esbo_ds/ESBO-ETC
esbo_ds/ESBO-ETC
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This commit is contained in:
Lukas Klass 2020-05-20 09:13:26 +02:00
parent 1c8e0ff8b0
commit 85403e984b
1 changed files with 6 additions and 8 deletions
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esbo_etc/classes/psf/Zemax.py
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@ -99,7 +99,7 @@ class Zemax(IPSF):
elif type(contained_energy) in [int, float]: elif type(contained_energy) in [int, float]:
contained_energy = contained_energy / 100 * u.dimensionless_unscaled contained_energy = contained_energy / 100 * u.dimensionless_unscaled
center_point, psf, psf_osf = self.calcPSF(jitter_sigma) center_point, psf, psf_osf = self.__calcPSF(jitter_sigma)
# Calculate the maximum possible radius for the circle containing the photometric aperture # 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), r_max = max(np.sqrt(center_point[0] ** 2 + center_point[1] ** 2),
@ -118,7 +118,7 @@ class Zemax(IPSF):
self.__f_number * self.__d_aperture) * self.__d_aperture / self.__wl self.__f_number * self.__d_aperture) * self.__d_aperture / self.__wl
return reduced_observation_angle * u.dimensionless_unscaled return reduced_observation_angle * u.dimensionless_unscaled
def calcPSF(self, jitter_sigma: u.Quantity = None, obstruction: float = None): def __calcPSF(self, jitter_sigma: u.Quantity = None):
""" """
Calculate the PSF from the Zemax-file. This includes oversampling the PSF and convolving with the Calculate the PSF from the Zemax-file. This includes oversampling the PSF and convolving with the
jitter-gaussian. jitter-gaussian.
@ -126,9 +126,7 @@ class Zemax(IPSF):
Parameters Parameters
---------- ----------
jitter_sigma : Quantity jitter_sigma : Quantity
Sigma of the telescope's jitter in arcsec Sigma of the telescope's jitter in arcsec.
obstruction : float
The central obstruction as ratio A_ob / A_ap
Returns Returns
------- -------
@ -212,7 +210,7 @@ class Zemax(IPSF):
# Calculate the new PSF-center indices of the reduced mask # Calculate the new PSF-center indices of the reduced mask
psf_center_ind = [mask.psf_center_ind[0] - y_ind.min(), mask.psf_center_ind[1] - x_ind.min()] psf_center_ind = [mask.psf_center_ind[0] - y_ind.min(), mask.psf_center_ind[1] - x_ind.min()]
# Oversample the reduced mask # Oversample the reduced mask
mask_red_os = self.rebin(mask_red, self.__osf).view(PixelMask) mask_red_os = self._rebin(mask_red, self.__osf).view(PixelMask)
# Calculate the new PSF-center indices of the reduced mask # Calculate the new PSF-center indices of the reduced mask
psf_center_ind = [x * self.__osf for x in psf_center_ind] psf_center_ind = [x * self.__osf for x in psf_center_ind]
@ -222,7 +220,7 @@ class Zemax(IPSF):
psf = self.__psf_os psf = self.__psf_os
psf_osf = self.__psf_osf psf_osf = self.__psf_osf
else: else:
center_point, psf, psf_osf = self.calcPSF(jitter_sigma) center_point, psf, psf_osf = self.__calcPSF(jitter_sigma)
# Calculate the coordinates of each PSF value in microns # Calculate the coordinates of each PSF value in microns
x = (np.arange(psf.shape[1]) - center_point[1]) * self.__grid_delta[1].to(u.um).value / psf_osf x = (np.arange(psf.shape[1]) - center_point[1]) * self.__grid_delta[1].to(u.um).value / psf_osf
y = (np.arange(psf.shape[0]) - center_point[0]) * self.__grid_delta[0].to(u.um).value / psf_osf y = (np.arange(psf.shape[0]) - center_point[0]) * self.__grid_delta[0].to(u.um).value / psf_osf
@ -233,7 +231,7 @@ class Zemax(IPSF):
(np.arange(mask_red_os.shape[0]) - psf_center_ind[0]) * mask_red_os.pixel_size.to(u.um).value) (np.arange(mask_red_os.shape[0]) - psf_center_ind[0]) * mask_red_os.pixel_size.to(u.um).value)
# Bin the oversampled reduced mask to the original resolution and multiply with the reduced mask to select only # Bin the oversampled reduced mask to the original resolution and multiply with the reduced mask to select only
# the relevant values # the relevant values
res = mask_red * self.rebin(res, 1 / self.__osf) res = mask_red * self._rebin(res, 1 / self.__osf)
# Integrate the reduced mask and divide by the indefinite integral to get relative intensities # Integrate the reduced mask and divide by the indefinite integral to get relative intensities
res = res * mask_red_os.pixel_size.to(u.um).value ** 2 / ( res = res * mask_red_os.pixel_size.to(u.um).value ** 2 / (
psf.sum() * (self.__grid_delta[0].to(u.um).value / psf_osf) ** 2) psf.sum() * (self.__grid_delta[0].to(u.um).value / psf_osf) ** 2)