esbo_ds/ESBO-ETC
esbo_ds/ESBO-ETC
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Minor improvements

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This commit is contained in:
Lukas Klass 2020-10-01 16:35:24 +02:00
parent 59aa006c5e
commit 54661ee478
1 changed files with 11 additions and 8 deletions
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19
esbo_etc/classes/psf/Airy.py
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@ -95,26 +95,27 @@ class Airy(IPSF):
# Convert jitter to reduced observation angle in lambda / d_ap # Convert jitter to reduced observation angle in lambda / d_ap
jitter_sigma = jitter_sigma.to(u.rad).value * self.__d_aperture / self.__wl.to(u.m) jitter_sigma = jitter_sigma.to(u.rad).value * self.__d_aperture / self.__wl.to(u.m)
# Calculate necessary grid length to accommodate the psf and 3-sigma of the gaussian # Calculate necessary grid length to accommodate the psf and 3-sigma of the gaussian
grid_width = (reduced_observation_angle / 2 + 3 * jitter_sigma) grid_width = (reduced_observation_angle / 2 + 3 * jitter_sigma.value)
# Calculate the reduced observation angle of a single detector pixel # Calculate the reduced observation angle of a single detector pixel
reduced_observation_angle_pixel = (self.__pixel_size / ( reduced_observation_angle_pixel = (self.__pixel_size / (
self.__f_number * self.__d_aperture) * self.__d_aperture / self.__wl).decompose() self.__f_number * self.__d_aperture) * self.__d_aperture / self.__wl).decompose()
# Calculate the width of each grid element # Calculate the width of each grid element
dx = reduced_observation_angle_pixel.value / self.__osf dx = reduced_observation_angle_pixel.value / self.__osf
# Calculate the necessary number of points on the grid # Calculate the necessary number of points on the grid
n_points = np.ceil(grid_width / dx).value n_points = np.ceil(grid_width / dx)
# Calculate the corresponding x-coordinates of each grid element # Calculate the corresponding x-coordinates of each grid element
x = np.arange(1, n_points + 1) * dx x = np.arange(1, n_points + 1) * dx
# Calculate the psf from an airy disk for each element on the grid # Calculate the psf from an airy disk for each element on the grid
psf = self.__airy(np.pi * x, np.sqrt(obstruction)) psf = self.__airy(np.pi * x, np.sqrt(obstruction))
# Calculate the integral of the undisturbed airy disk in order to scale the result of the convolution # Calculate the integral of the undisturbed airy disk in order to scale the result of the convolution
# This corresponds to an integration in polar coordinates: I(r)*r dr dphi
total = np.sum(psf * x) * dx * 2 * np.pi total = np.sum(psf * x) * dx * 2 * np.pi
# Mirror the PSF to the negative x-domain # Mirror the PSF to the negative x-domain
psf = np.concatenate((np.flip(psf), np.array([1]), psf)) psf = np.concatenate((np.flip(psf), np.array([1]), psf))
# Calculate a gaussian kernel # Calculate a gaussian kernel
kernel = 1 / (2 * np.pi * jitter_sigma ** 2) * np.exp(- x ** 2 / (2 * jitter_sigma ** 2)) kernel = 1 / (2 * np.pi * jitter_sigma.value ** 2) * np.exp(- x ** 2 / (2 * jitter_sigma.value ** 2))
# Mirror the kernel to the negative x-domain # Mirror the kernel to the negative x-domain
kernel = np.concatenate((np.flip(kernel), np.array([1 / (2 * np.pi * jitter_sigma ** 2)]), kernel)) kernel = np.concatenate((np.flip(kernel), np.array([1 / (2 * np.pi * jitter_sigma.value ** 2)]), kernel))
# Normalize the kernel # Normalize the kernel
kernel = kernel / np.sum(kernel) kernel = kernel / np.sum(kernel)
# Convolve the PSF with gaussian kernel # Convolve the PSF with gaussian kernel
@ -122,6 +123,7 @@ class Airy(IPSF):
# Reduce the PSF to the positive x-domain # Reduce the PSF to the positive x-domain
psf = psf[int((psf.shape[0] - 1) / 2):] psf = psf[int((psf.shape[0] - 1) / 2):]
# Scale the integral of the disturbed PSF equal to the undisturbed PSF # Scale the integral of the disturbed PSF equal to the undisturbed PSF
# This corresponds to an integration in polar coordinates: I(r)*r dr dphi
psf = psf / (np.sum(psf * np.arange(psf.shape[0]) * dx) * dx * 2 * np.pi) * total psf = psf / (np.sum(psf * np.arange(psf.shape[0]) * dx) * dx * 2 * np.pi) * total
self.__psf_jitter = psf self.__psf_jitter = psf
@ -130,12 +132,13 @@ class Airy(IPSF):
self.__osf * 2 self.__osf * 2
else: else:
# Calculate the rolling integral of the PSF # Calculate the rolling integral of the PSF
# This corresponds to an integration in polar coordinates: I(r)*r dr dphi
psf_int = np.cumsum(psf * np.arange(psf.shape[0]) * dx) * dx * 2 * np.pi psf_int = np.cumsum(psf * np.arange(psf.shape[0]) * dx) * dx * 2 * np.pi
# Scale the integral of the disturbed PSF equal to the undisturbed PSF # Calculate the percentage of encircled energy by dividing by the infinite integral of the undisturbed
psf_int = psf_int / (4 / np.pi) * (1 - obstruction) # airy disk
psf_int = psf_int / (4 / np.pi * 1 / (1 - obstruction))
# Calculate the reduced observation angle # Calculate the reduced observation angle
reduced_observation_angle = np.argmax( reduced_observation_angle = np.argmax(psf_int > contained_energy) * dx * 2
psf_int > contained_energy) * reduced_observation_angle_pixel.value / self.__osf * 2
return reduced_observation_angle * u.dimensionless_unscaled return reduced_observation_angle * u.dimensionless_unscaled