267 lines
13 KiB
Python
267 lines
13 KiB
Python
from typing import Union
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import numpy as np
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from astropy import units as u
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from .IPSF import IPSF
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from ..sensor.PixelMask import PixelMask
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from scipy.optimize import newton, fmin, bisect
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from scipy.special import j0, j1
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from scipy.signal import fftconvolve
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from scipy.integrate import quad
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from scipy.interpolate import interp1d
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class Airy(IPSF):
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"""
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A class for modelling the PSF using an airy disk.
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"""
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@u.quantity_input(wl="length", d_aperture="length")
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def __init__(self, f_number: float, wl: u.Quantity, d_aperture: u.Quantity, osf: float, pixel_size: u.Quantity):
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"""
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Initialize a new PSF from a airy disk.
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Parameters
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----------
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f_number : float
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The working focal number of the optical system
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wl : Quantity
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The central wavelength which is used for calculating the PSF
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d_aperture : Quantity
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The diameter of the telescope's aperture.
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osf : float
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The oversampling factor to be used for oversampling the PSF with regards to the pixel size.
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pixel_size : Quantity
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The size of a pixel as length-quantity.
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"""
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self.__f_number = f_number
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self.__wl = wl
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self.__d_aperture = d_aperture
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self.__osf = osf
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self.__pixel_size = pixel_size
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self.__psf_jitter = None
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def calcReducedObservationAngle(self, contained_energy: Union[str, int, float],
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jitter_sigma: u.Quantity = None, obstruction: float = 0.0) -> u.Quantity:
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"""
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Calculate the reduced observation angle in lambda / d_ap for the given contained energy.
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Parameters
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----------
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contained_energy : Union[str, int, float]
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The percentage of energy to be contained within a circle with the diameter reduced observation angle.
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jitter_sigma : Quantity
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Sigma of the telescope's jitter in arcsec
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obstruction : float
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The central obstruction as ratio A_ob / A_ap
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Returns
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-------
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reduced_observation_angle: Quantity
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The reduced observation angle in lambda / d_ap
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"""
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# Calculate the reduced observation angle in lambda / D for the given encircled energy
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reduced_observation_angle = 0 * u.dimensionless_unscaled
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if type(contained_energy) == str:
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# Encircled energy is of type string
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if contained_energy.lower() == "peak":
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# For the peak value of the PSF, the observation angle becomes zero which leads to one exposed
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# pixel later in the code
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reduced_observation_angle = 0 * u.dimensionless_unscaled
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elif contained_energy.lower() == "fwhm":
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# Width of the FWHM of the airy disk
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reduced_observation_angle = 1.028
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if not np.isclose(obstruction, 0.0):
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# Use obstructed airy disk
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reduced_observation_angle = newton(lambda y: self.__airy(np.pi * y, np.sqrt(obstruction)) - 0.5,
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reduced_observation_angle / 2) * 2
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contained_energy = "fwhm"
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elif contained_energy.lower() == "min":
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# Width of the first minimum of the airy disk
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reduced_observation_angle = 1.22 * 2
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contained_energy = 0.8377 * u.dimensionless_unscaled
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if not np.isclose(obstruction, 0.0):
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# Use obstructed airy disk
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reduced_observation_angle = fmin(lambda y: self.__airy(np.pi * y, np.sqrt(obstruction)),
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reduced_observation_angle / 2, disp=False)[0] * 2
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contained_energy = self.__airy_int(np.pi * reduced_observation_angle / 2,
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np.sqrt(obstruction)) * u.dimensionless_unscaled
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else:
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# Calculate the width numerically from the integral of the airy disk
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contained_energy = contained_energy / 100 * u.dimensionless_unscaled
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reduced_observation_angle = 2 * bisect(
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lambda y: self.__airy_int(np.pi * y, np.sqrt(obstruction)) - contained_energy.value, 0, 100)
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if jitter_sigma is not None and (isinstance(contained_energy, u.Quantity) or isinstance(contained_energy, str)
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and contained_energy.lower() == "fwhm"):
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# Convert jitter to reduced observation angle in lambda / d_ap
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jitter_sigma = jitter_sigma.to(u.rad).value * self.__d_aperture / self.__wl.to(u.m)
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# Calculate necessary grid length to accommodate the psf and 3-sigma of the gaussian
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grid_width = (reduced_observation_angle / 2 + 3 * jitter_sigma)
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# Calculate the reduced observation angle of a single detector pixel
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reduced_observation_angle_pixel = (self.__pixel_size / (
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self.__f_number * self.__d_aperture) * self.__d_aperture / self.__wl).decompose()
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# Calculate the width of each grid element
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dx = reduced_observation_angle_pixel.value / self.__osf
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# Calculate the necessary number of points on the grid
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n_points = np.ceil(grid_width / dx).value
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# Calculate the corresponding x-coordinates of each grid element
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x = np.arange(1, n_points + 1) * dx
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# Calculate the psf from an airy disk for each element on the grid
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psf = self.__airy(np.pi * x, np.sqrt(obstruction))
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# Calculate the integral of the undisturbed airy disk in order to scale the result of the convolution
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total = np.sum(psf * x) * dx * 2 * np.pi
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# Mirror the PSF to the negative x-domain
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psf = np.concatenate((np.flip(psf), np.array([1]), psf))
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# Calculate a gaussian kernel
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kernel = 1 / (2 * np.pi * jitter_sigma ** 2) * np.exp(
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- np.concatenate((np.flip(x), np.array([0]), x)) ** 2 / (2 * jitter_sigma ** 2))
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# Normalize the kernel
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kernel = kernel / np.sum(kernel)
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# Convolve the PSF with gaussian kernel
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psf = fftconvolve(np.pad(psf, int(n_points), mode="constant", constant_values=0), kernel, mode="same")
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# Reduce the PSF to the positive x-domain
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psf = psf[int((psf.shape[0] - 1) / 2):]
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# Scale the integral of the disturbed PSF equal to the undisturbed PSF
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psf = psf / (np.sum(psf * np.arange(psf.shape[0]) * dx) * dx * 2 * np.pi) * total
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self.__psf_jitter = psf
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if isinstance(contained_energy, str) and contained_energy.lower() == "fwhm":
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reduced_observation_angle = np.argmax(psf < psf[0] / 2) * reduced_observation_angle_pixel.value / \
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self.__osf * 2
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else:
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# Calculate the rolling integral of the PSF
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psf_int = np.cumsum(psf * np.arange(psf.shape[0]) * dx) * dx * 2 * np.pi
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# Scale the integral of the disturbed PSF equal to the undisturbed PSF
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psf_int = psf_int / (4 / np.pi) * (1 - obstruction)
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# Calculate the reduced observation angle
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reduced_observation_angle = np.argmax(
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psf_int > contained_energy) * reduced_observation_angle_pixel.value / self.__osf * 2
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return reduced_observation_angle * u.dimensionless_unscaled
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@staticmethod
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def __airy(x: Union[float, np.ndarray], obstruction: float = None) -> Union[float, np.ndarray]:
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"""
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Calculate function values of the airy disk
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Parameters
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----------
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x : Union[float, np.ndarray]
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radial coordinate to calculate the function value for.
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obstruction : float
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The linear central obstruction ratio of the aperture.
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Returns
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-------
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res : Union[float, np.ndarray]
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The function values of the airy disk at the given coordinates
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"""
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# Standardize input values
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if not isinstance(x, np.ndarray):
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x = np.array([x])
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# Initialize return values and assign values for the singularity at x=0
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res = np.zeros(x.shape)
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res[np.isclose(x, 0.0)] = 1.0
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x_temp = x[np.invert(np.isclose(x, 0.0))]
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if obstruction and not np.isclose(obstruction, 0.0):
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# Use obstructed airy disk
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# See also https://en.wikipedia.org/wiki/Airy_disk#Obscured_Airy_pattern
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res[np.invert(np.isclose(x, 0.0))] = 1 / (1 - obstruction ** 2) ** 2 * (
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2 * (j1(x_temp) - obstruction * j1(obstruction * x_temp)) / x_temp) ** 2
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else:
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# Use unobstructed airy disk
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# See also https://en.wikipedia.org/wiki/Airy_disk#Mathematical_formulation
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res[np.invert(np.isclose(x, 0.0))] = (2 * j1(x_temp) / x_temp) ** 2
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# Unbox arrays of length 1
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if len(res.shape) == 1 and len(res) == 1:
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res = res[0]
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return res
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@staticmethod
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def __airy_int(x: float, obstruction: float = None) -> float:
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"""
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Calculate the integral of the airy disk from 0 to x.
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Parameters
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----------
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x : float
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The upper limit for the integration.
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obstruction : float
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The linear central obstruction ratio of the aperture.
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Returns
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-------
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res : float
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The integral of the airy disk.
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"""
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if np.isclose(x, 0.0):
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# Short circuit for an integration-range of length 0
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return 0.
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else:
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if obstruction and not np.isclose(obstruction, 0.0):
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# Use integral of obstructed airy disk
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# See also https://en.wikipedia.org/wiki/Airy_disk#Obscured_Airy_pattern
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return 1 / (1 - obstruction ** 2) * (1 - j0(x) ** 2 - j1(x) ** 2 + obstruction ** 2 * (
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1 - j0(obstruction * x) ** 2 - j1(obstruction * x) ** 2) - 4 * obstruction * quad(
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lambda y: j1(y) * j1(obstruction * y) / y, 0, x, limit=100, epsrel=1e-6)[0])
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else:
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# Use unobstructed airy disk
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# See also https://en.wikipedia.org/wiki/Airy_disk#Mathematical_formulation
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return 1 - j0(x) ** 2 - j1(x) ** 2
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def mapToPixelMask(self, mask: PixelMask, jitter_sigma: u.Quantity = None, obstruction: float = 0.0) -> PixelMask:
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"""
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Map the integrated PSF values to a sensor grid.
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Parameters
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----------
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obstruction
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mask : PixelMask
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The pixel mask to map the values to. The values will only be mapped onto entries with the value 1.
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jitter_sigma : Quantity
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Sigma of the telescope's jitter in arcsec
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obstruction : float
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The central obstruction as ratio A_ob / A_ap
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Returns
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-------
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mask : PixelMask
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The pixel mask with the integrated PSF values mapped onto each pixel.
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"""
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# Calculate the indices of all non-zero elements of the mask
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y_ind, x_ind = np.nonzero(mask)
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# Extract a rectangle containing all non-zero values of the mask
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mask_red = mask[y_ind.min():(y_ind.max() + 1), x_ind.min():(x_ind.max() + 1)]
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# Calculate the new PSF-center indices of the reduced mask
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psf_center_ind = [mask.psf_center_ind[0] - y_ind.min(), mask.psf_center_ind[1] - x_ind.min()]
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# Oversample the reduced mask
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mask_red_os = self._rebin(mask_red, self.__osf).view(PixelMask)
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# Calculate the new PSF-center indices of the reduced mask
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psf_center_ind = [(x + 0.5) * self.__osf - 0.5 for x in psf_center_ind]
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reduced_observation_angle_pixel = (mask.pixel_size / (
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self.__f_number * self.__d_aperture) * self.__d_aperture / self.__wl).decompose()
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x_mesh, y_mesh = np.meshgrid(
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(np.arange(mask_red_os.shape[1]) - psf_center_ind[
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1]) * reduced_observation_angle_pixel.value / self.__osf,
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(np.arange(mask_red_os.shape[0]) - psf_center_ind[
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0]) * reduced_observation_angle_pixel.value / self.__osf)
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dist = np.sqrt(x_mesh ** 2 + y_mesh ** 2)
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if jitter_sigma is None:
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res = self.__airy(dist * np.pi, np.sqrt(obstruction))
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else:
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if self.__psf_jitter is None:
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self.calcReducedObservationAngle("fwhm", jitter_sigma, obstruction)
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psf_jitter = self.__psf_jitter
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x = np.arange(psf_jitter.shape[0]) * reduced_observation_angle_pixel.value / self.__osf
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psf_interp = interp1d(x=x, y=psf_jitter, kind='cubic', copy=False, bounds_error=False,
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fill_value="extrapolate")
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res = psf_interp(dist)
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res = self._rebin(res, 1 / self.__osf)
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res = (mask_red * res).view(np.ndarray)
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# Integrate the reduced mask and divide by the indefinite integral to get relative intensities
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res = res * (reduced_observation_angle_pixel.value / self.__osf) ** 2 / (4 / np.pi) * (1 - obstruction)
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# reintegrate the reduced mask into the complete mask
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mask[y_ind.min():(y_ind.max() + 1), x_ind.min():(x_ind.max() + 1)] = res
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return mask
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