformation=transformation,
        occulting=occulting,
        softening=softening,
        normalise=normalise,
    )

    self.width = float(width)
    self.angles = np.asarray(angles, dtype=float)

transmission(coords, pixel_scale)

Calculates the transmission of the aperture at the given coordinates.

Parameters:

Name Type Description Default
coords Array

The coordinates to calculate the transmission on.

 required
pixel_scale float

The pixel scale of the coordinates.

 required

Returns:

Name Type Description
transmission Array

The transmission of the aperture at the given coordinates.
Source code in src/dLux/layers/apertures.py 
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def transmission(
    self: ApertureLayer, coords: Array, pixel_scale: float
) -> Array:
    """
    Calculates the transmission of the aperture at the given coordinates.

    Parameters
    ----------
    coords : Array
        The coordinates to calculate the transmission on.
    pixel_scale : float
        The pixel scale of the coordinates.

    Returns
    -------
    transmission : Array
        The transmission of the aperture at the given coordinates.
    """
    if self.transformation is not None:
        coords = self.transformation.apply(coords)
    clip_val = pixel_scale * self.softness / 2
    return dlu.soft_spider(
        coords, self.width, self.angles, clip_val, self.occulting
    )
SquareAperture

Bases: DynamicAperture

A dynamically generated square aperture parameterised by it side length. Both jit and grad compatible.

UML

UML

Attributes:

Name Type Description
width (float, meters)

The side length of the aperture.
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.
softening (float, pixels)

The approximate pixel width of the soft boundary applied to the aperture.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class SquareAperture(DynamicAperture):
    """
    A dynamically generated square aperture parameterised by it side length.
    Both jit and grad compatible.

    ??? abstract "UML"
        ![UML](../../assets/uml/SquareAperture.png)

    Attributes
    ----------
    width: float, meters
        The side length of the aperture.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: float, pixels
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    width: float

    def __init__(
        self: ApertureLayer,
        width: float,
        transformation: CoordTransform = None,
        occulting: bool = False,
        softening: float = 1.0,
        normalise: bool = False,
    ):
        """
        Parameters
        ----------
        width: Array, meters
            The side length of the aperture.
        transformation: CoordTransform
            The object that applies the coordinate transformations to the aperture.
        occulting: bool = False
            Is the aperture occulting or transmissive. False results in a
            transmissive aperture, and True results in an occulting aperture.
        softening: float, pixels = 1.0
            The approximate pixel width of the soft boundary applied to the aperture.
        normalise : bool = False
            Whether to normalise the wavefront after passing through the aperture.
        """
        super().__init__(
            transformation=transformation,
            occulting=occulting,
            softening=softening,
            normalise=normalise,
        )

        self.width = float(width)

    def transmission(
        self: ApertureLayer, coords: Array, pixel_scale: float
    ) -> Array:
        """
        Calculates the transmission of the aperture at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the transmission on.
        pixel_scale : float
            The pixel scale of the coordinates.

        Returns
        -------
        transmission : Array
            The transmission of the aperture at the given coordinates.
        """
        if self.transformation is not None:
            coords = self.transformation.apply(coords)
        clip_val = pixel_scale * self.softness / 2
        return dlu.soft_square(coords, self.width, clip_val, self.occulting)

    @property
    def extent(self: ApertureLayer) -> float:
        """
        Returns the maximum extent of the aperture.

        Returns
        -------
        extent : float
            The maximum extent of the aperture.
        """
        return np.sqrt(2) * self.width

    @property
    def nsides(self: ApertureLayer) -> int:
        """
        Returns the number of sides of the aperture.

        Returns
        -------
        nsides : int
            The number of sides of the aperture.
        """
        return 4

__init__(width, transformation=None, occulting=False, softening=1.0, normalise=False)

Parameters:

Name Type Description Default
width float

The side length of the aperture.

 required
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.

 None
occulting bool

Is the aperture occulting or transmissive. False results in a transmissive aperture, and True results in an occulting aperture.

 False
softening float

The approximate pixel width of the soft boundary applied to the aperture.

 1.0
normalise bool = False

Whether to normalise the wavefront after passing through the aperture.

 False
Source code in src/dLux/layers/apertures.py 
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def __init__(
    self: ApertureLayer,
    width: float,
    transformation: CoordTransform = None,
    occulting: bool = False,
    softening: float = 1.0,
    normalise: bool = False,
):
    """
    Parameters
    ----------
    width: Array, meters
        The side length of the aperture.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    occulting: bool = False
        Is the aperture occulting or transmissive. False results in a
        transmissive aperture, and True results in an occulting aperture.
    softening: float, pixels = 1.0
        The approximate pixel width of the soft boundary applied to the aperture.
    normalise : bool = False
        Whether to normalise the wavefront after passing through the aperture.
    """
    super().__init__(
        transformation=transformation,
        occulting=occulting,
        softening=softening,
        normalise=normalise,
    )

    self.width = float(width)

transmission(coords, pixel_scale)

Calculates the transmission of the aperture at the given coordinates.

Parameters:

Name Type Description Default
coords Array

The coordinates to calculate the transmission on.

 required
pixel_scale float

The pixel scale of the coordinates.

 required

Returns:

Name Type Description
transmission Array

The transmission of the aperture at the given coordinates.
Source code in src/dLux/layers/apertures.py 
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def transmission(
    self: ApertureLayer, coords: Array, pixel_scale: float
) -> Array:
    """
    Calculates the transmission of the aperture at the given coordinates.

    Parameters
    ----------
    coords : Array
        The coordinates to calculate the transmission on.
    pixel_scale : float
        The pixel scale of the coordinates.

    Returns
    -------
    transmission : Array
        The transmission of the aperture at the given coordinates.
    """
    if self.transformation is not None:
        coords = self.transformation.apply(coords)
    clip_val = pixel_scale * self.softness / 2
    return dlu.soft_square(coords, self.width, clip_val, self.occulting)
AberratedAperture

Bases: BasisLayer, ApertureLayer

Creates a dynamically generated Aperture with aberrations. Both jit and grad compatible.

UML

UML

Attributes:

Name Type Description
aperture ApertureLayer

The aperture on which the aberration basis is defined.
basis list[Zernike]

A list of basis functions that generate the basis vectors.
coefficients Array

The amplitude of each basis vector of the aberrations.
as_phase bool

Whether to apply the basis as a phase phase or OPD. If True the basis is applied as a phase, else it is applied as an OPD.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class AberratedAperture(BasisLayer, ApertureLayer):
    """
    Creates a dynamically generated Aperture with aberrations. Both jit and grad
    compatible.

    ??? abstract "UML"
        ![UML](../../assets/uml/AberratedAperture.png)

    Attributes
    ----------
    aperture: ApertureLayer
        The aperture on which the aberration basis is defined.
    basis: list[Zernike]
        A list of basis functions that generate the basis vectors.
    coefficients: Array
        The amplitude of each basis vector of the aberrations.
    as_phase : bool
        Whether to apply the basis as a phase phase or OPD. If True the basis is
        applied as a phase, else it is applied as an OPD.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    aperture: ApertureLayer

    def __init__(
        self: ApertureLayer,
        aperture: ApertureLayer,
        noll_inds: Array,
        coefficients: Array = None,
        as_phase: bool = False,
    ):
        """
        Parameters
        ----------
        aperture: ApertureLayer
            The aperture on which the aberration basis is defined.
        noll_inds: Array
            The noll indices of the basis functions to use.
        coefficients: Array = None
            The amplitude of each basis vector of the aberrations.
        as_phase : bool = False
            Whether to apply the basis as a phase phase or OPD. If True the basis is
            applied as a phase, else it is applied as an OPD.
        """
        # Ensure aperture is dynamic
        if not isinstance(aperture, DynamicAperture):
            raise TypeError(
                "AberratedApertures can not contain Static, "
                "Compound or Multi Apertures. AberratedApertures can be "
                "placed in Compound or Multi Apertures, which can then be "
                "promoted to Static."
            )

        super().__init__(normalise=aperture.normalise, as_phase=as_phase)

        # Ensure transmissive
        if aperture.occulting:
            raise TypeError("AberratedApertures can not be occulting.")

        if isinstance(aperture, Spider):
            raise TypeError("AberratedApertures can not be spiders.")

        # Set Aperture
        self.aperture = aperture
        self.basis = ZernikeBasis(noll_inds)

        if coefficients is None:
            coefficients = np.zeros(len(noll_inds))
        self.coefficients = np.asarray(coefficients, dtype=float)

    def calculate(self: ApertureLayer):
        """
        Required abstract method. Raises NotImplementedError as it is invalid here.
        """
        raise NotImplementedError(
            "Aberrated Apertures can not use the .calculate() method because "
            "they need coords to be generated on. please use "
            ".eval_basis(coords) method instead."
        )

    def transmission(
        self: ApertureLayer, coords: Array, pixel_scale: float
    ) -> Array:
        """
        Calculates the transmission of the aperture at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the transmission on.
        pixel_scale : float
            The pixel scale of the coordinates.

        Returns
        -------
        transmission : Array
            The transmission of the aperture at the given coordinates.
        """
        return self.aperture.transmission(coords, pixel_scale)

    def calc_basis(self: ApertureLayer, coords: Array) -> Array:
        """
        Calculates the basis vectors at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the basis vectors on.

        Returns
        -------
        basis : Array
            The basis vectors at the given coordinates.
        """
        if self.aperture.transformation is not None:
            coords = self.aperture.transformation.apply(coords)
        coords /= self.aperture.extent
        return self.basis.calculate_basis(coords, self.aperture.nsides)

    def eval_basis(self: ApertureLayer, coords: Array) -> Array:
        """
        Evaluates the basis vectors at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to evaluate the basis vectors on.

        Returns
        -------
        aberrations : Array
            The aberrations at the given coordinates.
        """
        basis = self.calc_basis(coords)
        return dlu.eval_basis(basis, self.coefficients)

    def apply(self: ApertureLayer, wavefront: Wavefront) -> Wavefront:
        """
        Applies the layer to the wavefront.

        Parameters
        ----------
        wavefront : Wavefront
            The wavefront to operate on.

        Returns
        -------
        wavefront : Wavefront
            The transformed wavefront.
        """
        # Transmission
        wavefront *= self.transmission(
            wavefront.coordinates, wavefront.pixel_scale
        )
        if self.normalise:
            wavefront = wavefront.normalise()

        # Transform coordinate
        if self.aperture.transformation is not None:
            coords = self.aperture.transformation.apply(wavefront.coordinates)
        else:
            coords = wavefront.coordinates

        # Aberrations
        aberrations = self.eval_basis(coords)
        if self.as_phase:
            wavefront = wavefront.add_phase(aberrations)
        else:
            wavefront += aberrations
        return wavefront

__init__(aperture, noll_inds, coefficients=None, as_phase=False)

Parameters:

Name Type Description Default
aperture ApertureLayer

The aperture on which the aberration basis is defined.

 required
noll_inds Array

The noll indices of the basis functions to use.

 required
coefficients Array

The amplitude of each basis vector of the aberrations.

 None
as_phase bool = False

Whether to apply the basis as a phase phase or OPD. If True the basis is applied as a phase, else it is applied as an OPD.

 False
Source code in src/dLux/layers/apertures.py 
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def __init__(
    self: ApertureLayer,
    aperture: ApertureLayer,
    noll_inds: Array,
    coefficients: Array = None,
    as_phase: bool = False,
):
    """
    Parameters
    ----------
    aperture: ApertureLayer
        The aperture on which the aberration basis is defined.
    noll_inds: Array
        The noll indices of the basis functions to use.
    coefficients: Array = None
        The amplitude of each basis vector of the aberrations.
    as_phase : bool = False
        Whether to apply the basis as a phase phase or OPD. If True the basis is
        applied as a phase, else it is applied as an OPD.
    """
    # Ensure aperture is dynamic
    if not isinstance(aperture, DynamicAperture):
        raise TypeError(
            "AberratedApertures can not contain Static, "
            "Compound or Multi Apertures. AberratedApertures can be "
            "placed in Compound or Multi Apertures, which can then be "
            "promoted to Static."
        )

    super().__init__(normalise=aperture.normalise, as_phase=as_phase)

    # Ensure transmissive
    if aperture.occulting:
        raise TypeError("AberratedApertures can not be occulting.")

    if isinstance(aperture, Spider):
        raise TypeError("AberratedApertures can not be spiders.")

    # Set Aperture
    self.aperture = aperture
    self.basis = ZernikeBasis(noll_inds)

    if coefficients is None:
        coefficients = np.zeros(len(noll_inds))
    self.coefficients = np.asarray(coefficients, dtype=float)

transmission(coords, pixel_scale)

Calculates the transmission of the aperture at the given coordinates.

Parameters:

Name Type Description Default
coords Array

The coordinates to calculate the transmission on.

 required
pixel_scale float

The pixel scale of the coordinates.

 required

Returns:

Name Type Description
transmission Array

The transmission of the aperture at the given coordinates.
Source code in src/dLux/layers/apertures.py 
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def transmission(
    self: ApertureLayer, coords: Array, pixel_scale: float
) -> Array:
    """
    Calculates the transmission of the aperture at the given coordinates.

    Parameters
    ----------
    coords : Array
        The coordinates to calculate the transmission on.
    pixel_scale : float
        The pixel scale of the coordinates.

    Returns
    -------
    transmission : Array
        The transmission of the aperture at the given coordinates.
    """
    return self.aperture.transmission(coords, pixel_scale)
CompoundAperture

Bases: CompositeAperture

Dynamically generates an Apertures from a series of overlapping sub-apertures. Both jit and grad compatible.

This class combines the aperture via a multiplication of the sub-apertures. An example would be a HST-like aperture with an obscuring secondary mirror and spiders.

This class can only contain a single AberratedAperture.

If you want to combine apertures via an addition of the sub-apertures such as an aperture mask, use the MultiAperture class.

Note that this class can not contain a MultiAperture, but MultiApertures can contain CompoundApertures.

UML

UML

Attributes:

Name Type Description
apertures dict

The sub-apertures that make up the full aperture.
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class CompoundAperture(CompositeAperture):
    """
    Dynamically generates an Apertures from a series of overlapping sub-apertures. Both
    jit and grad compatible.

    This class combines the aperture via a _multiplication_ of the sub-apertures. An
    example would be a HST-like aperture with an obscuring secondary mirror and spiders.

    This class can only contain a _single_ AberratedAperture.

    If you want to combine apertures via an _addition_ of the sub-apertures such as an
    aperture mask, use the MultiAperture class.

    Note that this class can not contain a MultiAperture, but MultiApertures can
    contain CompoundApertures.

    ??? abstract "UML"
        ![UML](../../assets/uml/CompoundAperture.png)

    Attributes
    ----------
    apertures: dict
        The sub-apertures that make up the full aperture.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    def __init__(
        self: ApertureLayer,
        apertures: list,
        transformation: CoordTransform = None,
        normalise: bool = False,
    ):
        """
        Parameters
        ----------
        apertures: list[ApertureLayer]
            The sub-apertures that make up the full aperture.
        transformation: CoordTransform
            The object that applies the coordinate transformations to the aperture.
        normalise : bool = False
            Whether to normalise the wavefront after passing through the aperture.
        """
        super().__init__(
            apertures=apertures,
            transformation=transformation,
            normalise=normalise,
        )

        # Check for more than one aberrated aperture
        naberrated = 0
        for aperture in self.apertures.values():
            if isinstance(aperture, AberratedAperture):
                naberrated += 1
            if naberrated > 1:
                raise TypeError(
                    "CompoundAperture can only have a single "
                    "AberratedAperture."
                )

    def transmission(
        self: ApertureLayer, coords: Array, pixel_scale: float
    ) -> Array:
        """
        Calculates the transmission of the aperture at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the transmission on.
        pixel_scale : float
            The pixel scale of the coordinates.

        Returns
        -------
        transmission : Array
            The transmission of the aperture at the given coordinates.
        """
        if self.transformation is not None:
            coords = self.transformation.apply(coords)
        return self.transmissions(coords, pixel_scale).prod(0)

__init__(apertures, transformation=None, normalise=False)

Parameters:

Name Type Description Default
apertures list

The sub-apertures that make up the full aperture.

 required
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.

 None
normalise bool = False

Whether to normalise the wavefront after passing through the aperture.

 False
Source code in src/dLux/layers/apertures.py 
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def __init__(
    self: ApertureLayer,
    apertures: list,
    transformation: CoordTransform = None,
    normalise: bool = False,
):
    """
    Parameters
    ----------
    apertures: list[ApertureLayer]
        The sub-apertures that make up the full aperture.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    normalise : bool = False
        Whether to normalise the wavefront after passing through the aperture.
    """
    super().__init__(
        apertures=apertures,
        transformation=transformation,
        normalise=normalise,
    )

    # Check for more than one aberrated aperture
    naberrated = 0
    for aperture in self.apertures.values():
        if isinstance(aperture, AberratedAperture):
            naberrated += 1
        if naberrated > 1:
            raise TypeError(
                "CompoundAperture can only have a single "
                "AberratedAperture."
            )

transmission(coords, pixel_scale)

Calculates the transmission of the aperture at the given coordinates.

Parameters:

Name Type Description Default
coords Array

The coordinates to calculate the transmission on.

 required
pixel_scale float

The pixel scale of the coordinates.

 required

Returns:

Name Type Description
transmission Array

The transmission of the aperture at the given coordinates.
Source code in src/dLux/layers/apertures.py 
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def transmission(
    self: ApertureLayer, coords: Array, pixel_scale: float
) -> Array:
    """
    Calculates the transmission of the aperture at the given coordinates.

    Parameters
    ----------
    coords : Array
        The coordinates to calculate the transmission on.
    pixel_scale : float
        The pixel scale of the coordinates.

    Returns
    -------
    transmission : Array
        The transmission of the aperture at the given coordinates.
    """
    if self.transformation is not None:
        coords = self.transformation.apply(coords)
    return self.transmissions(coords, pixel_scale).prod(0)
MultiAperture

Bases: CompositeAperture

Dynamically generates an Apertures from a series of separated sub-apertures. Both jit and grad compatible.

This class combines the aperture via an addition of the sub-apertures. An example would be a aperture mask with multiple holes.

This class can only contain a multiple AberratedAperture, or CompoundApertures.

If you want to combine apertures via a multiplication of the sub-apertures such as HST-like aperture, use the CompoundAperture class.

UML

UML

Attributes:

Name Type Description
apertures dict

The sub-apertures that make up the full aperture.
transformation CoordTransform

The object that applies the coordinate transformations to the aperture.
normalise bool

Whether to normalise the wavefront after passing through the aperture.
Source code in src/dLux/layers/apertures.py 
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class MultiAperture(CompositeAperture):
    """
    Dynamically generates an Apertures from a series of separated sub-apertures. Both
    jit and grad compatible.

    This class combines the aperture via an _addition_ of the sub-apertures. An example
    would be a aperture mask with multiple holes.

    This class can only contain a _multiple_ AberratedAperture, or CompoundApertures.

    If you want to combine apertures via a _multiplication_ of the sub-apertures such
    as HST-like aperture, use the CompoundAperture class.

    ??? abstract "UML"
        ![UML](../../assets/uml/MultiAperture.png)

    Attributes
    ----------
    apertures: dict
        The sub-apertures that make up the full aperture.
    transformation: CoordTransform
        The object that applies the coordinate transformations to the aperture.
    normalise : bool
        Whether to normalise the wavefront after passing through the aperture.
    """

    def eval_basis(self: ApertureLayer, coords: Array) -> Array:
        """
        Evaluates the basis vectors at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to evaluate the basis vectors on.

        Returns
        -------
        aberrations : Array
            The aberrations at the given coordinates.
        """
        return super().eval_basis(coords).sum(0)

    def transmission(
        self: ApertureLayer, coords: Array, pixel_scale: float
    ) -> Array:
        """
        Calculates the transmission of the aperture at the given coordinates.

        Parameters
        ----------
        coords : Array
            The coordinates to calculate the transmission on.
        pixel_scale : float
            The pixel scale of the coordinates.

        Returns
        -------
        transmission : Array
            The transmission of the aperture at the given coordinates.
        """
        if self.transformation is not None:
            coords = self.transformation.apply(coords)
        return self.transmissions(coords, pixel_scale).sum(0)

transmission(coords, pixel_scale)

Calculates the transmission of the aperture at the given coordinates.

Parameters:

Name Type Description Default
coords Array

The coordinates to calculate the transmission on.

 required
pixel_scale float

The pixel scale of the coordinates.

 required

Returns:

Name Type Description
transmission Array

The transmission of the aperture at the given coordinates.
Source code in src/dLux/layers/apertures.py 
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def transmission(
    self: ApertureLayer, coords: Array, pixel_scale: float
) -> Array:
    """
    Calculates the transmission of the aperture at the given coordinates.

    Parameters
    ----------
    coords : Array
        The coordinates to calculate the transmission on.
    pixel_scale : float
        The pixel scale of the coordinates.

    Returns
    -------
    transmission : Array
        The transmission of the aperture at the given coordinates.
    """
    if self.transformation is not None:
        coords = self.transformation.apply(coords)
    return self.transmissions(coords, pixel_scale).sum(0)