switch name

python/lsst/meas/algorithms/gp_interpolation.py

@@ -343,10 +343,10 @@ def run(self):
                 xmin, xmax = max([global_xmin, bbox.minX]), min(global_xmax, bbox.maxX)
                 ymin, ymax = max([global_ymin, bbox.minY]), min(global_ymax, bbox.maxY)
                 localBox = Box2I(Point2I(xmin, ymin), Extent2I(xmax - xmin, ymax - ymin))
-                sub_masked_image = self.masked_image[localBox]
+                masked_sub_image = self.masked_image[localBox]
 
-                sub_masked_image = self.interpolate_sub_masked_image(sub_masked_image)
-                self.masked_image[localBox] = sub_masked_image
+                masked_sub_image = self.interpolate_masked_sub_image(masked_sub_image)
+                self.masked_image[localBox] = masked_sub_image
 
     def _good_pixel_binning(self, pixels):
         """
@@ -379,13 +379,13 @@ def _good_pixel_binning(self, pixels):
             [binning.coords0[:, 0], binning.coords0[:, 1], binning.params0]
         ).T
 
-    def interpolate_sub_masked_image(self, sub_masked_image):
+    def interpolate_masked_sub_image(self, masked_sub_image):
         """
         Interpolate the masked sub-image.
 
         Parameters:
         -----------
-        sub_masked_image : `lsst.afw.image.MaskedImage`
+        masked_sub_image : `lsst.afw.image.MaskedImage`
             The sub-masked image to be interpolated.
 
         Returns:
@@ -398,16 +398,16 @@ def interpolate_sub_masked_image(self, sub_masked_image):
             self.correlation_length * self.correlation_length_cut
         )  # need integer as input.
         bad_pixel, good_pixel = ctUtils.findGoodPixelsAroundBadPixels(
-            sub_masked_image, self.defects, buffer=cut
+            masked_sub_image, self.defects, buffer=cut
         )
         # Do nothing if bad pixel is None.
         if bad_pixel.size == 0 or good_pixel.size == 0:
             self.log.info("No bad or good pixels found. No interpolation performed.")
-            return sub_masked_image
+            return masked_sub_image
         # Do GP interpolation if bad pixel found.
         else:
             # gp interpolation
-            sub_image_array = sub_masked_image.getVariance().array
+            sub_image_array = masked_sub_image.getVariance().array
             white_noise = np.sqrt(
                 np.mean(sub_image_array[np.isfinite(sub_image_array)])
             )
@@ -419,7 +419,7 @@ def interpolate_sub_masked_image(self, sub_masked_image):
                     self.log.info(
                         "No bad or good pixels found. No interpolation performed."
                     )
-                    return sub_masked_image
+                    return masked_sub_image
                 # kernel amplitude might be better described by maximum value of good pixel given
                 # the data and not really a random gaussian field.
                 kernel_amplitude = np.max(good_pixel[:, 2:])
@@ -453,6 +453,6 @@ def interpolate_sub_masked_image(self, sub_masked_image):
                     )
 
             # Update values
-            ctUtils.updateImageFromArray(sub_masked_image.image, bad_pixel)
-            updateMaskFromArray(sub_masked_image.mask, bad_pixel, self.interpBit)
-            return sub_masked_image
+            ctUtils.updateImageFromArray(masked_sub_image.image, bad_pixel)
+            updateMaskFromArray(masked_sub_image.mask, bad_pixel, self.interpBit)
+            return masked_sub_image