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Sources

PointSource

Bases: Source

A simple point source with a spectrum, position and flux.

UML

UML

Attributes:

Name Type Description
position (Array, radians)

The (x, y) on-sky position of this object.
flux (float, photons)

The flux of the object.
spectrum Spectrum

The spectrum of this object, represented by a Spectrum object.
Source code in src/dLux/sources.py 
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class PointSource(Source):
    """
    A simple point source with a spectrum, position and flux.

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

    Attributes
    ----------
    position : Array, radians
        The (x, y) on-sky position of this object.
    flux : float, photons
        The flux of the object.
    spectrum : Spectrum
        The spectrum of this object, represented by a Spectrum object.
    """

    position: Array
    flux: float

    def __init__(
        self: Source,
        wavelengths: Array = None,
        position: Array = np.zeros(2),
        flux: float = 1.0,
        weights: Array = None,
        spectrum: Spectrum() = None,
    ):
        """
        Parameters
        ----------
        wavelengths : Array, metres = None
            The array of wavelengths at which the spectrum is defined. This input is
            ignored if a Spectrum object is provided.
        position : Array, radians = np.zeros(2)
            The (x, y) on-sky position of this object.
        flux : float, photons = 1.
            The flux of the object.
        spectrum : Spectrum = None
            The spectrum of this object, represented by a Spectrum object.
        """
        # Position and Flux
        self.position = np.asarray(position, dtype=float)
        self.flux = float(flux)

        if self.position.shape != (2,):
            raise ValueError("position must be a 1d array of shape (2,).")

        super().__init__(
            wavelengths=wavelengths, weights=weights, spectrum=spectrum
        )

    def model(
        self: Source,
        optics: Optics,
        return_wf: bool = False,
        return_psf: bool = False,
    ) -> Array:
        """
        Models the source object through the provided optics.

        Parameters
        ----------
        optics : Optics
            The optics through which to model the source object.
        return_wf : bool = False
            Should the Wavefront object be returned instead of the psf Array?
        return_psf : bool = False
            Should the PSF object be returned instead of the psf Array?

        Returns
        -------
        object : Array, Wavefront, PSF
            if `return_wf` is False and `return_psf` is False, returns the psf Array.
            if `return_wf` is True and `return_psf` is False, returns the Wavefront
                object.
            if `return_wf` is False and `return_psf` is True, returns the PSF object.
        """
        self = self.normalise()
        weights = self.weights * self.flux
        return optics.propagate(
            self.wavelengths, self.position, weights, return_wf, return_psf
        )

__init__(wavelengths=None, position=np.zeros(2), flux=1.0, weights=None, spectrum=None)

Parameters:

Name Type Description Default
wavelengths Array, metres = None

The array of wavelengths at which the spectrum is defined. This input is ignored if a Spectrum object is provided.

 None
position Array, radians = np.zeros(2)

The (x, y) on-sky position of this object.

 zeros(2)
flux float, photons = 1.

The flux of the object.

 1.0
spectrum Spectrum = None

The spectrum of this object, represented by a Spectrum object.

 None
Source code in src/dLux/sources.py 
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def __init__(
    self: Source,
    wavelengths: Array = None,
    position: Array = np.zeros(2),
    flux: float = 1.0,
    weights: Array = None,
    spectrum: Spectrum() = None,
):
    """
    Parameters
    ----------
    wavelengths : Array, metres = None
        The array of wavelengths at which the spectrum is defined. This input is
        ignored if a Spectrum object is provided.
    position : Array, radians = np.zeros(2)
        The (x, y) on-sky position of this object.
    flux : float, photons = 1.
        The flux of the object.
    spectrum : Spectrum = None
        The spectrum of this object, represented by a Spectrum object.
    """
    # Position and Flux
    self.position = np.asarray(position, dtype=float)
    self.flux = float(flux)

    if self.position.shape != (2,):
        raise ValueError("position must be a 1d array of shape (2,).")

    super().__init__(
        wavelengths=wavelengths, weights=weights, spectrum=spectrum
    )

model(optics, return_wf=False, return_psf=False)

Models the source object through the provided optics.

Parameters:

Name Type Description Default
optics Optics

The optics through which to model the source object.

 required
return_wf bool = False

Should the Wavefront object be returned instead of the psf Array?

 False
return_psf bool = False

Should the PSF object be returned instead of the psf Array?

 False

Returns:

Name Type Description
object (Array, Wavefront, PSF)

if return_wf is False and return_psf is False, returns the psf Array. if return_wf is True and return_psf is False, returns the Wavefront object. if return_wf is False and return_psf is True, returns the PSF object.
Source code in src/dLux/sources.py 
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def model(
    self: Source,
    optics: Optics,
    return_wf: bool = False,
    return_psf: bool = False,
) -> Array:
    """
    Models the source object through the provided optics.

    Parameters
    ----------
    optics : Optics
        The optics through which to model the source object.
    return_wf : bool = False
        Should the Wavefront object be returned instead of the psf Array?
    return_psf : bool = False
        Should the PSF object be returned instead of the psf Array?

    Returns
    -------
    object : Array, Wavefront, PSF
        if `return_wf` is False and `return_psf` is False, returns the psf Array.
        if `return_wf` is True and `return_psf` is False, returns the Wavefront
            object.
        if `return_wf` is False and `return_psf` is True, returns the PSF object.
    """
    self = self.normalise()
    weights = self.weights * self.flux
    return optics.propagate(
        self.wavelengths, self.position, weights, return_wf, return_psf
    )

PointSources

Bases: Source

A set of point sources with the same spectrum, but different positions and fluxes.

UML

UML

Attributes:

Name Type Description
position (Array, radians)

The ((x0, y0), (x1, y1), ...) on-sky positions of these sources.
flux (Array, photons)

The fluxes of the sources.
spectrum Spectrum

The spectrum of this object, represented by a Spectrum object.
Source code in src/dLux/sources.py 
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class PointSources(Source):
    """
    A set of point sources with the same spectrum, but different positions and fluxes.

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

    Attributes
    ----------
    position : Array, radians
        The ((x0, y0), (x1, y1), ...) on-sky positions of these sources.
    flux : Array, photons
        The fluxes of the sources.
    spectrum : Spectrum
        The spectrum of this object, represented by a Spectrum object.
    """

    position: Array
    flux: Array

    def __init__(
        self: Source,
        wavelengths: Array = None,
        position: Array = np.zeros((1, 2)),
        flux: Array = None,
        weights: Array = None,
        spectrum: Spectrum() = None,
    ):
        """
        Parameters
        ----------
        wavelengths : Array, metres
            The array of wavelengths at which the spectrum is defined.
        position : Array, radians = np.zeros((1, 2))
            The (x, y) on-sky position of this object.
        flux : Array, photons = None
            The flux of the object.
        weights : Array = None
            The spectral weights of the object.
        spectrum : Spectrum = None
            The spectrum of this object, represented by a Spectrum object.
        """
        super().__init__(
            spectrum=spectrum, wavelengths=wavelengths, weights=weights
        )

        # More complex parameter checks here because of extra dims
        self.position = np.asarray(position, dtype=float)
        if self.position.ndim != 2:
            raise ValueError("position must be a 2d array.")

        if flux is None:
            self.flux = np.ones(len(self.position))
        else:
            self.flux = np.asarray(flux, dtype=float)

            if self.flux.ndim != 1:
                raise ValueError("flux must be a 1d array.")

            if len(self.flux) != len(self.position):
                raise ValueError(
                    "Length of flux must be equal to length of " "positions."
                )

    def model(
        self: Source,
        optics: Optics,
        return_wf: bool = False,
        return_psf: bool = False,
    ) -> Array:
        """
        Models the source object through the provided optics.

        Parameters
        ----------
        optics : Optics
            The optics through which to model the source object.
        return_wf : bool = False
            Should the Wavefront object be returned instead of the psf Array?
        return_psf : bool = False
            Should the PSF object be returned instead of the psf Array?

        Returns
        -------
        object : Array, Wavefront, PSF
            if `return_wf` is False and `return_psf` is False, returns the psf Array.
            if `return_wf` is True and `return_psf` is False, returns the Wavefront
                object.
            if `return_wf` is False and `return_psf` is True, returns the PSF object.
        """
        if return_wf and return_psf:
            raise ValueError(
                "return_wf and return_psf cannot both be True. "
                "Please choose one."
            )
        self = self.normalise()
        weights = self.weights[None, :] * self.flux[:, None]
        prop_fn = lambda position, weight: optics.propagate(
            self.wavelengths, position, weight, return_wf=True
        )
        wfs = filter_vmap(prop_fn)(self.position, weights)

        if return_wf:
            return wfs
        if return_psf:
            return PSF(wfs.psf.sum((0, 1)), wfs.pixel_scale.mean())
        else:
            return wfs.psf.sum((0, 1))

__init__(wavelengths=None, position=np.zeros((1, 2)), flux=None, weights=None, spectrum=None)

Parameters:

Name Type Description Default
wavelengths (Array, metres)

The array of wavelengths at which the spectrum is defined.

 None
position Array, radians = np.zeros((1, 2))

The (x, y) on-sky position of this object.

 zeros((1, 2))
flux Array, photons = None

The flux of the object.

 None
weights Array = None

The spectral weights of the object.

 None
spectrum Spectrum = None

The spectrum of this object, represented by a Spectrum object.

 None
Source code in src/dLux/sources.py 
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def __init__(
    self: Source,
    wavelengths: Array = None,
    position: Array = np.zeros((1, 2)),
    flux: Array = None,
    weights: Array = None,
    spectrum: Spectrum() = None,
):
    """
    Parameters
    ----------
    wavelengths : Array, metres
        The array of wavelengths at which the spectrum is defined.
    position : Array, radians = np.zeros((1, 2))
        The (x, y) on-sky position of this object.
    flux : Array, photons = None
        The flux of the object.
    weights : Array = None
        The spectral weights of the object.
    spectrum : Spectrum = None
        The spectrum of this object, represented by a Spectrum object.
    """
    super().__init__(
        spectrum=spectrum, wavelengths=wavelengths, weights=weights
    )

    # More complex parameter checks here because of extra dims
    self.position = np.asarray(position, dtype=float)
    if self.position.ndim != 2:
        raise ValueError("position must be a 2d array.")

    if flux is None:
        self.flux = np.ones(len(self.position))
    else:
        self.flux = np.asarray(flux, dtype=float)

        if self.flux.ndim != 1:
            raise ValueError("flux must be a 1d array.")

        if len(self.flux) != len(self.position):
            raise ValueError(
                "Length of flux must be equal to length of " "positions."
            )

model(optics, return_wf=False, return_psf=False)

Models the source object through the provided optics.

Parameters:

Name Type Description Default
optics Optics

The optics through which to model the source object.

 required
return_wf bool = False

Should the Wavefront object be returned instead of the psf Array?

 False
return_psf bool = False

Should the PSF object be returned instead of the psf Array?

 False

Returns:

Name Type Description
object (Array, Wavefront, PSF)

if return_wf is False and return_psf is False, returns the psf Array. if return_wf is True and return_psf is False, returns the Wavefront object. if return_wf is False and return_psf is True, returns the PSF object.
Source code in src/dLux/sources.py 
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def model(
    self: Source,
    optics: Optics,
    return_wf: bool = False,
    return_psf: bool = False,
) -> Array:
    """
    Models the source object through the provided optics.

    Parameters
    ----------
    optics : Optics
        The optics through which to model the source object.
    return_wf : bool = False
        Should the Wavefront object be returned instead of the psf Array?
    return_psf : bool = False
        Should the PSF object be returned instead of the psf Array?

    Returns
    -------
    object : Array, Wavefront, PSF
        if `return_wf` is False and `return_psf` is False, returns the psf Array.
        if `return_wf` is True and `return_psf` is False, returns the Wavefront
            object.
        if `return_wf` is False and `return_psf` is True, returns the PSF object.
    """
    if return_wf and return_psf:
        raise ValueError(
            "return_wf and return_psf cannot both be True. "
            "Please choose one."
        )
    self = self.normalise()
    weights = self.weights[None, :] * self.flux[:, None]
    prop_fn = lambda position, weight: optics.propagate(
        self.wavelengths, position, weight, return_wf=True
    )
    wfs = filter_vmap(prop_fn)(self.position, weights)

    if return_wf:
        return wfs
    if return_psf:
        return PSF(wfs.psf.sum((0, 1)), wfs.pixel_scale.mean())
    else:
        return wfs.psf.sum((0, 1))

BinarySource

Bases: Source

A binary source parameterised by the position, flux, separation, position_angle, and contrast between the two sources.

UML

UML

Attributes:

Name Type Description
position (Array, radians)

The mean (x, y) on-sky position of this object.
mean_flux (float, photons)

The mean flux of the sources.
separation (float, radians)

The separation of the two sources in radians.
position_angle (float, radians)

The position angle between the two sources measured clockwise from the vertical axis.
contrast float

The contrast ratio between the two sources.
spectrum Spectrum

The spectrum of this object, represented by a Spectrum object.
Source code in src/dLux/sources.py 
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class BinarySource(Source):
    """
    A binary source parameterised by the position, flux, separation, position_angle,
    and contrast between the two sources.

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

    Attributes
    ----------
    position : Array, radians
        The mean (x, y) on-sky position of this object.
    mean_flux : float, photons
        The mean flux of the sources.
    separation : float, radians
        The separation of the two sources in radians.
    position_angle : float, radians
        The position angle between the two sources measured clockwise from the
        vertical axis.
    contrast : float
        The contrast ratio between the two sources.
    spectrum : Spectrum
        The spectrum of this object, represented by a Spectrum object.
    """

    position: Array
    mean_flux: float
    separation: float
    position_angle: float
    contrast: float

    def __init__(
        self: Source,
        wavelengths: Array = None,
        position: Array = np.zeros(2),
        mean_flux: float = 1.0,
        separation: float = 0.0,
        position_angle: float = np.pi / 2,
        contrast: float = 1.0,
        spectrum: Spectrum() = None,
        weights: Array = None,
    ):
        """
        Parameters
        ----------
        wavelengths : Array, metres = None
            The array of wavelengths at which the spectrum is defined.
        position : Array, radians = np.zeros(2)
            The (x, y) on-sky position of this object.
        mean_flux : float, photons = 1.
            The mean flux of the sources.
        separation : float, radians = 0.
            The separation of the two sources in radians.
        position_angle : float, radians = np.pi / 2
            The position angle between the two sources measured clockwise from the
            vertical axis.
        contrast : float = 1.
            The contrast ratio between the two sources.
        spectrum : Spectrum = None
            The spectrum of this object, represented by a Spectrum object.
        """
        wavelengths = np.asarray(wavelengths, dtype=float)
        if weights is None:
            weights = np.ones((2, len(wavelengths)))

        # Position and Flux
        self.position = np.asarray(position, dtype=float)
        self.mean_flux = float(mean_flux)

        if self.position.shape != (2,):
            raise ValueError("position must be a 1d array of shape (2,).")

        # Binary values
        self.separation = float(separation)
        self.position_angle = float(position_angle)
        self.contrast = float(contrast)

        super().__init__(
            wavelengths=wavelengths,
            spectrum=spectrum,
            weights=weights,
        )

    def model(
        self: Source,
        optics: Optics,
        return_wf: bool = False,
        return_psf: bool = False,
    ) -> Array:
        """
        Models the source object through the provided optics.

        Parameters
        ----------
        optics : Optics
            The optics through which to model the source object.
        return_wf : bool = False
            Should the Wavefront object be returned instead of the psf Array?
        return_psf : bool = False
            Should the PSF object be returned instead of the psf Array?

        Returns
        -------
        object : Array, Wavefront, PSF
            if `return_wf` is False and `return_psf` is False, returns the psf Array.
            if `return_wf` is True and `return_psf` is False, returns the Wavefront
                object.
            if `return_wf` is False and `return_psf` is True, returns the PSF object.
        """
        # Normalise and get input values
        self = self.normalise()
        positions = dlu.positions_from_sep(
            self.position, self.separation, self.position_angle
        )
        flux = dlu.fluxes_from_contrast(self.mean_flux, self.contrast)
        weights = self.weights * flux[:, None]

        # Return wf case is simple
        prop_fn = lambda position, weight: optics.propagate(
            self.wavelengths, position, weight, return_wf, return_psf
        )
        output = filter_vmap(prop_fn)(positions, weights)

        # Return wf is simple case
        if return_wf:
            return output

        # Return psf just requires constructing object
        if return_psf:
            return PSF(output.data.sum(0), output.pixel_scale.mean())

        # Return array is simple
        return output.sum(0)

__init__(wavelengths=None, position=np.zeros(2), mean_flux=1.0, separation=0.0, position_angle=np.pi / 2, contrast=1.0, spectrum=None, weights=None)

Parameters:

Name Type Description Default
wavelengths Array, metres = None

The array of wavelengths at which the spectrum is defined.

 None
position Array, radians = np.zeros(2)

The (x, y) on-sky position of this object.

 zeros(2)
mean_flux float, photons = 1.

The mean flux of the sources.

 1.0
separation float, radians = 0.

The separation of the two sources in radians.

 0.0
position_angle float, radians = np.pi / 2

The position angle between the two sources measured clockwise from the vertical axis.

 pi / 2
contrast float = 1.

The contrast ratio between the two sources.

 1.0
spectrum Spectrum = None

The spectrum of this object, represented by a Spectrum object.

 None
Source code in src/dLux/sources.py 
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def __init__(
    self: Source,
    wavelengths: Array = None,
    position: Array = np.zeros(2),
    mean_flux: float = 1.0,
    separation: float = 0.0,
    position_angle: float = np.pi / 2,
    contrast: float = 1.0,
    spectrum: Spectrum() = None,
    weights: Array = None,
):
    """
    Parameters
    ----------
    wavelengths : Array, metres = None
        The array of wavelengths at which the spectrum is defined.
    position : Array, radians = np.zeros(2)
        The (x, y) on-sky position of this object.
    mean_flux : float, photons = 1.
        The mean flux of the sources.
    separation : float, radians = 0.
        The separation of the two sources in radians.
    position_angle : float, radians = np.pi / 2
        The position angle between the two sources measured clockwise from the
        vertical axis.
    contrast : float = 1.
        The contrast ratio between the two sources.
    spectrum : Spectrum = None
        The spectrum of this object, represented by a Spectrum object.
    """
    wavelengths = np.asarray(wavelengths, dtype=float)
    if weights is None:
        weights = np.ones((2, len(wavelengths)))

    # Position and Flux
    self.position = np.asarray(position, dtype=float)
    self.mean_flux = float(mean_flux)

    if self.position.shape != (2,):
        raise ValueError("position must be a 1d array of shape (2,).")

    # Binary values
    self.separation = float(separation)
    self.position_angle = float(position_angle)
    self.contrast = float(contrast)

    super().__init__(
        wavelengths=wavelengths,
        spectrum=spectrum,
        weights=weights,
    )

model(optics, return_wf=False, return_psf=False)

Models the source object through the provided optics.

Parameters:

Name Type Description Default
optics Optics

The optics through which to model the source object.

 required
return_wf bool = False

Should the Wavefront object be returned instead of the psf Array?

 False
return_psf bool = False

Should the PSF object be returned instead of the psf Array?

 False

Returns:

Name Type Description
object (Array, Wavefront, PSF)

if return_wf is False and return_psf is False, returns the psf Array. if return_wf is True and return_psf is False, returns the Wavefront object. if return_wf is False and return_psf is True, returns the PSF object.
Source code in src/dLux/sources.py 
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def model(
    self: Source,
    optics: Optics,
    return_wf: bool = False,
    return_psf: bool = False,
) -> Array:
    """
    Models the source object through the provided optics.

    Parameters
    ----------
    optics : Optics
        The optics through which to model the source object.
    return_wf : bool = False
        Should the Wavefront object be returned instead of the psf Array?
    return_psf : bool = False
        Should the PSF object be returned instead of the psf Array?

    Returns
    -------
    object : Array, Wavefront, PSF
        if `return_wf` is False and `return_psf` is False, returns the psf Array.
        if `return_wf` is True and `return_psf` is False, returns the Wavefront
            object.
        if `return_wf` is False and `return_psf` is True, returns the PSF object.
    """
    # Normalise and get input values
    self = self.normalise()
    positions = dlu.positions_from_sep(
        self.position, self.separation, self.position_angle
    )
    flux = dlu.fluxes_from_contrast(self.mean_flux, self.contrast)
    weights = self.weights * flux[:, None]

    # Return wf case is simple
    prop_fn = lambda position, weight: optics.propagate(
        self.wavelengths, position, weight, return_wf, return_psf
    )
    output = filter_vmap(prop_fn)(positions, weights)

    # Return wf is simple case
    if return_wf:
        return output

    # Return psf just requires constructing object
    if return_psf:
        return PSF(output.data.sum(0), output.pixel_scale.mean())

    # Return array is simple
    return output.sum(0)

ResolvedSource

Bases: PointSource

A single resolved source with a spectrum, position, flux, and distribution array that represents the resolved component.

UML

UML

Attributes:

Name Type Description
position (Array, radians)

The (x, y) on-sky position of this object.
flux (float, photons)

The flux of the object.
distribution Array

The array of intensities representing the resolved source.
spectrum Spectrum

The spectrum of this object, represented by a Spectrum object.
Source code in src/dLux/sources.py 
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class ResolvedSource(PointSource):
    """
    A single resolved source with a spectrum, position, flux, and distribution array
    that represents the resolved component.

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

    Attributes
    ----------
    position : Array, radians
        The (x, y) on-sky position of this object.
    flux : float, photons
        The flux of the object.
    distribution : Array
        The array of intensities representing the resolved source.
    spectrum : Spectrum
        The spectrum of this object, represented by a Spectrum object.
    """

    distribution: Array

    def __init__(
        self: Source,
        wavelengths: Array = None,
        position: Array = np.zeros(2),
        flux: float = 1.0,
        distribution: Array = np.ones((3, 3)),
        weights: Array = None,
        spectrum: Spectrum() = None,
    ):
        """
        Parameters
        ----------
        wavelengths : Array, metres
            The array of wavelengths at which the spectrum is defined.
        position : Array, radians = np.zeros(2)
            The (x, y) on-sky position of this object.
        flux : float, photons = 1.
            The flux of the object.
        distribution : Array = np.ones((3, 3))
            The array of intensities representing the resolved source.
        weights : Array = None
            The spectral weights of the object.
        spectrum : Spectrum = None
            The spectrum of this object, represented by a Spectrum object.
        """
        distribution = np.asarray(distribution, dtype=float)
        self.distribution = distribution / distribution.sum()

        if self.distribution.ndim != 2:
            raise ValueError("distribution must be a 2d array.")

        super().__init__(
            position=position,
            flux=flux,
            spectrum=spectrum,
            wavelengths=wavelengths,
            weights=weights,
        )

    def normalise(self: Source) -> Source:
        """
        Method for returning a new source object with a normalised total
        spectrum and source distribution.

        Returns
        -------
        source : Source
            The source object with the normalised spectrum and distribution.
        """
        spectrum = self.spectrum.normalise()
        distribution_floor = np.maximum(self.distribution, 0.0)
        distribution = distribution_floor / distribution_floor.sum()
        return self.set(["spectrum", "distribution"], [spectrum, distribution])

    def model(
        self: Source,
        optics: Optics = None,
        return_wf: bool = False,
        return_psf: bool = False,
    ) -> Array:
        """
        Models the source object through the provided optics.

        Parameters
        ----------
        optics : Optics
            The optics through which to model the source object.
        return_wf : bool = False
            Should the Wavefront object be returned instead of the psf Array?
        return_psf : bool = False
            Should the PSF object be returned instead of the psf Array?

        Returns
        -------
        object : Array, Wavefront, PSF
            if `return_wf` is False and `return_psf` is False, returns the psf Array.
            if `return_wf` is True and `return_psf` is False, returns the Wavefront
                object.
            if `return_wf` is False and `return_psf` is True, returns the PSF object.
        """
        if return_wf and return_psf:
            raise ValueError(
                "return_wf and return_psf cannot both be True. "
                "Please choose one."
            )
        # Normalise and get parameters
        self = self.normalise()
        weights = self.weights * self.flux

        # Note we always return wf here so we can convolve each wavelength
        # individually if a chromatic wavefront output is required.
        wf = optics.propagate(
            self.wavelengths, self.position, weights, return_wf=True
        )

        # Returning wf is a special case
        if return_wf:
            conv_fn = lambda psf: convolve(psf, self.distribution, mode="same")
            return wf.set("amplitude", vmap(conv_fn)(wf.psf) ** 0.5)

        # Return psf object
        conv_psf = convolve(wf.psf.sum(0), self.distribution, mode="same")
        if return_psf:
            return PSF(conv_psf, wf.pixel_scale.mean())

        # Return array psf
        return conv_psf

__init__(wavelengths=None, position=np.zeros(2), flux=1.0, distribution=np.ones((3, 3)), weights=None, spectrum=None)

Parameters:

Name Type Description Default
wavelengths (Array, metres)

The array of wavelengths at which the spectrum is defined.

 None
position Array, radians = np.zeros(2)

The (x, y) on-sky position of this object.

 zeros(2)
flux float, photons = 1.

The flux of the object.

 1.0
distribution Array = np.ones((3, 3))

The array of intensities representing the resolved source.

 ones((3, 3))
weights Array = None

The spectral weights of the object.

 None
spectrum Spectrum = None

The spectrum of this object, represented by a Spectrum object.

 None
Source code in src/dLux/sources.py 
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def __init__(
    self: Source,
    wavelengths: Array = None,
    position: Array = np.zeros(2),
    flux: float = 1.0,
    distribution: Array = np.ones((3, 3)),
    weights: Array = None,
    spectrum: Spectrum() = None,
):
    """
    Parameters
    ----------
    wavelengths : Array, metres
        The array of wavelengths at which the spectrum is defined.
    position : Array, radians = np.zeros(2)
        The (x, y) on-sky position of this object.
    flux : float, photons = 1.
        The flux of the object.
    distribution : Array = np.ones((3, 3))
        The array of intensities representing the resolved source.
    weights : Array = None
        The spectral weights of the object.
    spectrum : Spectrum = None
        The spectrum of this object, represented by a Spectrum object.
    """
    distribution = np.asarray(distribution, dtype=float)
    self.distribution = distribution / distribution.sum()

    if self.distribution.ndim != 2:
        raise ValueError("distribution must be a 2d array.")

    super().__init__(
        position=position,
        flux=flux,
        spectrum=spectrum,
        wavelengths=wavelengths,
        weights=weights,
    )

model(optics=None, return_wf=False, return_psf=False)

Models the source object through the provided optics.

Parameters:

Name Type Description Default
optics Optics

The optics through which to model the source object.

 None
return_wf bool = False

Should the Wavefront object be returned instead of the psf Array?

 False
return_psf bool = False

Should the PSF object be returned instead of the psf Array?

 False

Returns:

Name Type Description
object (Array, Wavefront, PSF)

if return_wf is False and return_psf is False, returns the psf Array. if return_wf is True and return_psf is False, returns the Wavefront object. if return_wf is False and return_psf is True, returns the PSF object.
Source code in src/dLux/sources.py 
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def model(
    self: Source,
    optics: Optics = None,
    return_wf: bool = False,
    return_psf: bool = False,
) -> Array:
    """
    Models the source object through the provided optics.

    Parameters
    ----------
    optics : Optics
        The optics through which to model the source object.
    return_wf : bool = False
        Should the Wavefront object be returned instead of the psf Array?
    return_psf : bool = False
        Should the PSF object be returned instead of the psf Array?

    Returns
    -------
    object : Array, Wavefront, PSF
        if `return_wf` is False and `return_psf` is False, returns the psf Array.
        if `return_wf` is True and `return_psf` is False, returns the Wavefront
            object.
        if `return_wf` is False and `return_psf` is True, returns the PSF object.
    """
    if return_wf and return_psf:
        raise ValueError(
            "return_wf and return_psf cannot both be True. "
            "Please choose one."
        )
    # Normalise and get parameters
    self = self.normalise()
    weights = self.weights * self.flux

    # Note we always return wf here so we can convolve each wavelength
    # individually if a chromatic wavefront output is required.
    wf = optics.propagate(
        self.wavelengths, self.position, weights, return_wf=True
    )

    # Returning wf is a special case
    if return_wf:
        conv_fn = lambda psf: convolve(psf, self.distribution, mode="same")
        return wf.set("amplitude", vmap(conv_fn)(wf.psf) ** 0.5)

    # Return psf object
    conv_psf = convolve(wf.psf.sum(0), self.distribution, mode="same")
    if return_psf:
        return PSF(conv_psf, wf.pixel_scale.mean())

    # Return array psf
    return conv_psf

normalise()

Method for returning a new source object with a normalised total spectrum and source distribution.

Returns:

Name Type Description
source Source

The source object with the normalised spectrum and distribution.
Source code in src/dLux/sources.py 
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def normalise(self: Source) -> Source:
    """
    Method for returning a new source object with a normalised total
    spectrum and source distribution.

    Returns
    -------
    source : Source
        The source object with the normalised spectrum and distribution.
    """
    spectrum = self.spectrum.normalise()
    distribution_floor = np.maximum(self.distribution, 0.0)
    distribution = distribution_floor / distribution_floor.sum()
    return self.set(["spectrum", "distribution"], [spectrum, distribution])

PointResolvedSource

Bases: ResolvedSource

A class for modelling a point source and a resolved source that is defined relative to the point source. An example would be an unresolved star with a resolved dust shell or debris disk. These two objects share the same spectra but have their flux defined by flux (the mean flux) and the flux ratio (contrast) between the point source and resolved distribution. The resolved component is defined by an array of intensities that represent the resolved distribution.

UML

UML

Attributes:

Name Type Description
position (Array, radians)

The (x, y) on-sky position of this object.
flux (float, photons)

The mean flux of the point and resolved source.
distribution Array

The array of intensities representing the resolved source.
contrast float

The contrast ratio between the point source and the resolved source.
spectrum Spectrum

The spectrum of this object, represented by a Spectrum object.
Source code in src/dLux/sources.py 
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class PointResolvedSource(ResolvedSource):
    """
    A class for modelling a point source and a resolved source that is defined
    relative to the point source. An example would be an unresolved star with
    a resolved dust shell or debris disk. These two objects share the same
    spectra but have their flux defined by flux (the mean flux) and the flux
    ratio (contrast) between the point source and resolved distribution. The
    resolved component is defined by an array of intensities that represent
    the resolved distribution.

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

    Attributes
    ----------
    position : Array, radians
        The (x, y) on-sky position of this object.
    flux : float, photons
        The mean flux of the point and resolved source.
    distribution : Array
        The array of intensities representing the resolved source.
    contrast : float
        The contrast ratio between the point source and the resolved source.
    spectrum : Spectrum
        The spectrum of this object, represented by a Spectrum object.
    """

    contrast: float

    def __init__(
        self: Source,
        wavelengths: Array = None,
        position: Array = np.zeros(2),
        flux: float = 1.0,
        distribution: Array = np.ones((3, 3)),
        contrast: float = 1.0,
        weights: Array = None,
        spectrum: Spectrum() = None,
    ) -> Source:
        """
        Parameters
        ----------
        wavelengths : Array, metres = None
            The array of wavelengths at which the spectrum is defined.
        position : Array, radians = np.zeros(2)
            The (x, y) on-sky position of this object.
        flux : float, photons = 1.
            The mean flux of the point and resolved source.
        distribution : Array = np.ones((3, 3))
            The array of intensities representing the resolved source.
        contrast : float = 1.
            The contrast ratio between the point source and the resolved source.
        weights : Array = None
            The spectral weights of the object.
        spectrum : Spectrum = None
            The spectrum of this object, represented by a Spectrum object.
        """
        wavelengths = np.asarray(wavelengths, dtype=float)
        if weights is None:
            weights = np.ones((2, len(wavelengths)))

        self.contrast = float(contrast)

        super().__init__(
            wavelengths=wavelengths,
            position=position,
            flux=flux,
            distribution=distribution,
            spectrum=spectrum,
            weights=weights,
            # contrast=contrast,
        )

    def model(
        self: Source,
        optics: Optics,
        return_wf: bool = False,
        return_psf: bool = False,
    ) -> Array:
        """
        Models the source object through the provided optics.

        Parameters
        ----------
        optics : Optics
            The optics through which to model the source object.
        return_wf : bool = False
            Should the Wavefront object be returned instead of the psf Array?
        return_psf : bool = False
            Should the PSF object be returned instead of the psf Array?

        Returns
        -------
        object : Array, Wavefront, PSF
            if `return_wf` is False and `return_psf` is False, returns the psf Array.
            if `return_wf` is True and `return_psf` is False, returns the Wavefront
                object.
            if `return_wf` is False and `return_psf` is True, returns the PSF object.
        """
        if return_wf and return_psf:
            raise ValueError(
                "return_wf and return_psf cannot both be True. "
                "Please choose one."
            )
        # Normalise and get parameters
        self = self.normalise()
        flux = dlu.fluxes_from_contrast(self.flux, self.contrast)
        weights = self.weights * flux[:, None]

        # Note we always return wf here so we can convolve each wavelength
        # individually if a chromatic wavefront output is required. We also
        # Can not propagate the weights since they have different values
        # for the point and resolved source.
        wf = optics.propagate(self.wavelengths, self.position, return_wf=True)

        # Returning wf is a special case, we need to convolve each psf with
        # the distribution, and them re-combine them into a vectorised wf
        if return_wf:
            # Perform convolution
            conv_fn = lambda psf: convolve(psf, self.distribution, mode="same")
            conv_wf = wf.set("amplitude", vmap(conv_fn)(wf.psf) ** 0.5)

            # Stack leaves manually, this is a bit of a hack to get around
            # string leaf errors from tree_map, and to avoid
            # flattening/unflattening with partition and combine
            stack_leaves = lambda x, y: np.stack([x, y], axis=0)
            amplitudes = stack_leaves(wf.amplitude, conv_wf.amplitude)
            phases = stack_leaves(wf.phase, conv_wf.phase)
            pixel_scales = stack_leaves(wf.pixel_scale, conv_wf.pixel_scale)
            wavelengths = stack_leaves(wf.wavelength, conv_wf.wavelength)

            # Combine into single wf and finally apply weights
            combined_wf = wf.set(
                ["wavelength", "amplitude", "phase", "pixel_scale"],
                [wavelengths, amplitudes, phases, pixel_scales],
            )
            return combined_wf.multiply("amplitude", weights[:, :, None, None])

        # Create singe array psf object
        point_psf = (np.expand_dims(weights[0], (1, 2)) * wf.psf).sum(0)
        resolved_psf = (np.expand_dims(weights[1], (1, 2)) * wf.psf).sum(0)
        conv_psf = convolve(resolved_psf, self.distribution, mode="same")
        psf = point_psf + conv_psf
        if return_psf:
            return PSF(psf, wf.pixel_scale.mean())

        # Return array psf
        return psf

__init__(wavelengths=None, position=np.zeros(2), flux=1.0, distribution=np.ones((3, 3)), contrast=1.0, weights=None, spectrum=None)

Parameters:

Name Type Description Default
wavelengths Array, metres = None

The array of wavelengths at which the spectrum is defined.

 None
position Array, radians = np.zeros(2)

The (x, y) on-sky position of this object.

 zeros(2)
flux float, photons = 1.

The mean flux of the point and resolved source.

 1.0
distribution Array = np.ones((3, 3))

The array of intensities representing the resolved source.

 ones((3, 3))
contrast float = 1.

The contrast ratio between the point source and the resolved source.

 1.0
weights Array = None

The spectral weights of the object.

 None
spectrum Spectrum = None

The spectrum of this object, represented by a Spectrum object.

 None
Source code in src/dLux/sources.py 
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def __init__(
    self: Source,
    wavelengths: Array = None,
    position: Array = np.zeros(2),
    flux: float = 1.0,
    distribution: Array = np.ones((3, 3)),
    contrast: float = 1.0,
    weights: Array = None,
    spectrum: Spectrum() = None,
) -> Source:
    """
    Parameters
    ----------
    wavelengths : Array, metres = None
        The array of wavelengths at which the spectrum is defined.
    position : Array, radians = np.zeros(2)
        The (x, y) on-sky position of this object.
    flux : float, photons = 1.
        The mean flux of the point and resolved source.
    distribution : Array = np.ones((3, 3))
        The array of intensities representing the resolved source.
    contrast : float = 1.
        The contrast ratio between the point source and the resolved source.
    weights : Array = None
        The spectral weights of the object.
    spectrum : Spectrum = None
        The spectrum of this object, represented by a Spectrum object.
    """
    wavelengths = np.asarray(wavelengths, dtype=float)
    if weights is None:
        weights = np.ones((2, len(wavelengths)))

    self.contrast = float(contrast)

    super().__init__(
        wavelengths=wavelengths,
        position=position,
        flux=flux,
        distribution=distribution,
        spectrum=spectrum,
        weights=weights,
        # contrast=contrast,
    )

model(optics, return_wf=False, return_psf=False)

Models the source object through the provided optics.

Parameters:

Name Type Description Default
optics Optics

The optics through which to model the source object.

 required
return_wf bool = False

Should the Wavefront object be returned instead of the psf Array?

 False
return_psf bool = False

Should the PSF object be returned instead of the psf Array?

 False

Returns:

Name Type Description
object (Array, Wavefront, PSF)

if return_wf is False and return_psf is False, returns the psf Array. if return_wf is True and return_psf is False, returns the Wavefront object. if return_wf is False and return_psf is True, returns the PSF object.
Source code in src/dLux/sources.py 
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def model(
    self: Source,
    optics: Optics,
    return_wf: bool = False,
    return_psf: bool = False,
) -> Array:
    """
    Models the source object through the provided optics.

    Parameters
    ----------
    optics : Optics
        The optics through which to model the source object.
    return_wf : bool = False
        Should the Wavefront object be returned instead of the psf Array?
    return_psf : bool = False
        Should the PSF object be returned instead of the psf Array?

    Returns
    -------
    object : Array, Wavefront, PSF
        if `return_wf` is False and `return_psf` is False, returns the psf Array.
        if `return_wf` is True and `return_psf` is False, returns the Wavefront
            object.
        if `return_wf` is False and `return_psf` is True, returns the PSF object.
    """
    if return_wf and return_psf:
        raise ValueError(
            "return_wf and return_psf cannot both be True. "
            "Please choose one."
        )
    # Normalise and get parameters
    self = self.normalise()
    flux = dlu.fluxes_from_contrast(self.flux, self.contrast)
    weights = self.weights * flux[:, None]

    # Note we always return wf here so we can convolve each wavelength
    # individually if a chromatic wavefront output is required. We also
    # Can not propagate the weights since they have different values
    # for the point and resolved source.
    wf = optics.propagate(self.wavelengths, self.position, return_wf=True)

    # Returning wf is a special case, we need to convolve each psf with
    # the distribution, and them re-combine them into a vectorised wf
    if return_wf:
        # Perform convolution
        conv_fn = lambda psf: convolve(psf, self.distribution, mode="same")
        conv_wf = wf.set("amplitude", vmap(conv_fn)(wf.psf) ** 0.5)

        # Stack leaves manually, this is a bit of a hack to get around
        # string leaf errors from tree_map, and to avoid
        # flattening/unflattening with partition and combine
        stack_leaves = lambda x, y: np.stack([x, y], axis=0)
        amplitudes = stack_leaves(wf.amplitude, conv_wf.amplitude)
        phases = stack_leaves(wf.phase, conv_wf.phase)
        pixel_scales = stack_leaves(wf.pixel_scale, conv_wf.pixel_scale)
        wavelengths = stack_leaves(wf.wavelength, conv_wf.wavelength)

        # Combine into single wf and finally apply weights
        combined_wf = wf.set(
            ["wavelength", "amplitude", "phase", "pixel_scale"],
            [wavelengths, amplitudes, phases, pixel_scales],
        )
        return combined_wf.multiply("amplitude", weights[:, :, None, None])

    # Create singe array psf object
    point_psf = (np.expand_dims(weights[0], (1, 2)) * wf.psf).sum(0)
    resolved_psf = (np.expand_dims(weights[1], (1, 2)) * wf.psf).sum(0)
    conv_psf = convolve(resolved_psf, self.distribution, mode="same")
    psf = point_psf + conv_psf
    if return_psf:
        return PSF(psf, wf.pixel_scale.mean())

    # Return array psf
    return psf

Scene

Bases: BaseSource

A source object that holds a set of sources that are model simultaneously.

UML

UML

Attributes:

Name Type Description
sources dict

A dictionary of source objects to model simultaneously.
Source code in src/dLux/sources.py 
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class Scene(BaseSource):
    """
    A source object that holds a set of sources that are model simultaneously.

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

    Attributes
    ----------
    sources : dict
        A dictionary of source objects to model simultaneously.
    """

    sources: dict

    def __init__(self: Scene, sources: list[Source]):
        """
        Parameters
        ----------
        sources : list[Source]
            A list of source objects to model simultaneously.
        """
        super().__init__()
        if isinstance(sources, (BaseSource, tuple)):
            sources = [sources]
        self.sources = dlu.list2dictionary(sources, False, BaseSource)

    def normalise(self: Scene) -> Scene:
        """
        Method for returning a new scene with normalised source objects.

        Returns
        -------
        scene : Scene
            The normalised scene object.
        """
        is_source = lambda leaf: isinstance(leaf, BaseSource)
        norm_fn = lambda source: source.normalise()
        sources = tree_map(norm_fn, self.sources, is_leaf=is_source)
        return self.set("sources", sources)

    def __getattr__(self: Source, key: str) -> Any:
        """
        Raises the individual sources via their keys.

        Parameters
        ----------
        key : str
            The key of the item to be searched for in the sub-dictionaries.

        Returns
        -------
        item : object
            The item corresponding to the supplied key in the sub-dictionaries.
        """
        if key in self.sources.keys():
            return self.sources[key]
        raise AttributeError(
            f"{self.__class__.__name__} has no attribute " f"{key}."
        )

    def model(
        self: Scene,
        optics: Optics(),
        return_wf: bool = False,
        return_psf: bool = False,
    ) -> Array:
        """
        Models the source object through the provided optics.

        Parameters
        ----------
        optics : Optics
            The optics through which to model the source object.
        return_wf : bool = False
            Should the Wavefront object be returned instead of the psf Array?
        return_psf : bool = False
            Should the PSF object be returned instead of the psf Array?

        Returns
        -------
        object : Array, Wavefront, PSF
            if `return_wf` is False and `return_psf` is False, returns the psf Array.
            if `return_wf` is True and `return_psf` is False, returns the Wavefront
                object.
            if `return_wf` is False and `return_psf` is True, returns the PSF object.
        """
        self = self.normalise()

        # Define leaf_fn and map across sources
        leaf_fn = lambda leaf: isinstance(leaf, BaseSource)
        output = tree_map(
            lambda s: s.model(optics, return_wf, return_psf),
            self.sources,
            is_leaf=leaf_fn,
        )

        # Return wf case is simple
        if return_wf:
            return output

        # Return psf case requires mapping across the psf outputs
        if return_psf:
            # Define mapping function
            leaf_fn = lambda leaf: isinstance(leaf, PSF)
            get_psfs = lambda psf: psf.data.sum(tuple(range(psf.ndim)))
            get_pscales = lambda psf: psf.pixel_scale.mean()

            # Get values and return PSF
            psf = dlu.map2array(get_psfs, output, leaf_fn).sum(0)
            pixel_scale = dlu.map2array(get_pscales, output, leaf_fn).mean()
            return PSF(psf, pixel_scale)

        # Return array is simple
        return dlu.map2array(lambda x: x, output).sum(0)

__getattr__(key)

Raises the individual sources via their keys.

Parameters:

Name Type Description Default
key str

The key of the item to be searched for in the sub-dictionaries.

 required

Returns:

Name Type Description
item object

The item corresponding to the supplied key in the sub-dictionaries.
Source code in src/dLux/sources.py 
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def __getattr__(self: Source, key: str) -> Any:
    """
    Raises the individual sources via their keys.

    Parameters
    ----------
    key : str
        The key of the item to be searched for in the sub-dictionaries.

    Returns
    -------
    item : object
        The item corresponding to the supplied key in the sub-dictionaries.
    """
    if key in self.sources.keys():
        return self.sources[key]
    raise AttributeError(
        f"{self.__class__.__name__} has no attribute " f"{key}."
    )

__init__(sources)

Parameters:

Name Type Description Default
sources list[Source]

A list of source objects to model simultaneously.

 required
Source code in src/dLux/sources.py 
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def __init__(self: Scene, sources: list[Source]):
    """
    Parameters
    ----------
    sources : list[Source]
        A list of source objects to model simultaneously.
    """
    super().__init__()
    if isinstance(sources, (BaseSource, tuple)):
        sources = [sources]
    self.sources = dlu.list2dictionary(sources, False, BaseSource)

model(optics, return_wf=False, return_psf=False)

Models the source object through the provided optics.

Parameters:

Name Type Description Default
optics Optics

The optics through which to model the source object.

 required
return_wf bool = False

Should the Wavefront object be returned instead of the psf Array?

 False
return_psf bool = False

Should the PSF object be returned instead of the psf Array?

 False

Returns:

Name Type Description
object (Array, Wavefront, PSF)

if return_wf is False and return_psf is False, returns the psf Array. if return_wf is True and return_psf is False, returns the Wavefront object. if return_wf is False and return_psf is True, returns the PSF object.
Source code in src/dLux/sources.py 
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def model(
    self: Scene,
    optics: Optics(),
    return_wf: bool = False,
    return_psf: bool = False,
) -> Array:
    """
    Models the source object through the provided optics.

    Parameters
    ----------
    optics : Optics
        The optics through which to model the source object.
    return_wf : bool = False
        Should the Wavefront object be returned instead of the psf Array?
    return_psf : bool = False
        Should the PSF object be returned instead of the psf Array?

    Returns
    -------
    object : Array, Wavefront, PSF
        if `return_wf` is False and `return_psf` is False, returns the psf Array.
        if `return_wf` is True and `return_psf` is False, returns the Wavefront
            object.
        if `return_wf` is False and `return_psf` is True, returns the PSF object.
    """
    self = self.normalise()

    # Define leaf_fn and map across sources
    leaf_fn = lambda leaf: isinstance(leaf, BaseSource)
    output = tree_map(
        lambda s: s.model(optics, return_wf, return_psf),
        self.sources,
        is_leaf=leaf_fn,
    )

    # Return wf case is simple
    if return_wf:
        return output

    # Return psf case requires mapping across the psf outputs
    if return_psf:
        # Define mapping function
        leaf_fn = lambda leaf: isinstance(leaf, PSF)
        get_psfs = lambda psf: psf.data.sum(tuple(range(psf.ndim)))
        get_pscales = lambda psf: psf.pixel_scale.mean()

        # Get values and return PSF
        psf = dlu.map2array(get_psfs, output, leaf_fn).sum(0)
        pixel_scale = dlu.map2array(get_pscales, output, leaf_fn).mean()
        return PSF(psf, pixel_scale)

    # Return array is simple
    return dlu.map2array(lambda x: x, output).sum(0)

normalise()

Method for returning a new scene with normalised source objects.

Returns:

Name Type Description
scene Scene

The normalised scene object.
Source code in src/dLux/sources.py 
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def normalise(self: Scene) -> Scene:
    """
    Method for returning a new scene with normalised source objects.

    Returns
    -------
    scene : Scene
        The normalised scene object.
    """
    is_source = lambda leaf: isinstance(leaf, BaseSource)
    norm_fn = lambda source: source.normalise()
    sources = tree_map(norm_fn, self.sources, is_leaf=is_source)
    return self.set("sources", sources)