167 lines
8.5 KiB
Python
167 lines
8.5 KiB
Python
from .IPSF import IPSF
|
|
from ...lib.helpers import error, rasterizeCircle
|
|
import numpy as np
|
|
import astropy.units as u
|
|
import re
|
|
from logging import warning
|
|
from typing import Union
|
|
from scipy.optimize import bisect
|
|
from scipy.ndimage.interpolation import zoom
|
|
from scipy.signal import fftconvolve
|
|
|
|
|
|
class Zemax(IPSF):
|
|
"""
|
|
A class for modelling the PSF from a Zemax output file
|
|
"""
|
|
|
|
@u.quantity_input(wl="length", d_aperture="length", pixel_size="length")
|
|
def __init__(self, file: str, f_number: float, wl: u.Quantity, d_aperture: u.Quantity, osf: float,
|
|
pixel_size: u.Quantity):
|
|
"""
|
|
Initialize a new PSF from a Zemax file.
|
|
|
|
Parameters
|
|
----------
|
|
file : str
|
|
Path to the Zemax-file. The origin of the coordinate system is in the lower left corner of the matrix
|
|
f_number : float
|
|
The working focal number of the optical system
|
|
wl : Quantity
|
|
The central wavelength which is used for calculating the PSF
|
|
d_aperture : Quantity
|
|
The diameter of the telescope's aperture.
|
|
osf : float
|
|
The oversampling factor to be used for oversampling the PSF with regards to the pixel size.
|
|
pixel_size : Quantity
|
|
The size of a pixel as length-quantity.
|
|
"""
|
|
# Store parameters
|
|
self.__f_number = f_number
|
|
self.__wl = wl
|
|
self.__d_aperture = d_aperture
|
|
self.__osf = osf
|
|
self.__pixel_size = pixel_size
|
|
# Read PSF from file
|
|
with open(file, encoding="utf16") as fp:
|
|
self.__psf = np.genfromtxt((x.replace(",", ".") for x in fp), delimiter='\t', skip_header=21)
|
|
# Read header parameters from the file
|
|
with open(file, encoding="utf16") as fp:
|
|
head = [next(fp) for _ in range(21)]
|
|
# Parse shape of the grid and check the read PSF-array
|
|
shape = [int(x) for x in re.findall("[0-9]+", list(filter(re.compile("Image grid size: ").match, head))[0])]
|
|
if shape != list(self.__psf.shape):
|
|
warning("Not all PSF entries read.")
|
|
# Parse and calculate the grid width
|
|
grid_delta = [float(x.replace(",", ".")) for x in
|
|
re.findall("[0-9]+,*[0-9]*", list(filter(re.compile("Data area is ").match, head))[0])]
|
|
unit = re.findall(".+(?=\\.$)", re.sub("Data area is [0-9]+,*[0-9]* by [0-9]+,*[0-9]* ", "",
|
|
list(filter(re.compile("Data area is ").match, head))[0]))[0]
|
|
# noinspection PyArgumentList
|
|
self.__grid_delta = np.array(grid_delta) / np.array(shape) << u.Unit(unit)
|
|
# Parse the center point of the PSF in the grid
|
|
self.__center_point = [int(x) for x in
|
|
re.findall("[0-9]+", list(filter(re.compile("Center point is: ").match, head))[0])]
|
|
self.__center_point[0] = self.__psf.shape[0] - self.__center_point[0]
|
|
self.__center_point[1] -= 1
|
|
|
|
# @u.quantity_input(jitter_sigma=u.arcsec)
|
|
def calcReducedObservationAngle(self, contained_energy: Union[str, int, float, u.Quantity],
|
|
jitter_sigma: u.Quantity = None, obstruction: float = 0.0) -> u.Quantity:
|
|
"""
|
|
Calculate the reduced observation angle in lambda / d_ap for the given contained energy.
|
|
|
|
Parameters
|
|
----------
|
|
contained_energy : Union[str, int, float, u.Quantity]
|
|
The percentage of energy to be contained within a circle with the diameter reduced observation angle.
|
|
jitter_sigma : Quantity
|
|
Sigma of the telescope's jitter in arcsec
|
|
obstruction : float
|
|
The central obstruction as ratio A_ob / A_ap
|
|
|
|
Returns
|
|
-------
|
|
reduced_observation_angle: Quantity
|
|
The reduced observation angle in lambda / d_ap
|
|
"""
|
|
# Parse the contained energy
|
|
if type(contained_energy) == str:
|
|
try:
|
|
contained_energy = float(contained_energy) / 100.0 * u.dimensionless_unscaled
|
|
except ValueError:
|
|
error("Could not convert encircled energy to float.")
|
|
elif type(contained_energy) in [int, float]:
|
|
contained_energy = contained_energy / 100 * u.dimensionless_unscaled
|
|
|
|
# Calculate the osf for the PSF based on the current resolution of the PSF
|
|
psf_osf = np.ceil(max(self.__grid_delta) / (2 * self.__pixel_size / self.__osf)).value * 2
|
|
if psf_osf == 1.0:
|
|
# No oversampling is necessary
|
|
psf = self.__psf
|
|
center_point = self.__center_point
|
|
else:
|
|
# Oversampling is necessary, oversample the PSF and calculate the new center point.
|
|
psf = zoom(self.__psf, zoom=psf_osf, order=1)
|
|
center_point = [(x + 0.5) * psf_osf - 0.5 for x in self.__center_point]
|
|
if jitter_sigma is not None:
|
|
# Convert angular jitter to jitter on focal plane
|
|
jitter_sigma_um = (jitter_sigma.to(u.rad) * self.__f_number * self.__d_aperture / u.rad).to(u.um)
|
|
# Jitter is enabled. Calculate the corresponding gaussian bell and convolve it with the PSF
|
|
if min(self.__grid_delta) / psf_osf < 6 * jitter_sigma_um:
|
|
# 3-sigma interval of the gaussian bell is larger than the grid width
|
|
# Calculate the necessary grid length for the 3-sigma interval of the gaussian bell
|
|
jitter_grid_length = np.ceil(6 * jitter_sigma_um / (min(self.__grid_delta) / psf_osf)).value
|
|
# Make sure, the grid size is odd in order to have a defined kernel center
|
|
jitter_grid_length = int(jitter_grid_length if jitter_grid_length % 2 == 1 else jitter_grid_length + 1)
|
|
|
|
# Create a meshgrid containing the x and y coordinates of each point within the first quadrant of the
|
|
# gaussian kernel
|
|
xv, yv = np.meshgrid(range(-int((jitter_grid_length - 1) / 2), 1),
|
|
range(-int((jitter_grid_length - 1) / 2), 1))
|
|
# Calculate the gaussian kernel in the first quadrant
|
|
kernel = 1 / (2 * np.pi * jitter_sigma_um.value ** 2) * np.exp(
|
|
-((xv * min(self.__grid_delta.value) / psf_osf) ** 2 +
|
|
(yv * min(self.__grid_delta.value) / psf_osf) ** 2) / (2 * jitter_sigma_um.value ** 2))
|
|
# Mirror the kernel from the first quadrant to all other quadrants
|
|
kernel = np.concatenate((kernel, np.flip(kernel, axis=1)[:, 1:]), axis=1)
|
|
kernel = np.concatenate((kernel, np.flip(kernel, axis=0)[1:, :]), axis=0)
|
|
# Normalize kernel
|
|
kernel = kernel / np.sum(kernel)
|
|
# Convolve PSF with gaussian kernel
|
|
psf = fftconvolve(np.pad(psf, int((jitter_grid_length - 1) / 2), mode="constant", constant_values=0),
|
|
kernel, mode="same")
|
|
# Calculate new center point
|
|
center_point = [x + int((jitter_grid_length - 1) / 2) for x in center_point]
|
|
# Calculate the maximum possible radius for the circle containing the photometric aperture
|
|
r_max = max(np.sqrt(center_point[0]**2 + center_point[1]**2),
|
|
np.sqrt((psf.shape[0] - center_point[0])**2 + center_point[1]**2),
|
|
np.sqrt(center_point[0]**2 + (psf.shape[1] - center_point[1])**2),
|
|
np.sqrt((psf.shape[0] - center_point[0])**2 + (psf.shape[1] - center_point[1])**2))
|
|
# Calculate the total contained energy of the PSF
|
|
total = np.sum(psf)
|
|
# Iterate the optimal radius for the contained energy
|
|
r = bisect(lambda r_c: contained_energy.value - np.sum(
|
|
psf * rasterizeCircle(psf.shape[0], r_c, center_point[0], center_point[1])) / total, 0, r_max, xtol=1e-1)
|
|
# Calculate the reduced observation angle in lambda / d_ap
|
|
# noinspection PyTypeChecker
|
|
reduced_observation_angle = r / psf_osf * self.__grid_delta[0] / (
|
|
self.__f_number * self.__d_aperture) * self.__d_aperture / self.__wl
|
|
return reduced_observation_angle * u.dimensionless_unscaled
|
|
|
|
def mapToGrid(self, grid: np.ndarray) -> np.ndarray:
|
|
"""
|
|
Map the integrated PSF values to a sensor grid.
|
|
|
|
Parameters
|
|
----------
|
|
grid : ndarray
|
|
The grid to map the values to. The values will only be mapped onto entries with the value 1.
|
|
|
|
Returns
|
|
-------
|
|
grid : ndarray
|
|
The grid with the mapped values.
|
|
"""
|
|
pass
|