diff --git a/datasets/raise/fft/remove_period_patterns.py b/datasets/raise/fft/remove_period_patterns.py
index ac8d506..d27863e 100644
--- a/datasets/raise/fft/remove_period_patterns.py
+++ b/datasets/raise/fft/remove_period_patterns.py
@@ -30,6 +30,7 @@ def removePeriodicPatterns(fft1Part):
     # This example is intended to demonstrate how astropy.convolve and
     # scipy.convolve handle missing data, so we start by setting the brightest
     # pixels to NaN to simulate a "saturated" data set
+    # See [Benjamin_Loison/astropy/issues/2](https://codeberg.org/Benjamin_Loison/astropy/issues/2).
     height, width = fft1Part.shape
     for x in range(width):
         fft1Part[height // 2, x] = np.nan
@@ -45,6 +46,7 @@ def removePeriodicPatterns(fft1Part):
     kernel = Gaussian2DKernel(x_stddev = X_STDDEV)
 
     # create a "fixed" image with NaNs replaced by interpolated values
+    # See [Benjamin_Loison/astropy/issues/1](https://codeberg.org/Benjamin_Loison/astropy/issues/1).
     fixedImagePart = interpolate_replace_nans(fft1Part, kernel)
     return fixedImagePart
 
@@ -90,4 +92,4 @@ differenceBetweenBothImages = invOriginalFft1 - invFixedImage
 # `np.log10(1 + abs(differenceBetweenBothImages))` does not seem more interesting.
 axes[2].imshow(differenceBetweenBothImages)
 plt.tight_layout()
-plt.show()
\ No newline at end of file
+plt.show()