Use percentile instead of median for column bias subtraction

As suggested by Robert, use a low percentile (default 10th) instead
of the median for column bias estimation. Stars and trails are very
unlikely to live in the 10th percentile, which avoids the dark dip
artifacts created by the median being pulled by bright sources.
The percentile is slightly noisier but eliminates the dark bands.

Added biasPercentile config option to GuiderReader.get() and
processStamps() to allow tuning the percentile value.

Author: estevesjh

python/lsst/summit/utils/guiders/reading.py

@@ -701,6 +701,7 @@ def get(
         seqNum: int,
         doSubtractMedian: bool = True,
         columnMaskK: float = 50.0,
+        biasPercentile: float = 10.0,
         scienceDetNum: int = 94,
     ) -> GuiderData:
         """
@@ -713,9 +714,12 @@ def get(
         seqNum : `int`
             Sequence number.
         doSubtractMedian : `bool`, optional
-            If True, subtract median row bias from each stamp.
+            If True, subtract column bias from each stamp.
         columnMaskK : `float`, optional
             Threshold factor for column mask detection.
+        biasPercentile : `float`, optional
+            Percentile (0-100) for column bias estimation.
+            Lower values avoid star/trail contamination.
         scienceDetNum : `int`, optional
             Science detector number for WCS reference.
 
@@ -748,6 +752,7 @@ def get(
             rawStampsDict,
             doSubtractMedian,
             columnMaskK,
+            biasPercentile,
         )
         guiderData = GuiderData(
             seqNum=seqNum,
@@ -797,19 +802,22 @@ def processStamps(
         rawStampsDict: dict[str, Stamps],
         doSubtractMedian: bool,
         columnMaskK: float,
+        biasPercentile: float = 10.0,
     ) -> dict[str, Stamps]:
         """
-        Apply median row bias subtraction and per-stamp column masking.
+        Apply column bias subtraction and per-stamp column masking.
 
 
         Parameters
         ----------
         rawStampsDict : `dict[str, Stamps]`
             ROI view of stamps from butler `guider_raw`.
         doSubtractMedian : `bool`, optional
-            If True, subtract median row bias.
+            If True, subtract column bias.
         columnMaskK : `float`, optional
             Threshold factor for column mask detection.
+        biasPercentile : `float`, optional
+            Percentile for column bias estimation.
 
         Returns
         -------
@@ -829,23 +837,28 @@ def processStamps(
                 # Work on a copy - never modify original
                 data = stamps[i].stamp_im.image.array.copy()
 
-                # Compute median row bias
-                medianRows = np.nanmedian(data, axis=0)
+                # Compute column bias using low percentile to avoid
+                # star contamination (median pulls dark dips at star cols).
+                percRows = np.nanpercentile(data, biasPercentile, axis=0)
+                rPerc = np.nanpercentile(data, biasPercentile)
                 medianValue = np.nanmedian(data)
-                # Replace NaN medians (fully masked columns) with global median
-                medianRows = np.where(np.isnan(medianRows), medianValue, medianRows)
+                # Replace NaN values (fully masked columns) with global median
+                percRows = np.where(np.isnan(percRows), medianValue, percRows)
 
                 # Compute column mask on bias-subtracted data
-                colMask = getColumnMask(data - medianRows[np.newaxis, :], k=columnMaskK)
+                colMask = getColumnMask(
+                    data - percRows[np.newaxis, :] - (rPerc - medianValue),
+                    k=columnMaskK,
+                )
 
                 # Create mask array for MaskedImageF (BAD=1 for masked cols)
                 nRows, nCols = data.shape
                 maskArray = np.zeros((nRows, nCols), dtype=np.uint32)
                 maskArray[colMask] = 1  # Set BAD bit for masked columns
 
-                # Apply median row bias subtraction
+                # Apply column bias subtraction
                 if doSubtractMedian:
-                    data = data - medianRows[np.newaxis, :]
+                    data = data - percRows[np.newaxis, :] - (rPerc - medianValue)
 
                 # Fill masked columns with global median
                 if colMask.any():