Return full convolution and rely on internal padding
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@ -119,7 +119,7 @@ class Airy(IPSF):
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# Normalize the kernel
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# Normalize the kernel
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kernel = kernel / np.sum(kernel)
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kernel = kernel / np.sum(kernel)
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# Convolve the PSF with gaussian 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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psf = fftconvolve(psf, kernel, mode="full")
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# Reduce the PSF to the positive x-domain
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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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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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# Scale the integral of the disturbed PSF equal to the undisturbed PSF
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@ -176,8 +176,7 @@ class Zemax(IPSF):
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# Normalize kernel
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# Normalize kernel
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kernel = kernel / np.sum(kernel)
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kernel = kernel / np.sum(kernel)
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# Convolve PSF with gaussian kernel
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# Convolve PSF with gaussian kernel
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psf = fftconvolve(np.pad(psf, int((jitter_grid_length - 1) / 2), mode="constant", constant_values=0),
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psf = fftconvolve(psf, kernel, mode="full")
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kernel, mode="same")
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# Calculate new center point
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# Calculate new center point
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center_point = [x + int((jitter_grid_length - 1) / 2) for x in center_point]
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center_point = [x + int((jitter_grid_length - 1) / 2) for x in center_point]
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# Save the values as object attribute
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# Save the values as object attribute
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