Source code for astrophot.models.airy

import torch
import numpy as np

from ..utils.decorators import ignore_numpy_warnings, combine_docstrings
from .psf_model_object import PSFModel
from .mixins import RadialMixin
from ..param import forward
from ..backend_obj import backend, ArrayLike

__all__ = ("AiryPSF",)


[docs] @combine_docstrings class AiryPSF(RadialMixin, PSFModel): """The Airy disk is an analytic description of the diffraction pattern for a circular aperture. The diffraction pattern is described exactly by the configuration of the lens system under the assumption that all elements are perfect. This expression goes as: .. math:: I(\\theta) = I_0\\left[\\frac{2J_1(x)}{x}\\right]^2 .. math:: x = ka\\sin(\\theta) = \\frac{2\\pi a r}{\\lambda R} where :math:`I(\\theta)` is the intensity as a function of the angular position within the diffraction system along its main axis, :math:`I_0` is the central intensity of the airy disk, :math:`J_1` is the Bessel function of the first kind of order one, :math:`k = \\frac{2\\pi}{\\lambda}` is the wavenumber of the light, :math:`a` is the aperture radius, :math:`r` is the radial position from the center of the pattern, :math:`R` is the distance from the circular aperture to the observation plane. In the ``Airy_PSF`` class we combine the parameters :math:`a,R,\\lambda` into a single ratio to be optimized (or fixed by the optical configuration). :param I0: The central intensity of the airy disk in flux/arcsec^2. :param aRL: The ratio of the aperture radius to the product of the wavelength and the distance from the aperture to the observation plane, :math:`\\frac{a}{R \\lambda}`. """ _model_type = "airy" _parameter_specs = { "I0": { "units": "flux/arcsec^2", "value": 1.0, "shape": (), "dynamic": False, "description": "The central intensity of the airy disk in flux/arcsec^2.", }, "aRL": { "units": "a/(R lambda)", "shape": (), "dynamic": True, "description": "The ratio of the aperture radius to the product of the wavelength and the distance from the aperture to the observation plane.", }, } usable = True
[docs] @torch.no_grad() @ignore_numpy_warnings def initialize(self): super().initialize() if self.I0.initialized and self.aRL.initialized: return icenter = self.target.targpixel_to_mypixel(*self.center.value) if not self.I0.initialized: mid_chunk = self.target._data[ int(icenter[0]) - 2 : int(icenter[0]) + 2, int(icenter[1]) - 2 : int(icenter[1]) + 2, ] self.I0.value = backend.mean(mid_chunk) / self.target.upsample**2 if not self.aRL.initialized: self.aRL.value = (5.0 / 8.0) * 2
[docs] @forward def radial_model(self, R: ArrayLike, I0: ArrayLike, aRL: ArrayLike) -> ArrayLike: x = 2 * np.pi * aRL * R return I0 * (2 * backend.bessel_j1(x) / x) ** 2