modify doc in gp_interp

lsst · Aug 30, 2024 · 2e6f54b · 2e6f54b
1 parent 31ea5f1
commit 2e6f54b
Showing 1 changed file with 11 additions and 17 deletions.
diff --git a/python/lsst/meas/algorithms/gp_interpolation.py b/python/lsst/meas/algorithms/gp_interpolation.py
@@ -20,17 +20,13 @@ def updateMaskFromArray(mask, bad_pixel, interpBit):
 
     Parameters
     ----------
-    mask : MaskedImage #TO DO, this is not the right format need to check.
+    mask : `lsst.afw.image.MaskedImage`
         The mask image to update.
-    bad_pixel : array-like
+    bad_pixel : `np.array`
         An array-like object containing the coordinates of the bad pixels.
         Each row should contain the x and y coordinates of a bad pixel.
-    interpBit : int
+    interpBit : `int`
         The bit value to set for the bad pixels in the mask.
-
-    Returns
-    -------
-    None
     """
     x0 = mask.getX0()
     y0 = mask.getY0()
@@ -45,25 +41,23 @@ def median_with_mad_clipping(data, mad_multiplier=2.0):
     """
     Calculate the median of the input data after applying Median Absolute Deviation (MAD) clipping.
 
+    The MAD clipping method is used to remove outliers from the data. The median of the data is calculated,
+    and then the MAD is calculated as the median absolute deviation from the median. The data is then clipped
+    by removing values that are outside the range of median +/- mad_multiplier * MAD. Finally, the median of
+    the clipped data is returned.
+
     Parameters:
     -----------
-    data : array-like
+    data : `np.array`
         Input data array.
-    mad_multiplier : float, optional
+    mad_multiplier : `float`, optional
         Multiplier for the MAD value used for clipping. Default is 2.0.
 
     Returns:
     --------
-    median_clipped : float
+    median_clipped : `float`
         Median value of the clipped data.
 
-    Notes:
-    ------
-    The MAD clipping method is used to remove outliers from the data. The median of the data is calculated,
-    and then the MAD is calculated as the median absolute deviation from the median. The data is then clipped
-    by removing values that are outside the range of median +/- mad_multiplier * MAD. Finally, the median of
-    the clipped data is returned.
-
     Examples:
     ---------
     >>> data = [1, 2, 3, 4, 5, 100]